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International Journal of Education in Mathematics, Science and Technology
Volume 1, Number 2, April 2013
ISSN: 2147-611X
1
2
2013
2147-611X
ISSN: 2147-611X
EDITORIAL BOARD
Editors
Mack SHELLEY - Iowa State University, U.S.A.
Ismail SAHIN - Necmettin Erbakan University, Turkey
Section Editors
Arthur POWELL - Rutgers University, U.S.A.
Utkun AYDIN - MEF University, Turkey
Chun-Yen CHANG - National Taiwan Normal University, Taiwan
I. Ozgur ZEMBAT - Mevlana University, Turkey
Jacqueline T. MCDONNOUGH - Virginia Commonwealth University, U.S.A.
Meric OZGELDI - Mersin University, Turkey
Lina TANKELEVICIENE - Siauliai University, Lithuania
Niyazi ERDOGAN - Balikesir University, Turkey
Sandra ABEGGLEN - London Metropolitan University, U.K.
Editorial Board
Ann D. THOMPSON - Iowa State University, U.S.A
Bill COBERN - Western Michigan University, U.S.A.
Douglas B. CLARK - Vanderbilt University, U.S.A.
Gokhan OZDEMIR - Nigde University, Turkey
Hakan AKCAY - Yildiz Technical University, Turkey
Huseh-Hua CHUANG - National Sun Yat-sen University, Taiwan
Igor M. VERNER - Technion - Israel Institute of Technology, Israel
Ilhan VARANK - Yildiz Technical University, Turkey
James M. LAFFEY - University of Missouri, U.S.A.
Kamisah OSMAN - National University of Malaysia, Malaysia
Lynne SCHRUM - George Mason University, U.S.A.
Mary B. NAKHLEH - Purdue University, U.S.A.
Musa DIKMENLI - Necmettin Erbakan University, Turkey
Muteb ALQAHTANI - Rutgers University, U.S.A.
Ok-Kyeong KIM - Western Michigan University, U.S.A.
Pasha ANTONENKO - Oklahoma State University, U.S.A.
Paul ERNEST - University of Exeter, UK
Pornrat WATTANAKASIWICH - Chiang Mai University, Thailand
Robert E. YAGER - University of Iowa, U.S.A.
Sanjay SHARMA - Roorkee E&M Technology Institute, India
Sinan ERTEN - Hacettepe University, Turkey
Tsung-Hau JEN - National Taiwan Normal University, Taiwan
William F. MCCOMAS - University of Arkansas, U.S.A.
Yilmaz SAGLAM - Gaziantep University, Turkey
Technical Support
Selahattin ALAN - Selçuk University, Turkey
Ismail CELIK – Necmettin Erbakan University, Turkey
International Journal of Education in Mathematics, Science and Technology (IJEMST)
The International Journal of Education in Mathematics, Science and Technology (IJEMST) is a peer-reviewed scholarly online journal. The IJEMST is
published quarterly in January, April, July and October. The IJEMST welcomes any papers on math education, science education and educational technology
using techniques from and applications in any technical knowledge domain: original theoretical works, literature reviews, research reports, social issues,
psychological issues, curricula, learning environments, research in an educational context, book reviews, and review articles. The articles should be original,
unpublished, and not in consideration for publication elsewhere at the time of submission to the IJEMST. Access to the Journal articles is free to individuals,
libraries and institutions through IJEMST’s website.
Abstracting/ Indexing
The IJEMST are indexed by the following abstracting and indexing services: Ulrich Index, ASOS Index, Journal Seek, JournalRate, Directory of Research
Journals Indexing (DRJI), Infobase Index, ResearchBib, Index Copernicus, TUBITAK ULAKBIM Dergipark, ERIH, Scientific Indexing Service (SIS), and
Education Resources Information Center (ERIC).
Submissions
All submissions should be in electronic (.Doc or .Docx) format. Submissions in PDF and other non-editable formats are not acceptable. Manuscripts can be
submitted through the journal website. All manuscripts should use the latest APA style. The manuscript template for formatting is available on the journal
website.
Contact Info
International Journal of Education in Mathematics, Science and Technology (IJEMST)
Email: ijemst@gmail.com
Web: http://www.ijemst.com
ISSN: 2147-611X
TABLE OF CONTENTS
A Case Study of E-tutors’ Teaching Practice: Does Technology Drive Pedagogy?
75
Hsueh-Hua Chuang
Transfer of Learning in Mathematics, Science, and Reading among Students in Turkey: A Study
83
Using 2009 PISA Data
Mack Shelley, Atila Yildirim
Representations of Fundamental Chemistry Concepts in Relation to the Particulate Nature of
96
Matter
Zübeyde Demet Kırbulut, Michael Edward Beeth
Analysis of Scientific Epistemological Beliefs of Eighth Graders
107
Nilgün Yenice, Barış Özden
Integrated Programs for Science and Mathematics-Review
116
Kürşat Kurt, Mustafa Pehlivan
Influence of Scientific Stories on Students Ideas about Science and Scientists
Sinan Erten, S. Ahmet Kıray, Betül Şen-Gümüş
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Volume 1, Number 2, April 2013, 75-82
ISSN: 2147-611X
A Case Study of E-tutors’ Teaching Practice: Does Technology Drive
Pedagogy?
Hsueh-Hua Chuang1*
National Sun Yat-sen University, Taiwan
1
Abstract
This article presents a case study of e-tutoring teaching practice during a 20-week e-tutoring program aimed at
improving the English proficiency of targeted students. The study revealed what and why certain online tools
were used by e-tutors and investigated how different technological proficiency and face-to-face (f2f) teaching
experience shaped e-tutors’ teaching practices in cyberspace. Data were collected through transcriptions of each
recorded synchronous Skype teaching session, interviews of e-tutors, project artefacts, and e-tutors’ weekly
memos. Results showed that use of Skype establishes a social presence in e-tutor and e-tutee instructional
relationships and that online broadcasting is often equivalent to online teaching for e-tutors who are comfortable
and familiar with face-to-face teaching environments. In addition, technology has shaped the teaching practice
of e-tutors. This finding implies an adapted framework of technological pedagogical content knowledge for etutors to maximise the benefits of the designed online tutoring environments.
Keywords: E-tutoring, TPACK.
Introduction
Web-based instruction has gained widespread recognition among researchers and educators because it is able to
provide learners with distant, interactive, and individualised learning activities (Miller & Miller, 2000; Roblyer
& Doering, 2010). In particular, the characteristics of individualised learning activities in web-based instruction
address the need for one-on-one tutoring support in a cost-effective method called e-tutoring. E-tutoring is often
called online tutoring because e-tutors interact directly with learners to support their learning processes via the
Internet even though they may be separated by both time and place (Denard, 2003; Flowers, 2007). E-tutoring
features instructional practices that range from highly-structured individualised support to occasional responses
to specific homework questions or assignments. Traditional face-to-face (f2f) personal tutoring is often not costeffective and not available to many children of low social economic status (SES) families (Flowers, 2007).
Thus, the concept of e-tutoring has become a viable option to replace traditional f2f tutoring. To make tutoring
accessible to more learners, recent web technology to accommodate individual choices has been developed,
featuring personal options and mutual interactions rather than a one-way delivery mode, making the
implementation of e-tutoring more feasible.
Pyle and Dziuban (2011) noted that web technology has driven online pedagogy in such a way that instructors
need to learn its use to assist their teaching in cyberspace. After reviewing the related e-tutor literature, Denis,
Watland, Pirotte, and Verday (2004) proposed competencies of e-tutors encompassing content and
metacognition and identities such as process facilitator, adviser, assessor, technologist, resource provider,
administrator, designer, co-learner, and even researcher as a reflective practitioner. They also addressed the
importance of the pedagogical and communication-related competencies of e-tutors. These roles and
competencies of e-tutors echo the recently advocated technological pedagogical content knowledge (TPCK)
framework for depicting a teacher’s professional practice in teaching using technology (Mishra & Koehler,
2006). Specifically, within online web environments, Lee and Tsai (2010) suggested that online instructors
should acquire technological pedagogical content knowledge-web (TPCK-W) competence as a sub-strand of the
overarching TPCK framework, to better address the requirements of online teaching practice.
*
Corresponding Author: Hsueh-Hua Chuang, hsuehhua@gmail.com
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Chuang
Most of the e-tutoring literature focuses on the increased demand for personal e-tutors because of their costeffectiveness compared with traditional personal tutors (e.g., Flowers, 2007). Descriptions of the design of etutoring models (e.g., Barker, 2002), the implementation of e-tutoring programs, and the ability of these
programs to improve identified skill deficiencies (e.g., Johnson & Bratt, 2009) have been well documented.
However, few studies have looked profoundly into how e-tutors conduct teaching practice in cyberspace and
how different technological proficiencies and f2f teaching experience are reflected in teaching or tutoring in
cyberspace. Thus, this study investigated the teaching practice of e-tutors with various degrees of technological
knowledge and face-to-face teaching experience. Specifically, we sought to
 understand what and why certain online learning tools were utilized by e-tutors; and
 identify characteristics of instructional practice of e-tutors with various degrees of technological
proficiency and face-to-face teaching experience.
Methods
Project background
Globalisation has made English an essential language in the global village. However, English-achievement test
results at elementary and secondary schools in Taiwan show that most students with low socioeconomic status
(SES) backgrounds are at the low end of the achievement scale (Chang, 2002). To bridge this achievement gap,
e-tutoring has been proposed, given the established effectiveness of online support for learning (Denard, 2003).
An e-tutor program sponsored by Taiwan’s National Science Council was initiated in 2009 to provide remedial
support for low SES students with the hope of improving their academic English proficiency.
The Moodle-based Internet course management system provided tutors and tutees with both synchronous and
asynchronous tools. E-tutors relied heavily on Skype’s video-conferencing tools to conduct synchronous
teaching and real-time text communication and used other asynchronous tools such as discussion boards and
email for communication purposes. Links to other online English learning resources and four modules of Flashbased multimedia courses, starting from basic phonics up through beginning- and intermediate-level reading,
were also embedded in the course management system. The design of these four multimedia English learning
modules reflected the standards set by Taiwan’s Ministry of Education (MOE). The wide range of course
content was intended to provide individualised support based on e-tutees’ progress and current English
proficiency so that each e-tutee could progress at his or her own pace.
Project procedure
Moodle is a secure open-source Internet-based course-management system that can be customised to fit each
individual course design. Barker (2002) stressed the importance of an online tutoring environment in the context
of computer-supported collaborative working. Other researchers (Denis, Watlan, Priotte, & Verday, 2004)
proposed that, given the interactive nature of computer-mediated communication (CMC) technology, e-tutoring
allows for a social constructivist approach involving e-tutors helping learners to manage learning resources and
interactions between e-tutors and their peers. Therefore, in designing the e-tutoring program described here, we
embedded multimedia units that encompass basic lessons on the English alphabet and phonics to beginning and
intermediate reading passages, as well as appropriate synchronous and asynchronous tools for the course
platform such as video conferencing (via Skype), learning portfolios in which e-tutors can leave qualitative
remarks on each formative assessment activity and e-tutees can track their progress and respond, and links to
other Internet English-learning resource sites. Thus, the e-tutoring course design combined a) learners’
independent work on the Moodle platform with its four multimedia Flash animation learning modules, b)
tutoring sessions led by e-tutors, c) online formative assessment (learning progress repost) carried out by the
same e-tutors, and d) CMC tools such as video conferencing, discussion forums, message boards, and email.
The e-tutoring program ran from September 2009 to January 2010, a 20-week period while schools were in
session, as a supplementary effort to target individual e-tutees’ English deficiencies. College and graduate
student e-tutors with adequate English proficiency were recruited to participate in the e-tutor program. Recruited
e-tutors had to attend three workshop sessions, totalling 12 hours, to enter the program as online English etutors. The three workshop sessions included training to familiarise themselves with the Moodle e-tutoring
course platform, communication technology tools (e.g., Skype), the course design of the remedial English
curriculum, and monitoring mechanisms to ensure the quality of online tutoring.
IJEMST (International Journal of Education in Mathematics, Science and Technology)
77
Volunteering e-tutors were recruited from a national university. The screening process included expression of
interest, ability at a specified English proficiency level, and group interviews to explain the program’ initiation
and implementation procedures. Given the voluntary nature of the program, we particularly emphasised the
participating e-tutors’ commitment to the program. E-tutors had to commit at least two hours a week to the
program, consisting of synchronous interaction via Skype as well as additional time spent on asynchronous
interactions such as e-tutee learning portfolios and discussion forums to make e-tutoring functional. Technical
and instructional support was provided to both e-tutors and e-tutees by full-time technical support personnel, an
educational technology professor, and a teacher of English as a foreign language (EFL). All e-tutors signed
informed consent documents and were informed that their teaching would be recorded and observed. In total, 10
e-tutors were selected to tutor 10 children who were recruited from an elementary school in a metropolitan area
in southern Taiwan. The particular elementary school was selected because it had appropriate available
technology and the school principal expressed willingness to provide computer lab access to the selected
elementary school children during designated times when school was in session. The school EFL teachers
identified 10 children, who, in their collective professional opinion, would benefit from supplementary etutoring support but would be unable to afford the high cost of personal tutoring. The participating 10
elementary school students were all fifth graders. The program thus targeted students in need of support without
the usual concern of providing each student with the individual access necessary to resolve the access issue of
the digital divide. Unfortunately, from the 10 dyads of e-tutor and e-tutee, four of the 10 initially recruited etutors withdrew from the project due to personal reasons. We recruited another four e-tutors and were left with
six complete cases of e-tutoring.
Participants in this study
We selected as study participants the six e-tutors who stayed throughout the program and completed a 20-week
e-tutoring session. We requested and received permission for the research from all e-tutors and e-tutees in the
program. To better understand these e-tutors technology backgrounds, during the first meeting we asked them to
complete a form describing their experiences with email, Skype video conferencing, MSN chat, and online
learning platforms. In addition, participants had to have demonstrated a level of English proficiency equivalent
to passing the General English Proficiency Test (GEPT) at a high to intermediate level, indicating the ability to
handle a broad range of topics with English capability roughly equivalent to that of a non-English-major
Taiwanese university graduate. The results of the technology background surveys showed that two of the male
e-tutors were more technology proficient than two female e-tutors in terms of more frequency and experience in
navigating information and communication technology (ICT) tools. Although the other two e-tutors also did not
have the highest level of technological proficiency, they had practical classroom experience in teaching English
face-to-face as a foreign language as well as tutoring experience that the two technology-proficient e-tutors
lacked.
Data sources and data analysis
We collected data from transcriptions of each recorded synchronous Skype teaching session, interviews with etutors, project artefacts from the Moodle course website, project meeting memos, and e-tutors’ weekly memos.
Data collection and analysis occurred throughout the study and sometimes occurred simultaneously. At the
initial stage of data analysis, a preliminary data analysis was conducted to check and track the data to identify
areas requiring further questioning and inquiries (Grbich, 2007). For example, when we observed less frequent
use of the online discussion forum, we interviewed e-tutors regarding this specific matter.
In addition, we also conducted frequency counts of each activity on the course website to rank the tools used
during e-tutoring. For example, if an e-tutor used Skype, email, and a learning portfolio in one teaching session,
each of these activities was counted. We conducted frequency counts of the six selected e-tutors who used the
various tools during the 20-week e-tutoring process.
Following the preliminary stage of data analysis, we conducted thematic analysis to explore aspects and issues
that became evident and central to research questions. Skype teaching sessions and interview data were the main
source of data in this thematic analysis stage. Transcriptions of all recorded synchronous Skype teaching
sessions and interviews of e-tutors were analysed using the constant comparative method (Lincoln & Guba,
1985). First, the transcriptions were coded. Then the coded segments were compared within each of the Skype
teaching sessions and interviews, and finally the concepts and themes across all Skype teaching session and
interviews were analysed until recurring themes emerged. Other data such as project artefacts from the Moodle
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Chuang
course website, project meeting memos, and e-tutors’ weekly memos provided confirmatory data for
triangulation purposes. A careful examination of the data collected identified themes elaborated in the following
section.
Results and Discussion
The dominant use of synchronous tools
Although the design of the e-tutoring course site incorporated a computer-supported collaborative learning
concept, it was also intended to create a shared knowledge corpus with external resources through tools for
synchronous support (i.e., Skype conferencing/chat) and asynchronous support (i.e., email, lesson unit
discussion boards, learning passport assessment). The majority of teaching practice was conducted using
synchronous tools such as Skype conferencing and chat sessions (see Table 1). Barker (2002) viewed electronic
mail and computer conferencing as the two most widely used resources in online communication because, when
an individual externalises knowledge through these two resources, the cognitive processes employed by the
individual are emphasised. In this e-tutoring situation, email was not as widely used as computer conferencing,
specifically the use of video conferencing via Skype. Two possible explanations for this result involve the
nature of tutoring and the limited Internet access on the student side.
Table 1. Frequency counts of each synchronous (S) and asynchronous (A) CMC tool used
CMC tools
Frequency Counts
%
Skype (S)
146
63%
Email (A)
20
9%
Discussion Forum (A)
16
7%
Learning Portfolio (A)
39
16%
Message Board (A)
12
5%
The challenge of tutoring and asynchronous interactions
The concept of tutoring is to offer individual guidance and attempt to attain each individual’s learning goals at
his or her own pace. The act of tutoring has a very high association with the degree of social presence.
Heilbronn and Libby (1973) proposed that technological immediacy can promote social presence because the
maximum amount of information is transmitted and social immediacy is conveyed through speech and verbal
and non-verbal cues. Thus, from the perspective of technological immediacy, e-tutoring creates a togetherness
of geographically-distant persons connected through a telecommunication medium. Among CMC tools,
asynchronous tools such as email are regarded as having a lower level of social presence than are synchronous
tools such as Skype video conferencing. Several e-tutors mentioned that their e-tutees expressed loss of contact
and great frustration when they had Skype problems and failed to meet each other online. Inability to use Skype
video conferencing during e-tutoring created a loss of immediacy in the e-tutor and e-tutee relationship. Thus,
the extensive and intensive use of Skype in e-tutoring can help support the establishment of social presence for
both e-tutors and e-tutees by providing additional verbal and visual cues that email and other CMC tools cannot
accomplish. Johnson and Bratt (2009), in their study of e-tutoring of school children by technology education
students, also described the crucial role of video conferencing in cultivating the tutor-tutee’s instructional
relationship. In our study, one of the e-tutors mentioned a case in which she had a schedule conflict and missed
a Skype meeting session; she felt that she did not engage in tutoring that day, although she did email the e-tutee
a worksheet and reviews of lesson units.
Demand for personal tutoring has increased for helping students meet national standards and benchmarks
because of the recent emphasis on educational standards. Most after-school tutoring programs in Taiwan are
directed toward meeting the demands of test-driven curricula. EFL tutoring in Taiwan often has a more
objective approach in which learning requires transmission of knowledge and should be teacher-directed. This
has made synchronous methods of e-tutoring (e.g., using Skype) one of its prominent features. Most e-tutors are
driven to fully regulate the learning process and take attention away from the learner. In the real physical
context, this is achieved by providing the learner with a one-on-one monitor using a top-down approach in terms
of course content, learning progress, and learner focus. One of the e-tutors said, “I’ll need to control the pace of
teaching on my side in the e-tutoring session. Discussion forums, message boards, and email do not seem to
fully respond to this need for spontaneous monitoring.”
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McMann (1994) reported that the roles an e-tutor assumes in conducting teaching practice do not differ much
from those of traditional f2f instruction. However, Mason (1991) noted that the roles of e-tutors involved
reasonability at both the technical and educational levels. In terms of technical role requirements, Berge (1995)
proposed that the e-tutor should be familiar, comfortable, and competent with ICT systems, and Baker (2002)
focused on competence in navigating various CMC tools in a web-based learning system. Therefore, the
challenge of transferring f2f tutoring to Skype video conferencing tutoring usually involves providing technical
guidance and feedback on technical problems. From the recorded teaching transcripts, we observed that e-tutors
in the beginning stage spent most of their time guiding the students in familiarisation and navigation of the
course platform and computer video-conferencing Skype functions such as text chat, sending files, and adjusting
the web camera and microphone volume. We noticed that the two e-tutors with higher technological skills used
a shared desktop so that they and their tutees could see each other’s desktop activities. When interviewed as to
why they utilised the shared desktop function, they gave two reasons: to share their teaching aids and resources
with the e-tutee and, more importantly, to prevent the tutee from multi-tasking by, for example, visiting gaming
websites during the e-tutoring session. Most of the other e-tutors later followed this example and adopted the
shared desktop model as a method of monitoring e-tutees.
Being free from the physical constraints of traditional f2f on-site tutoring, e-tutoring faces the challenge of an
effective attention-monitoring mechanism in cyberspace. This challenge was addressed in a previous study on
the design of e-learning environments for supporting students and tutors through the use of shared desktops and
shared applications (Odeh & Ketaneh, 2007). The two tech-savvy e-tutors who initiated the use of shared
desktops shared their experiences in an e-tutor meeting after which most of the other e-tutors also adopted the
shared desktop approach.
The impact of students’ limited access to the Internet
The e-tutees were mostly from low SES families and only three of the 10 participating tutees had home Internet
access. The students took advantage of the noon session in the school computer lab to meet with their e-tutors
and sometimes to conduct other online learning activities if their e-tutors were not simultaneously available on
Skype. E-tutors were aware of the access issue for most of the e-tutees and made efforts to be simultaneously
available online during the noon session when e-tutees were allowed access to the school computer lab. This
resulted in a majority of teaching practice (85%) conducted through synchronous tools such as Skype
conferencing. For those dyads that had email interactions, e-tutees had home Internet access. A previous study
(Chuang, Yang, & Liu, 2009) regarding the influence of digital divide factors on the motivation of low-SES
elementary school students in Taiwan to use technology to learn English found that the existence of computer
and Internet resources at schools or in the community was a significant predictor of learners’ motivation to
utilise technology to learn English. Creating a fair technological opportunity for everyone by removing
restrictions of region, education, and economic status through public access to ICT is a key to rectifying the
digital divide, particularly as e-tutoring has increasingly become a cost-effective technique for providing
remedial support to improve schoolchildren’s academic achievements.
Online broadcasting
In the six complete e-tutor and e-tutee cases, one phenomenon we observed was the unidentified line between
online broadcasting and online learning in the e-tutor group with lower-level IT fluency but with more f2f EFL
teaching experience. Even though the most common teaching practices of the e-tutors were inclined toward
objectivism and were generally teacher centred, we observed that the e-tutors with more f2f EFL teaching
experiences often used web cameras via video conferencing to broadcast to the e-tutees. They used paper flash
cards via the web camera to teach new vocabulary and conduct sentence drills. One of them even showed how
she pronounced a word by broadcasting her mouth shape via the webcam. They tested their e-tutees to see if
they had memorized new words by having them write down answers on a piece of paper and holding it toward
the web camera, rather than the more customary approach of typing real-time answers, so the e-tutors could
check their spelling. Those e-tutors with more f2f teaching experience belonged to the third generation of the
telelearning model. Taylor (1995) proposed that third-generation distance education is based on the use of
information technology, including audio/video conferencing and broadcast television/radio. In other words,
these educators are familiar with and comfortable with online broadcasting using recently developed
sophisticated web video conferencing technology like Skype to increase broadcasting interactivity. This is a way
of recognizing the need to simulate face-to-face communication through technologies that support two-way
communication between e-tutors and e-tutees.
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Chuang
The impact of technological proficiency
On the other hand, the two e-tutors with more sophisticated technological skills and fluency in navigating in
cyberspace were more comfortable in utilising online resources to teach the same content. For example, one etutor found a YouTube video to show to his e-tutee to illustrate how the mouth and tongue muscle should
coordinate when pronouncing an English word. They integrated an online dictionary into their program while
teaching reading using an online interactive multimedia book. They combined the benefits of interactive
multimedia with enhanced interactivity and access to an extensive body of Internet-connected teaching-learning
resources. In addition, they transferred electronic files more often and more frequently used other asynchronous
tools such as email, the discussion forum, and learning portfolios than the other two e-tutors with more f2f
teaching experience and less technological proficiency. One interesting observation is that they could still
conduct tutoring via other CMC tools when Skype conferencing encountered technical problems such as
webcam disconnection or slow Internet speed. In a similar situation, the other group with less fluency in ICT
tools would usually give up and reschedule another Skype tutoring session.
Although the mere presence of technological knowledge does not guarantee good online teaching, based on the
findings from this study on online tutoring, we would argue that proficient technical skills are the grounds on
which pedagogical knowledge and content knowledge can be combined to form online teaching technological
pedagogical content knowledge. Otherwise, online learning is just a cyber version of the physical world,
breaking only the space boundary but failing to address the issue of online learning as a way to actually
transform teaching and learning as advocated by most educational technology experts (Salmon, 2004).
McPherson and Nunes (2004) mentioned that, among the four roles of an e-tutor (i.e., pedagogical, social,
managerial, and technical), the technical roles are for academics the most daunting and challenging. We propose
that an e-tutor’s technological knowledge is the fundamental basis and could even be the primary criterion for
success as an online tutor. This is reflected in Barker’s (2002) emphasis on the technical skills required to be an
online tutor. IT skills and fluency imply a hierarchical qualification. Thus, in the context of the one-on-one
tutoring in a cyber teaching and learning environment, we propose this adapted diagram (Figure 1) of TPCK to
reflect the capability of an e-tutor to explore and maximize the benefits of online tutoring environments. This
adapted diagram, different from the original diagram that presents three equivalent circles of technology,
content, and pedagogy, stresses the importance of interactions among the three components T (Technology), P
(Pedagogy), and C (Content), while stressing that technology must be the base on which the other interactions
occur. In articulating the essence of TPCK, Koehler and Mishra (2008), when addressing the advent of new
technology, stated that the arrival of technology forced educators to think about core pedagogical issues such as
how to represent content on the web and further proposed that “It is the advent of technology that drives the
kinds of decisions we make about content and pedagogy, by highlighting or revealing previously hidden facets
of the content, by enabling connections between diverse domains of knowledge, or support newer forms of
technology” (p. 19). In fact, two of the initially-recruited e-tutors dropped out of the e-tutoring program because
that they did not feel comfortable teaching within a cyber environment due to their lack of IT fluency.
Figure 1. An adapted TPCK framework for e-tutors
81
Conclusion
This case study revealed two possible reasons for the incompatibility of tutoring and asynchronous interactions.
First, the use of synchronous video-conferencing tools such as Skype established a social presence in e-tutor and
e-tutee instructional relationships. Second, online broadcasting was often equivalent to online teaching for those
e-tutors who are comfortable and familiar with a face-to-face teaching environment. In addition, we also found
that technology shaped the teaching practices of e-tutors. This process, originating from technological
knowledge, encompasses what was once referred to as communication literacy and now falls under the broader
term of media literacy that includes recognising information needs, distinguishing ways of addressing gaps,
constructing strategies of locating, accessing, comparing, and evaluating information, and organising, applying,
and synthesising information (Livingstone, 2004). In addition, an adapted TPCK is proposed to support and
frame an e-tutor’s ability to understand the constraints and abilities of various technologies, along with the
pedagogical and content knowledge necessary for further adaptations if successful instructional practices are to
take place between e-tutors and e-tutees. This study provides insight into the instructional practice of e-tutoring
and contributes to the existing literature on the recruitment and training necessary to become a successful online
tutor.
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ISSN: 2147-611X
Transfer of Learning in Mathematics, Science, and Reading among
Students in Turkey: A Study Using 2009 PISA Data
Mack Shelley1*, Atila Yildirim2
1
Iowa State University
2
Necmettin Erbakan University
Abstract
Using Program for International Student Achievement (PISA) 2009 data we study the transfer of knowledge
among reading, mathematics, and science among Turkish students. Both Science and Reading are significant
predictors of Mathematics scores, although clearly Science is a much stronger predictor; the transfer from
Science to Mathematics is much greater than is the transfer from Reading to Mathematics. SCHOOLID is the
single strongest predictor of Mathematics outcomes, likely reflecting the importance of socioeconomic and
regional or urban/rural differences in the quality of education available to students. Both Mathematics and
Reading are significant predictors of Science scores, although Mathematics is a stronger predictor; the transfer
from Mathematics to Science is greater than is the transfer from Reading to Science. SCHOOLID is a weaker
predictor of Science outcomes than are Mathematics scores, suggesting that the importance of socioeconomic
and regional or urban/rural differences in the quality of education available to students may have slightly less
consequence for Science outcomes than does the transfer effect from Mathematics to Science. Both Science and
Mathematics are significant predictors of Reading scores, but the transfer from Science to Reading is much
more robust than the transfer from Mathematics to Reading. SCHOOLID and Science are nearly identically
strong predictors of Reading outcomes, suggesting that the importance of socioeconomic and regional or
urban/rural differences in the quality of education available is on a par with the Science transfer to Reading.
Implications of these findings are discussed.
Keywords: Transfer of learning, Turkey, PISA
Introduction
This article reports results from a study of mechanisms of transfer of learning (e.g., Haskell, 2011; Cormier &
Hagman, 1987; Thorndike & Woodworth, 1901; Thorndike, 1923) across mathematics, science, and reading for
15-year-old Turkish high school students participating in the 2009 PISA study. Interest in the transfer of
learning has been heightened by concerns among the makers of education policy in many countries to provide
more efficient, more effective, and longer-lasting gains in content knowledge in key areas of learning (Glewwe,
2002; Hanushek & Kimko, 2000). Our focus here in on the process of knowledge transfer as a mechanism to
develop the skills required for economic, social, and cultural development. These skills are measured in a county
that is classified by the International Monetary Fund (IMF, 2011) as a largely developed newly industrialized
country. Turkey has the world's 15th largest gross domestic product (GDP) in terms of purchasing power parity
(World Bank, 2012) and 17th largest nominal GDP (World Bank, 2011).
The transfer of learning from one academic subject area to another, or beyond the classroom, is not a novel area
of research, but is evolving toward more sophisticated means of analysis. Leberman, McDonald, and Doyle
(2006) address the need to understand how what is learned in the classroom can be adapted and used in the
workplace. Mestre (2005) explicates the complex and sometimes confusing perspectives on this topic by
distinguishing among different types of transfer: near and far, vertical and lateral, specific and nonspecific,
literal and figural. Other studies (e.g., Intergovernmental Studies Program, 2005) address the modalities by
which knowledge carries over in classroom learning and in training activities. Dixon and Brown (2012) have
addressed the crucial role in the transfer of learning that is played by the process of connecting concepts during
problem solving. They emphasize that the high school experience needs to provide sufficient authentic problem*
Corresponding Author: Mack Shelley, mshelley@iastate.edu
84
Shelley & Yildirim
solving and project-based activities to prepare students to deal with the types of problems they will need to solve
in the real world.
Of more direct relevance to the purposes of our study is the recent research by Khishfe (2012) on the use of an
explicit reflective approach to provide more effective transfer of nature of science (NOS) understandings into
similar contexts. The purpose of the study was to investigate the effectiveness of explicit NOS instruction in the
context of socially controversial scientific issues and explore whether it is possible to transfer acquired NOS
understandings taught explicitly in one context into other similar familiar and unfamiliar contexts. The results
showed no improvement in NOS understandings of participants in the non-NOS group in relation to the familiar
and unfamiliar contexts. In contrast, there was general improvement in the NOS understandings of participants
in the NOS group in relation to both the familiar and unfamiliar contexts.
Perkins and Salomon (1992) define transfer of learning as what happens when learning in one context enhances
(positive transfer) or undermines (negative transfer) a related performance in another context, as when learning
mathematics prepares students to study physics. Transfer includes near transfer (to closely related contexts and
performances) and far transfer (to rather different contexts and performances). Reflexive, or low road, transfer
involves the triggering of well-practiced routines by stimulus conditions similar to those in the learning context.
Mindful, or high road, transfer involves deliberate abstraction and a search for connections. Most formal
education aspires to transfer, either across subject areas or from the classroom into other aspects of a student’s
life and/or into subsequent employment. Consequently, the ends of education are not achieved unless transfer
occurs. As distinguished from ordinary learning, transfer has not occurred when a student solves problems at the
end of the chapter (which would be an example of ordinary learning) but is unable to solve similar problems
when they occur mixed with others at the end of the course or when related applications of the relevant concepts
cannot be applied successfully in another course or in other disciplines.
Several experiments seeking to document a positive impact of learning to program on problem solving and other
aspects of thinking yielded negative results (e.g., Pea & Kurland, 1984, Salomon & Perkins, 1987; Simon &
Hayes, 1977). However, some research has demonstrated that positive transfer can occur (e.g., Brown, 1989;
Campione et al., 1991; Clements & Gullo, 1984; Lehrer et al., 1988; Salomon et al., 1989). In general, near
transfer has been found to be more likely than far transfer to succeed. Two broad instructional strategies to
foster transfer can be identified: hugging and bridging (Perkins & Salomon, 1988). Hugging is based on
reflexive transfer, with instruction directly engaging learners in approximations to the performances that are
desired. For example, a teacher might give students trial exams rather than just talking about exam technique.
The learning experience thus maximizes the likelihood of later automatic low road transfer. In contrast, bridging
exploits the high road to transfer. Bridging implies instruction that encourages students to make abstractions and
search for possible connections. For example, a teacher might ask students to devise an exam strategy based on
their past experience, which would emphasize deliberate abstract analysis and planning.
PISA
The Program for International Student Achievement (PISA) addresses how well students can apply the
knowledge and skills they have learned at school to real-life challenges. The tests are designed to assess to what
extent students at the end of compulsory education can apply their knowledge to real-life situations and be
equipped for full participation in society (OECD, 2012). PISA, launched by the OECD (Organization for
Economic Co-operation and Development) in 1997, was designed to evaluate education systems worldwide
every three years by assessing 15-year-olds’ competencies in reading, mathematics, and science. The students
and their school principals also fill out background questionnaires to provide information on the students’
family background and how their schools are administered. The first PISA survey was carried out in 2000 in 43
countries, the second in 2003 in 41 countries, the third in 2006 in 57 countries, the fourth in 2009 in 74
countries, and the most recent survey was carried out in 2012 in 65 countries (OECD, 2012). Turkey, a member
of the OECD, participated in the PISA exam for the first time in 2003 to identify strengths of the education
system and areas in need of improvement (MONE, 2005, 2007).
PISA is a collaborative effort, bringing together scientific expertise from the participating countries and steered
jointly by their governments on the basis of shared, policy-driven interests. Through involvement in expert
groups, the participating countries ensure that the PISA assessment instruments are valid internationally and
take into account the cultural and curricular context of OECD member countries.
85
As in 2000, reading literacy was the focus of the PISA 2009 survey, but the reading framework has been
updated and now also includes the assessment of reading of electronic texts. The framework for assessing
mathematics was fully developed for the PISA 2003 assessment and remained unchanged in 2009. Similarly, the
framework for assessing science was fully developed for the PISA 2006 assessment and remained unchanged in
2009. PISA is structured to make it possible to find statistical associations between student achievement and
influences from family, school, and other educational sources. Interpretation of PISA results for policy purposes
must be sensitive to differences across countries and cultural contexts and must address actions taken by
families, government bodies, and educational organizations to impact all levels of educational systems. The
results from this study and from kindred analyses are intended to frame and facilitate decisions about education
policy taken by those who occupy positions of leadership in education such as ministers and secretaries of
education, those who make laws, technical staff who make operative and concrete decisions, administrators and
teachers who must implement specific educational actions, as well as the implementation of mandates or
guidelines that influence the behavior of students and their families.
PISA findings can be used by policymakers to gauge the knowledge and skills of students in their own country
(and in comparison with those of other participating countries), establish benchmarks for education
improvement compared to other countries or to enhance the capacity to foster equitable educational outcomes
and opportunities, and understand the relative strengths and weaknesses of their education systems (OECD,
2007). Students are assessed at age 15 because at that age they are approaching the end of compulsory education
in most OECD countries. The assessment is focused on ascertaining the extent of transfer of classroom-acquired
knowledge to everyday tasks and challenges, based on a dynamic model of lifelong learning in which the new
knowledge and skills that are necessary for successful adaptation to a changing world are acquired continuously
throughout life.
PISA uses paper-and-pencil tests, with assessments lasting a total of two hours for each student. Test items
include multiple-choice items and questions requiring students to construct their own responses, organized in
groups based on written presentation establishing a real-life situation. A total of about 390 minutes of test items
is covered, with different students taking different combinations of test items. Students answer a background
questionnaire, which takes 30 minutes to complete, providing information about themselves and their homes.
School principals are given a 20-minute questionnaire about their schools. In some countries, optional short
questionnaires are administered to parents to provide further information on reading engagement at the students’
homes, and students to provide information on their access to and use of computers as well as their educational
history and aspirations. Major domains have been reading in 2000, mathematics in 2003, science in 2006,
reading literacy in 2009, and mathematics in 2012.
The primary aim of the PISA assessment is to determine the extent to which young people have acquired the
wider knowledge and skills in reading, mathematics, and science that they will need in adult life, to assist with
data-driven decision making. The application of specific school-acquired knowledge in adult life depends on the
extent to which adults have acquired broader concepts and skills. In reading, the capacity to develop
interpretations of written material and reflect on the content and qualities of text are central skills. In
mathematics, the ability to reason quantitatively is more relevant than being able to answer familiar textbook
questions for the purpose of applying mathematical skills in everyday life. In science, specific knowledge such
as the names of plants and animals is less valuable than understanding broad topics such as energy consumption,
biodiversity, and human health. Students also need to develop communication and information technology skills
and learn to be adaptable, flexible, and oriented to solving problems.
Literacy
Reading literacy, which is based on cognitively-based theories emphasizing how reading assists to construct
comprehension, in print (Binkley & Linnakylä, 1996; Bruner, 1990; Dole, Duffy, Roehler, & Pearson, 1991)
and electronic media (Fastrez, 2001; Legros & Crinon, 2002; Reinking, 1994), is defined in terms of students’
ability to understand, use, and reflect on written and electronic text. Reading literacy is assessed in relation
to:(a) continuous and non-continuous text formats, including narration, exposition, and argumentation; (2)
proficiency in accessing and retrieving information, forming a broad general understanding of the text,
interpreting it, reflecting on its contents, and reflecting on its form and features; and (3) the purpose for which
the text was constructed. Mathematical literacy is concerned with students’ ability to analyze, reason, and
communicate ideas effectively as they pose, formulate, solve, and interpret solutions to mathematical problems
in different situations (Freudenthal, 1983). The PISA mathematics assessment focuses on quantity, space, shape,
change and relationships, and uncertainty; less emphasis is placed on numbers, algebra, and geometry.
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Shelley & Yildirim
Appropriate uses of mathematical language, modeling, and problem-solving skills are essential for student
success. A six-level performance scale is used to assess student PISA mathematics performance (Masters &
Forster, 1996; Masters, Adams, & Wilson, 1999), using an item response theory-based approach
Scientific literacy is the ability to use scientific knowledge and processes to understand the natural world and
participate in decisions that affect it (Koballa, Kemp, & Evans, 1997; Law, 2002). PISA’s science assessment
emphasizes scientific knowledge or concepts that help with understanding life and health, Earth and the
environment, and technology; describing, explaining, and predicting scientific phenomena; understanding the
process of scientific investigation; interpreting scientific evidence and conclusions; and knowing how to apply
scientific knowledge and processes in specific contexts. The emphasis is on a critical stance and a reflective
approach to science (Millar & Osborne, 1998; Norris & Phillips, 2003) and on science education for all people
(Fensham, 1985). Inevitably, scientific competencies draw upon reading and mathematical competencies (Norris
& Phillips, 2003). For example, aspects of mathematical competencies are required in data interpretation
contexts. Similarly, reading literacy is necessary when a student is demonstrating an understanding of scientific
terminology. These synergies among reading, mathematics, and science lie at the root of this analysis.
Preparation of students in reading, mathematics, and science skills is essential for economic growth and societal
development.
Education and the Economy in Turkey
The Economic Policy Research Foundation of Turkey (Özenç & Arslanhan, 2010) provided an evaluation of the
PISA 2009 results for Turkish students. Although Turkey achieved one of the largest improvements since 2003
in students’ scores among participating countries, Turkey’s students achieved only at OECD’s level 2, where 1
denotes the worst and 6 denotes the best performance, in all three areas of science, mathematics, and reading.
The report concludes that the need remains for comprehensive reform in the Turkish education system, to
establish the preconditions for Turkey to become a high-income country through improved competitiveness.
Among the 40 countries that participated in both 2003 and 2009, Turkey’s rank in science and mathematics rose
from 35th to 22rd place and in reading advanced from 33rd to 32nd place. Among the 65 countries evaluated in
the 2009 PISA test, Turkey ranked 43rd in science and mathematics and 41st in reading proficiency. From 2003
to 2009, Turkey’s mean score in mathematics rose from 423 to 445, the mean science score increased from 434
to 454, and the reading mean score grew from 441 to 464.
The Economic Policy Research Foundation of Turkey report attributed the partial improvement in Turkey’s
PISA performance on rising education expenditures, projects to enhance school enrollment for girls, free school
books, reduced class size, implementation of curriculum redesign for both formal and informal education, and
financial support mechanisms such as expanding elementary and secondary school scholarships to cover more
students. From 2003 to 2008, schooling participation rates grew from 90% to 95% for elementary schools, and
from 62% to 74% for secondary schools. The report concludes that such measures are inadequate to enhance
Turkey’s relative position, and called for comprehensive curricular change and integrated education reforms.
Blanchy and Şaşmaz (2011) focus on the fact that the dependency ratio (the number of children and the elderly
relative to the number of working-age people) is decreasing significantly in Turkey; this condition offers an
opportunity through about 2020 for the country to accelerate its socioeconomic development. Efforts to improve
the quality of its education services to address this opportunity are challenged by the nation’s disappointing
PISA results, with Turkey ranked 32nd among 34 OECD countries and with 40% of Turkish 15-year-old
students unable to attain a basic competence level in mathematical literacy. These difficulties are compounded
by a relatively high level of segregation associated with the socioeconomic background of Turkish students and
their families.
These concerns are supported by research showing that the knowledge and skills acquired during primary
education has an important positive impact on personal socioeconomic mobility (2002) and national economic
growth (2000), thereby necessitating a focus on learning acquisition and outcomes and further research targeted
to learning outcomes and their determinants at both the primary and secondary level. The authors attribute
Turkey’s improved performance to the Basic Education Reform that started in 1997, the Teaching Programs
Reform initiated in 2004, corresponding improvement in students’ skills, and increased motivation of schools
and students to perform better in cross-national comparisons. It is important to note that 15-year-olds, who are
the target for the PISA study, are outside the scope of mandatory education in Turkey, where only about 55-60%
of all 15-year-olds attend school regularly. The need for further research on the impact of socioeconomic
87
disparities and the lack of adequate preschool opportunities in disadvantaging Turkish students is frustrated by
the failure of Turkey to participate in the 2009 parent survey. We focus here on results from the student survey.
Since its inception, many studies have analyzed and interpreted PISA results for participating OECD and nonOECD countries. Several studies also have investigated Turkey’s performance on these assessments, focused on
either the mathematics or science performance of Turkish students (Alacaci & Erbas, 2010; Anil, 2009; Aypay,
2010; Demir & Kılıç, 2010; Demir, Kılıç, & Unal, 2010a, 2010b; Dincer & Uysal, 2010; EURYDICE, 2011;
Grisay & Monseur, 2007; Gumus & Atalmıs, 2011; Güzel & Berberoğlu, 2005; Güzeller & Akın, 2011;
Ovayolu & Kutlu, 2011; Unal & Demir, 2009; Ziya, Dogan, & Kelecioglu, 2010). In comparison to many other
countries participating in PISA, particularly OECD members, Turkey is disadvantaged in cross-national
comparisons on educational attainment as it has relatively large numbers of lower-socioeconomic students, a
low share of the budget allocated to education and research, and lower per capita income.
Data and Analysis
Our analysis uses data from Turkish students participating in the 2009 PISA study. The overall sample size is
4,963. One student who was listed as attending a private school was deleted from the analysis; the remaining
4,962 students on whom the analysis is based therefore all represent Turkish public schools, and the policy
perspectives we offer are relevant to Turkish public school students broadly. A total of 170 schools are
represented. The number of students per school ranged from a minimum of 1 to a maximum of 35, with an
average of 29.2 students per school. Although students were 15 years old at the time of PISA administration,
they are distributed across a range of grade levels: 24 (0.5%) were in 7th grade, 113 (2.3%) were in 8th grade,
1,225 (24.7%) were in 9th grade, 3,392 (68.4%) were in 10th grade, 196 (4.0%) were in 11th grade, and 12
(0.2%) were in 12th grade. A slight majority (2,536, or 51.1%) were male; 2,426, or 48.9% were female. The
data represent 751,283 weighted cases. Demographic distributions of the weighted data are very similar to what
is reported here for the unweighted results. For ease of interpretation, we report results for the unweighted data.
Our initial intent was to conduct a multilevel analysis of the data, with student at Level 1 and institutional
characteristics at Level 2. However, the thinness of data at the school level (with sometimes only 1 student per
school) made such an analysis problematic. In addition, the nature of the analysis, which is to attempt to
measure the transfer across reading, mathematics, and science, controlling for a number of student-level (Level1) characteristics, required the use of multiple regression using student-level predictor variables. Another
alternative approach, structural equation modeling, is not an efficient strategy given the large number of
predictor (exogenous) variables in this analysis, and is not as readily adaptable to the layered analysis we
undertake here with various combinations of predictors included in alternative model specifications. To adjust
for school characteristics, SCHOOLID (which identifies the school that a student attends) was added to the
model as a categorical main effect following initial model estimation without the SCHOOLID model
component; the discussion of model results focuses on the “full” model including SCHOOLID. The
SCHOOLID main effect in the model also serves as a surrogate measure for socioeconomic and regional
differences in Turkey that may precondition the likelihood of individual student success within a building.
Dependent Variables
Separate multiple regression models were estimated for each of the three dependent variables:
 PVMATHMEAN—Mean of 5 plausible values in mathematics
 PVSCIEMEAN—Mean of 5 plausible values in science
 PVREADMEAN—Mean of 5 plausible values in reading
Each dependent variable is the average of five plausible values for mathematics, science, and reading,
respectively. Plausible values are calculated because of the presence of missing data in measures of student
ability because it is too expensive and time-consuming for all students to answer every question in each of the
three areas. The cognitive data in PISA are scaled with the Rasch Model and the performance of students is
denoted with plausible values (OECD, 2009c). For minor domains, only one scale is included in the
international databases. For major domains, a combined scale and several subscales are provided. For each scale
and subscale, five plausible values per student are included. The methodology of plausible values consists of
computing posterior distributions around the reported values and assigning to each observation a set of random
values drawn from the posterior distributions. Plausible values therefore can be defined as random values from
the posterior distributions. For example, for a test including six dichotomous items, a continuous variable (i.e.,
88
Shelley & Yildirim
mental ability) can be transformed into an ordered categorical variable with possible scores of 0, 1, 2, 3, 4, 5 and
6. For purposes of our analytical approach, which is to estimate patterns of transfer across reading, mathematics,
and science content areas, we use combinations of the two other plausible values to predict each outcome. That
is, reading and mathematics plausible values are used to predict science plausible values, reading and science
are used to predict mathematics, and science and mathematics are used to predict reading.
Independent Variables
Independent variables were selected to encompass a range of student-level predictors, in addition to the Level-2
SCHOOLID main effect addressing school-level institutional and structural differences that may affect student
outcomes. Predictors also were selected with the purpose of maximizing the number of data values usable for
each model, by including predictors selected from a much larger set of potential independent variables with
relatively minimal amounts of missing data. The independent variables employed in our estimation equations
include (with the dataset mnemonic label and a brief description for each variable):
Leel-2 (school) predictor
 SCHOOLID—5-digit school ID
Level-1 (student and family) predictors
 ST01Q01—grade level
 ST10Q01—mother’s highest schooling attainment
 ST14Q01—father’s highest schooling attainment
 HISCED—highest educational level of parents
 MMINS—learning time (minutes per week)-Mathematics
 SMINS—learning time (minutes per week)-Science
 METASUM—meta-cognition: Summarizing
 UNDREM—meta-cognition: Understanding and Remembering
 ATTCOMP—attitude toward computers
 CSTRAT—use of control strategies
 CULTPOSS—cultural possessions
 DISCLIMA—disciplinary climate
 ELAB—use of elaboration strategies
 ENTUSE—instructional computer technology internet/entertainment use
 ESCS—index of economic, social, and cultural status
 HEDRES—home educational resources
 HIGHCONF—self-confidence in instructional computer technology high-level tasks
 HOMEPOS—home possessions
 ICTHOME—instructional computer technology availability at home
 JOYREAD—joy/like reading
 LIBUSE—use of libraries
 MEMOR—use of memorization strategies
 ONLNREAD—online reading
 USESCH—use of instructional computer technology at school
 WEALTH—wealth
A total of 18 multiple regression models were estimated, both with and without SCHOOLID, for each of the
following circumstances (with the same set of student-level predictors employed in each model):
 Predicting Mathematics from Science, with and without SCHOOLID
 Predicting Mathematics from Reading, with and without SCHOOLID
 Predicting Science from Mathematics, with and without SCHOOLID
 Predicting Science from Reading, with and without SCHOOLID
 Predicting Reading from Mathematics, with and without SCHOOLID
 Predicting Reading from Science, with and without SCHOOLID
 Predicting Mathematics from Science and Reading, with and without SCHOOLID
 Predicting Science from Mathematics and Reading, with and without SCHOOLID
 Predicting Reading from Science and Mathematics, with and without SCHOOLID
89
The logic behind this analysis was to investigate all possible combinations of transfer among the three subject
areas of Math, Science, and Reading. This process, conducted with models both including and not including the
level-2 identifier of building (SCHOOLID), makes it possible to compare the effectiveness of these prediction
models using student-level (Level-1) predictors adjusting for the Level-2 characteristics that make any one
school different from other schools. The same set of student-level predictors was included in each model.
We focus here on the results from predicting Mathematics from Science and Reading, predicting Science from
Mathematics and Reading, and predicting Reading from Science and Mathematics. In all cases, we report
detailed results from the models that include SCHOOLID and summarize the results of other models.
Results and Discussion
Predicting Mathematics from Science and Reading
Table 1 summarizes the multiple regression model predicting Mathematics scores from Science and Reading
scores, including all of the predictors listed above.
Table 1. Model results for predicting mean of 5 plausible values in mathematics from mean of 5 plausible values
in science and mean of 5 plausible values in reading
Source
df
F
p
Partial Eta Squared
Corrected Model
205
213.023
0.000
0.924
Intercept
1
32.457
0.000
0.009
MMINS
1
9.053
0.003
0.003
SMINS
1
42.551
0.000
0.012
METASUM
1
17.127
0.000
0.005
UNDREM
1
51.330
0.000
0.014
ATTCOMP
1
2.522
0.112
0.001
CSTRAT
1
9.202
0.002
0.003
CULTPOSS
1
2.684
0.101
0.001
DISCLIMA
1
4.566
0.033
0.001
ELAB
1
50.370
0.000
0.014
ENTUSE
1
0.452
0.502
0.000
ESCS
1
8.558
0.003
0.002
HEDRES
1
5.608
0.018
0.002
HIGHCONF
1
2.861
0.091
0.001
HOMEPOS
1
1.849
0.174
0.001
ICTHOME
1
1.052
0.305
0.000
JOYREAD
1
170.704
0.000
0.045
LIBUSE
1
45.594
0.000
0.013
MEMOR
1
202.903
0.000
0.054
ONLNREAD
1
7.578
0.006
0.002
USESCH
1
2.787
0.095
0.001
WEALTH
1
4.686
0.030
0.001
SCHOOLID
163
19.678
0.000
0.472
ST01Q01
4
32.741
0.000
0.035
ST10Q01
4
17.958
0.000
0.020
ST14Q01
4
2.767
0.026
0.003
HISCED
6
7.003
0.000
0.012
PVSCIEMEAN
1
2296.548
0.000
0.391
PVREADMEAN
1
73.260
0.000
0.020
Error
3584
Total
3790
Corrected Total
3789
The estimated model fits quite well, with values of 0.924 for R2 and 0.920 for adjusted R2. Both Science and
Reading are significant predictors of Mathematics scores, although clearly Science is a much stronger predictor
with a much larger F value and much larger value of partial eta squared (which measures the proportion of
explained variance attributable to each predictor); clearly, the transfer from Science to Mathematics is much
greater than is the transfer from Reading to Mathematics. SCHOOLID, by the metric of partial eta squared, is
90
Shelley & Yildirim
the single strongest predictor of Mathematics outcomes, likely reflecting the importance of socioeconomic and
regional or urban/rural differences in the quality of education available to students. The importance of
SCHOOLID is underscored by the fact that (detailed results not shown) when SCHOOLID is not included as a
predictor of Mathematics R2 drops to 0.856 and adjusted R2 declines to 0.855; without SCHOOLID in the
model, Science is far and away the most important predictor and Reading remains significant but far less
consequential. With SCHOOLID included in the model, when Mathematics scores are predicted only by
Science together with the other independent variables, R2 is 0.923 and adjusted R2 is 0.918; predicting
Mathematics from Reading without SCHOOLID in the model yields weaker results, with R2 of 0.876 and
adjusted R2 of 0.868. In the absence of SCHOOLID, the prediction equation for Mathematics with Science
yields R2 of 0.852 and adjusted R2 of 0.850, and with Reading as the predictor R2 drops sharply to 0.774 and
adjusted R2 declines to 0.772.
Predicting Science from Mathematics and Reading
Table 2 summarizes the multiple regression model predicting Science scores from Mathematics and Reading
Table 2. Model results for predicting mean of 5 plausible values in science from mean of 5 plausible values in
mathematics and mean of 5 plausible values in reading
Source
df
F
p
Partial Eta Squared
Corrected Model
205
235.068
0.000
0.931
Intercept
1
146.033
0.000
0.039
MMINS
1
0.071
0.789
0.000
SMINS
1
0.129
0.719
0.000
METASUM
1
36.760
0.000
0.010
UNDREM
1
170.258
0.000
0.045
ATTCOMP
1
3.776
0.052
0.001
CSTRAT
1
1.268
0.260
0.000
CULTPOSS
1
23.789
0.000
0.007
DISCLIMA
1
7.907
0.005
0.002
ELAB
1
0.208
0.649
0.000
ENTUSE
1
5.790
0.016
0.002
ESCS
1
13.428
0.000
0.004
HEDRES
1
1.948
0.163
0.001
HIGHCONF
1
4.597
0.032
0.001
HOMEPOS
1
0.205
0.651
0.000
ICTHOME
1
0.728
0.394
0.000
JOYREAD
1
39.638
0.000
0.011
LIBUSE
1
7.581
0.006
0.002
MEMOR
1
73.539
0.000
0.020
ONLNREAD
1
25.850
0.000
0.007
USESCH
1
0.166
0.683
0.000
WEALTH
1
1.760
0.185
0.000
SCHOOLID
163
13.455
0.000
0.380
ST01Q01
4
9.583
0.000
0.011
ST10Q01
4
20.504
0.000
0.022
ST14Q01
4
2.013
0.090
0.002
HISCED
6
15.337
0.000
0.025
PVMATHMEAN
1
2296.548
0.000
0.391
PVREADMEAN
1
1208.174
0.000
0.252
Error
3584
Total
3790
Corrected Total
3789
The estimated model fits quite well, with values of 0.931 for R2 and 0.927 for adjusted R2. Both Mathematics
and Reading are significant predictors of Science scores, and both have robust partial eta squared values,
although Mathematics is a stronger predictor with a larger F value and larger value of partial eta squared; the
transfer from Mathematics to Science is greater than is the transfer from Reading to Science. Measured by
partial eta squared, SCHOOLID is a slightly weaker predictor of Science outcomes than are Mathematics scores,
91
suggesting that the importance of socioeconomic and regional or urban/rural differences in the quality of
education available to students may have slightly less consequence for Science outcomes than does the transfer
effect from Mathematics to Science. The much less consequential role of SCHOOLID is underscored by the fact
that (detailed results not shown) R2 drops just to 0.888 and adjusted R2 declines to 0.887 with SCHOOLID not
included as a predictor of Science; without SCHOOLID included in the model, both Mathematics and Reading
are robust predictors of Science, although transfer from Mathematics to Science is marginally more
consequential than the transfer from Reading to Science. With SCHOOLID included in the model, when
Science scores are predicted only by Mathematics together with the other independent variables, R2 is 0.907 and
adjusted R2 is 0.902; predicting Science from Reading without SCHOOLID in the model yields somewhat
weaker results, with R2 of 0.886 and adjusted R2 of 0.880. In the absence of SCHOOLID, the prediction
equation for Science with Mathematics as a predictor yields R2 of .848 and adjusted R2 of 0.846, and with
Reading as the predictor R2 drops somewhat to 0.825 and adjusted R2 declines to 0.823.
Predicting Reading from Science and Mathematics
Table 3 summarizes the multiple regression model for predicting Reading scores from Science and Mathematics
Table 3. Model results for predicting mean of 5 plausible values in reading from mean of 5 plausible values in
science and mean of 5 plausible values in mathematics
Source
df
F
p
Partial Eta Squared
Corrected Model
205
125.088
0.000
0.877
Intercept
1
170.885
0.000
0.046
MMINS
1
2.109
0.147
0.001
SMINS
1
6.692
0.010
0.002
METASUM
1
29.941
0.000
0.008
UNDREM
1
6.394
0.011
0.002
ATTCOMP
1
23.052
0.000
0.006
CSTRAT
1
31.196
0.000
0.009
CULTPOSS
1
8.423
0.004
0.002
DISCLIMA
1
9.906
0.002
0.003
ELAB
1
29.248
0.000
0.008
ENTUSE
1
25.647
0.000
0.007
ESCS
1
14.546
0.000
0.004
HEDRES
1
7.440
0.006
0.002
HIGHCONF
1
3.086
0.079
0.001
HOMEPOS
1
0.029
0.865
0.000
ICTHOME
1
8.826
0.003
0.002
JOYREAD
1
89.021
0.000
0.024
LIBUSE
1
12.338
0.000
0.003
MEMOR
1
0.386
0.534
0.000
ONLNREAD
1
3.230
0.072
0.001
USESCH
1
17.014
0.000
0.005
WEALTH
1
3.852
0.050
0.001
SCHOOLID
163
7.161
0.000
0.246
ST01Q01
4
12.696
0.000
0.014
ST10Q01
4
1.860
0.115
0.002
ST14Q01
4
3.025
0.017
0.003
HISCED
6
5.255
0.000
0.009
PVSCIEMEAN
1
1208.174
0.000
0.252
PVMATHMEAN
1
73.260
0.000
0.020
Error
3584
Total
3790
Corrected Total
3789
The estimated model fits quite well, with values of 0.877 for R2 and 0.870 for adjusted R2. However, it should
be noted that this model predicts Reading scores less well than do the corresponding models predicting
Mathematics and Science scores. Both Science and Mathematics are significant predictors of Reading scores,
but the transfer from Science to Reading is much more robust than the transfer from Mathematics to Reading.
92
Shelley & Yildirim
Measured by partial eta squared, SCHOOLID and Science are nearly identically strong predictors of Reading
outcomes, suggesting that the importance of socioeconomic and regional or urban/rural differences in the quality
of education available is on a par with the Science transfer to Reading. The marginal role of SCHOOLID is
underscored by the fact that (detailed results not shown) R 2 drops to 0.837 and adjusted R2 declines to 0.836
with SCHOOLID not included as a predictor of Reading; without SCHOOLID included in the model,
Mathematics is a fairly robust predictor of Reading, and the transfer from Mathematics to Reading is trivially
small. With SCHOOLID included in the model, when Reading scores are predicted only by Mathematics
together with the other independent variables, R2 is 0.836 and adjusted R2 is 0.827; predicting Reading from
Science with SCHOOLID included in the model results in stronger results, with R2 of 0.875 and adjusted R2 of
0.868. In the absence of SCHOOLID, the prediction equation for Reading with Mathematics yields R 2 of 0.778
and adjusted R2 of 0.775, and with Science as the predictor R 2 rises notably to 0.832 and adjusted R2 increases
to 0.830.
Discussion
PISA data and results such as those presented in this research provide governments with a powerful tool to
shape their policymaking, particularly regarding educational impacts and workforce development. Our results
suggest that in the Turkish context there is convincing evidence that decisions regarding resource allocation and
curriculum should take can benefit from taking into consideration the asymmetries that we have noted.
A major conclusion from our findings is that there is clear evidence of transfer from Science to Mathematics.
There is reciprocal evidence of transfer from Mathematics to Science. Reading plays only a limited role in
predicting either Mathematics or Science scores. Transfer from Science to Reading is much more robust than the
transfer from Mathematics to Reading. This set of results emphasizes a key policy dilemma. From a
policymaking and policy implementation perspective, is it better to strengthen the STEM (science, technology,
engineering, and mathematics) linkages and thereby heighten the reciprocal linkages between Mathematics and
Science? Or, is it better strategy to redirect resources to strengthen the thus far more limited transfer role played
by Reading, thereby providing another set of stronger linkages to enhance transfer from Reading to both
Mathematics and Science?
A second area of potential implications arises from the highly varied role played by the socioeconomic and
regional or urban/rural differences in the quality of education available to students summarized in the
SCHOOLID variable, which is the single strongest predictor of Mathematics outcomes, but is a weaker
predictor of Science outcomes than are Mathematics scores, and about equal to Science as a predictor of
Reading outcomes. These diverse effects of school-level characteristics provide some intriguing policy
alternatives. As SCHOOLID is the strongest predictor of Mathematics outcomes, it may be an effective policy
option to concentrate public expenditures and legislation on efforts to equalize the socioeconomic disparities if
the “prime directive” is to enhance students’ Mathematics outcomes. Resulting higher Mathematics scores then
would be expected to eventuate in positive transfer to Science. In turn, since Science and SCHOOLID are about
equally important predictors of Reading outcomes, further positive effects on Reading could be anticipated from
the subsequent enhancement of Science outcomes.
However, another relevant dimension to addressing transfer across reading, science, and mathematics, as
measured by PISA, is that verbal acuity (writing and reading) may be thought of as a cognitive process and
learning tool in science and mathematics education (e.g., Gunel, 2009). This adds a dimension to the discussion
of student outcomes and the interdependence among skill sets that argues alternatively for providing a more
robust resource base to enhance verbal skills. Also, within the Turkish context it seems imperative to alleviate
the major regional, urban/rural, and socioeconomic disparities to increase the rate at which adolescents remain
in public education. The implication of Turkey’s new 4+4+4 system (4 years of first-level primary education, 4
years of second-level primary education, and 4 years of secondary education) also must be taken into
consideration.
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ISSN: 2147-611X
Representations of Fundamental Chemistry Concepts in Relation to the
Particulate Nature of Matter
Zübeyde Demet Kırbulut1*, Michael Edward Beeth2
1
Harran University
2
University of Wisconsin Oshkosh
Abstract
This study investigated high school students’ understanding of fundamental chemistry concepts - states of
matter, melting, evaporation, condensation, boiling, and vapor pressure, in relation to their understanding of the
particulate nature of matter. A sample of six students (four females and two males) enrolled in a second year
chemistry course at a midwestern high school in the USA was interviewed about their conceptions of states of
matter, melting, evaporation, condensation, boiling, and vapor pressure. Interviewees were also asked to apply
these concepts to explain everyday phenomena. Purposeful typical case sampling method was used to identify
the students who were interviewed for this study. Evidence from these interviews indicates that multiple
representations of the particulate nature of matter by students contribute to their understanding of the
aforementioned fundamental concepts.
Key words: Chemistry education, Conceptions, States of matter, Phase change, Particulate nature of matter
Introduction
Research on students’ conceptions has shed light on a wide range of issues related to learning science concepts
in school, to applying concepts when explaining everyday phenomena and to teaching for conceptual
understanding. Numerous studies have reported misconceptions with specific science concepts. These
misconceptions have serious implications for understanding conceptually related ideas by the student as well as
implications for teaching for conceptual understanding (see Duit, 2007 for a bibliography of literature on
students’ and teachers’ conceptions and science education). With respect to chemistry, many high school age
students are unsuccessful in their struggle to learn fundamental concepts such as states of matter, melting,
evaporation, condensation, boiling, and vapor pressure (Aydeniz & Kotowski, 2012; Canpolat, 2006). One
possible explanation for why learning these concepts is difficult is that many students are not invoking multiple
representations of a foundational chemistry concept, the particulate nature of matter, that could help a student
explain most fundamental chemistry concepts (Gabel, Samuel, & Hunn, 1987). Consequently, students are not
able to explain their understanding of concepts at the macroscopic, microscopic and submicroscopic levels of
representation (Gilbert & Treagust, 2009).
Theoretical Framework
States of matter, melting, evaporation, condensation, boiling, and vapor pressure are fundamental concepts in
many chemistry courses. Foundational to solid explanations for each of these is a well-articulated understanding
of the particulate nature of matter that includes references to the kinetic molecular theory, the structure of matter
and bonding. While many studies have investigated student conceptions related to these concepts individually
(Bar & Galili, 1994; Bar & Travis, 1991; Canpolat, 2006; Chang, 1999; Gopal, Kleinsmidt, & Case 2004;
Johnson, 1998a, b; Osborne & Cosgrove, 1983; Paik, Kim, Cho, & Park, 2004; Tytler, 2000), few studies have
looked across students’ explanations for these conceptually related topics. Osborne and Cosgrove (1983)
conducted clinical interviews with children from eight to 17 years of age to investigate their conceptions of the
changes in the states of water. They reported that younger children had superficial understanding about
evaporation, condensation, boiling, and melting while older children held similar views to the younger children
*
Corresponding Author: Zübeyde Demet Kırbulut, demetkirbulut@yahoo.com
97
even though they were exposed to formal teaching related to these concepts. Similarly, Chang (1999)
investigated college students’ conceptions of evaporation, condensation, and boiling and concluded that college
students had superficial understanding about these concepts, especially in relationship to their understanding of
water vapor. Johnson (1998a, b) indicated that students had difficulty in understanding the bubbles in boiling
water, evaporation, condensation and thus the gaseous state. He concluded that a robust understanding of the
particulate nature of matter was problematic for these learners. Also, Gopal et al. (2004) interviewed secondyear chemical engineering students and concluded that these students had inadequate understanding of
evaporation and condensation.
Bar and Travis (1991) further explored children’s conceptions of phase changes, liquid to gas, and reported that
children’s understanding of boiling preceded their understanding of evaporation. Following this line of work,
Bar and Galili (1994) investigated conceptions of evaporation in children from 5 to 14 years of age. They
indicated four views regarding children’s conceptions of evaporation: i) water disappeared; ii) water was
absorbed in the floor or/and ground; iii) when water evaporated, it was unseen and being transferred into an
alternative location such as in the sky, air or ceiling; and iv) water was transformed into air. Tytler (2000) also
found that young children did not show greater appreciation related to evaporation and condensation in a study
which he compared year 1 and year 6 students’ conceptions of evaporation and condensation. Canpolat (2006)
used open-ended questions and interviews to explore undergraduate students’ misconceptions related to
evaporation, evaporation rate, and vapor pressure. He found that students had superficial understanding related
to these concepts, with the following main misconceptions: i) in order for evaporation to take place, a liquid has
to take heat from its environment; ii) the evaporation rate of a liquid in an open container is different from that
of the liquid in a closed container; iii) in a closed container, the evaporation rate decreases as time passes; and
iv) the evaporation rate changes with surface area; v) in the case of adding or removing vapor, the vapor
pressure changes. Collectively, these studies support the need to continue investigating relationships among
fundamental concepts in chemistry and to seek information related to the extent to which students use multiple
representations of the particulate nature of matter when expressing formal chemistry concepts and everyday
phenomena.
The purpose of this study was to investigate advanced high school chemistry students’ understanding of states of
matter, melting, evaporation, condensation, boiling, and vapor pressure in relation to understanding of the
particulate nature of matter and students’ application of these concepts. This study is intended to identify the
role that a sound understanding of the particulate nature of matter has on other fundamental chemistry concepts.
This study addressed the following research questions:
1. How do students represent their understanding of the particulate nature of matter when asked to explain states
of matter, melting, evaporation, condensation, boiling and vapor pressure?
2. How do students apply their understanding of evaporation, condensation, and boiling in relation to the
particulate nature of matter when explaining everyday phenomena related to these concepts?
Method
Sample
The study employed a phenomenological method involving a small number of subjects through extensive and
prolonged engagement to develop patterns and relationships of meaning (Creswell, 1994). Six students (four
females, two males, ages 16 to 18, and grades 10-12) enrolled in a midwestern high school in the USA were
interviewed for this study. The students were enrolled in an advanced chemistry course at this high school. Total
enrollment for this high school was 1,153 with 22 students enrolled in the advanced chemistry course.
Purposeful typical case sampling method was used to identify the students who were the focus of the study
(Patton, 1990). As key informants, teachers were asked to identify those students who displayed average or
above achievement in and positive attitudes toward learning chemistry based on their observations, students’
grades in chemistry and students’ engagement in classroom activities.
Procedure
Interviews were conducted near the end of the academic year, after all of the topics of interest to this study had
been taught to the students in the study. The interview questions we selected are based on similar clinical
interviews used in previous studies (Bar & Galili, 1994; Boz, 2006; Canpolat, Pinarbasi, & Sozbilir, 2006;
Chang, 1999; Gopal et al., 2004; Osborne & Cosgrove, 1983; Shepherd & Renner, 1982). Our interview
98
Kırbulut & Beeth
consisted of seven questions and follow-up probes to investigate advanced high school chemistry students’
understanding of states of matter, melting, evaporation, condensation, boiling, and vapor pressure. During the
interview, students were also asked to explain everyday phenomena related to the concepts of interest and to
interpret graphs representing phase change (see Appendix for the interview protocol). Interviews lasted up to 55
minutes, were video recorded and transcribed for analysis. The interview protocol was piloted by the researchers
and revised for face validity prior to implementing it with the students in this study.
Data Analysis
Interviews were analyzed based on Creswell’s (1994) six generic steps: i) organize and prepare the data for
analysis, ii) read through all the data, iii) code the data, iv) generate themes or categories using the coding, v)
organization and the description of the data in terms of the coding and themes, vi) interpretation of the data. The
authors and their colleague independently coded the data according to a priori criteria (see Table 1), discussed
any conflicts between categories, and the categories were finally verified. Students’ responses to interview
questions were categorized as sound, partial or no understanding of the aforementioned concepts. However, it
should be noted in what follows that a category for “no understanding” was only necessary for the concept of
vapor pressure. Table 1 contains the complete coding scheme and criteria used for categorization of students’
understanding for each of the concepts as well as the phenomenon questions used in the study. In the transcripts
that follow, the number of the specific code that was applied to a statement is given in parenthesis immediately
following that segment.
Table 1 Coding and categorization scheme for students’ interview data
Codes
Criteria
1.1. Sound Understanding of
 Particles in solids are
Solids, Liquids, and Gases
tightly packed.
1.1.1 Solids
 Particles in solids have
1.1.2 Liquids
restricted movement.
1.1.3 Gases
low kinetic energy.
strong attractions
between them.
 Particles in liquids are
further apart than in
solids.
 Particles in liquids
move freely than in
solids.
have higher kinetic
energy than in solids.
have weaker
attractions between
them than in solids.
 Particles in gases are
further apart than
liquids.
 Particles in gases move
freely than in liquids.
 Particles in gases have
the highest kinetic
energy compared to
particles in liquids and
solids.
Table 1 (continued)
Codes
1.2. Partial Understanding of
1.2.1 Solids
1.2.2 Liquids
1.2.3 Gases
Melting
2.1.1. Representational
Understanding of
Melting
2.1.2. Melting Phenomenon
Melting
Understanding of
Melting
2.2.2. Melting Phenomenon
Evaporation
Understanding of
Evaporation
3.1.2. Evaporation
Phenomenon
3.1.3. Application of Everyday
Phenomena
Evaporation
Understanding of
Evaporation
3.2.2. Evaporation
Phenomenon
Phenomena
Criteria
 Particles in gases have
the weakest attractions
between them
compared to particles
in liquids and solids.
It includes a subset of the
sound
understanding
criteria, but not all of them
with misconceptions.

Matter is not
continuous.
 There are forces acting
between particles.
 Melting is a physical
change.
 Pure substances melt at
specific temperature.
 The temperature is
constant during
melting of a pure
substance.
 The kinetic energy of
particles increases
during melting.
sound
understanding

Matter is not
continuous.
 Gases are in constant
motion.
between particles.
 Evaporation of liquid
occurs at every
temperature without
heating by using its
internal energy.
 Evaporation is a
physical change.
sound
understanding
99
100 Kırbulut & Beeth
Table 1 (continued)
Codes
Condensation
Understanding of
Condensation
4.1.2. Condensation
Phenomenon
Phenomena
Criteria
following
sound
understanding criteria, but
not all of them with
misconceptions.
 Matter is not
continuous.
motion.
between particles.
 Condensation is a
physical change.
 Steam is condensed
water vapor.
 In a closed system,
condensation of water
vapor occurs when the
water vapor in the
system is saturated.
Boiling
following
sound
Understanding of
Boiling
misconceptions.
5.1.2. Boiling Phenomenon  Matter is not
continuous.
Phenomena
motion.
between particles.
 Pure substances boil at
specific temperature.
 The temperature is
constant during boiling
of a pure substance.
 Boiling is a physical
change.
Vapor Pressure
following
sound
misconceptions.
 Matter is not
continuous.
motion.
between particles.
 Vapor pressure is the
pressure exerted onto
the surface of a liquid
by particles at the
vapor phase which is
in equilibrium with its
liquid in a closed
container.
101
Table 1 (continued)
Codes
Criteria
6.1. Partial Understanding of Vapor  Vapor pressure is
Pressure
dependent on
temperature.
 Vapor pressure is
independent from
surface area.
6.2. No Understanding of Vapor
There is no or enough
Pressure
evidence
to
evaluate
students’ understanding as
sound or partial.
Results
Students’ Conceptions of States of Matter
Students’ conceptions of states of matter were categorized as sound understanding or partial understanding
in terms of their representations of the particulate nature of matter. Data excerpts selected for the sound
understanding category included statements consistent with the kinetic molecu lar theory, the structure of
matter and bonding. Excerpts categorized as partial understanding included one of these criteria and one or
more of the misconceptions known for that concept. Two students showed sound understanding of states of
matter. In the excerpt below, information consistent with the kinetic molecular theory, the structure of
matter and bonding are identified by the coding categories for sound understanding:
David: Solids retain their shape (1.1.1), and at any temperature they don’t fill t he container they’re put
in. They have a set mass, like a pressure as opposed to gas. If you compress [a gas], it gets smaller. So a
solid, if it is like a real solid, not like something flexible (1.1.1), it won’t compress under pressure
(1.1.1) until pressure gets too great and then it will just compact all the way. Liquids fill whatever
container they are in and fill all those space and flow downwards or in the direction of gravity if they
are poured out of a container (1.1.2). Molecules of gas move around the most (1.1.3)– they have kinetic
energy and they move around the fastest, and then liquids move around slightly less (1.1.2) and they
hold together because of bonds (1.1.2), for solids- all the molecules compact in one area (1.1.1) so they
don’t move around as much as the other two (1.1.1).
The other students interviewed were coded as having partial understanding of states of matter in terms of
the particulate nature of matter. In the excerpt that follows, one of the students mentioned the structure o f
substances when explaining the characteristics of solids, liquids and gases but she did not mention
anything about the kinetic molecular motion of particles:
Mary: I believe for gases, the molecules are further apart (1.2.3). They are spread out all over the place. And
liquids, the molecules are kind of closer to each other (1.2.2). And then solids, the molecules don’t even have
any space between them. They are very close to each other (1.2.1).
Students’ Conceptions of Melting
Student interview data could be categorized into sound and partial understanding for the concept of
melting as well. The first category included six criteria for sound understanding of melting; the second
category addresses a subset of these criteria with one or more of the misconceptions known for that
concept. Two students were identified with sound understanding of melting, the same two students who
expressed sound understanding of states of the particulate nature of matter. One of these students (David)
described melting as follows:
David: Change of a solid into its’ liquid state (2.1.2) - so going from being compacted to fluid and able
to move, so breaking apart the bonds that are holding the molecules together so they can move around
slightly (2.1.2).
During the interview, students were asked to identify where melting would occur on a phase change graph
we provided. The two students with a sound understanding indicated that melting of ice would occur at 0
0
C, and that the temperature would stay constant during melting.
Four students expressed ideas categorized as partial understanding. None of the students in this group had
sound understanding of the particulate nature of matter according to our earlier analysis. Students placed in
this category indicated definitions of melting that were between the macroscopic and microscopic level of
understanding as indicated in the following excerpt:
Interviewer: How would you describe melting?
Lisa: The molecules - like the ice would break up.
Interviewer: Break up?
Lisa: I don’t know what they do. They jut get warmer so they melt (2.2.2).
Interviewer: Ok. When you think about melting what comes to your mind?
Lisa: I really just think the temperature changes. It’s dripping because it is not solid anymore (2.2.2).
Students’ Conceptions of Evaporation
Only one student had sound understanding of evaporation. This understanding was consistent with and
supported by his sound understanding of the particulate nature of matter:
Interviewer: How would you define evaporation?
David: Liquid forming into a gas (3.1.2) - so the bonds that are holding the liquid together (3.1.2), kind
of loosely so that they stay in whatever container they are in if it is open (3.1.2), are gone completely.
They just are free to go wherever there is space. So as a liquid they just stay in whatever container they
are in, and as a gas they flow free in the environment (3.1.2).
David understood that evaporation involved a physical change, matter was not continuous, gases were in
constant motion, and that forces acting between particles needed to be included in his explanation. In
addition, he is one of the only students who indicated previously that evaporation of water could occur at
any temperature where it was in the liquid phase:
Interviewer: Can you show on the phase change graph where evaporation occurs?
David: Anywhere where it is liquid it would evaporate because water evaporates and it is not at 100 0C
so as long as it is a liquid it would evaporate into a gas so anywhere [on the graph where it is a] liquid.
The other five students had partial understandings of evaporation in terms of the particulate nature of
matter with known misconceptions. Lisa, for example, states her confusion over temperatures at which
water could evaporate and the misconception that when water does evaporate, it breaks into hydrogen and
oxygen gases:
Interviewer: How would you describe evaporation?
Lisa: Evaporation is when the water molecules break up (3.2.2) and turn into a gas and go into the air
(3.2.2).
Interviewer: Ok. Is there any specific temperature for evaporation?
Lisa: I thought it was at 100 0C but it might be like in a range or something. No, I guess it can’t be
because there is evaporating when it is not at hot that boiling temperature. I don’t really know when it
would evaporate (3.2.2).
Another student, Martha, also indicated partial understanding of the particulate nature of matter when
describing evaporation of water:
Martha: The water would just evaporate out and then go into the sky. Once it gets too much, it would
come down and then it would be like a cycle (3.2.2).
Interviewer: In terms of the particles, how would you describe this process?
Martha: The particles would go from the water and it move faster evaporating to the…
Interviewer: Do you think there is any change in terms of particles?
Martha: I don’t think they get bigger or smaller, they just move faster (3.2.1).
Interviewer: How would you write the formula for water? What would happen to water when it
evaporates?
Martha: It loses its oxygen. Like the oxygen goes to O2 and then the hydrogen bonds together to make
H2 (3.2.1).
When students with partial understanding were asked to show where evaporation occurred on a phase
change graph, they all indicated that evaporation of water would only occurred at or above 100 0C, none
indicated the correct answer. Furthermore, when all of the students were asked to explain everyday
phenomena related to evaporation (e.g., “when pure water in an open container at 25 0C is cooled to 10 0C,
what will happen to the level of water?”) only David, gave the correct explanation:
103
David: I guess if it’s open then some of it would evaporate. So I guess it goes down a little bit because it
evaporates. So if you have an open container it would evaporate and it would be a little bit lower, some
of the water would leave the container (3.1.3).
The majority of students made statements we categorized as partial understanding of the evaporation concept.
One area of misunderstanding for these students was that they indicated water would only evaporate at or above
100 0C. However, when students were asked the everyday phenomenon question - “after you wash your laundry
and leave it out to dry, what happens to water” – all indicated that water could evaporate at temperatures less
than 100 0C. The students failed to recognize this inconsistency in their explanations.
Students’ Conceptions of Condensation
All students’ conceptions of condensation were categorized as partial understanding. Although the students
knew that condensation involved a phase change from gas to liquid, none were able to identify the
condensation point for water on a phase change graph correctly. Most of them thought that condensation
could only occur after evaporation.
When the students were asked to define condensation, they also indicated a lack of understanding about
condensation in terms of the particulate nature of matter. For example, Lisa thought that hydrogen and
oxygen gases would come together when water condensed. She stated: “…particles, and they stack
together and they are colder and then it gets hotter and they break up and then when they are close to the
colder thing, again they come back together (4.1.2).”
The above excerpt also indicated that Lisa had conflicting ideas in relating heat and temperature to her
understanding of condensation. She indicated there must be an abrupt change in temperature for
condensation to occur. In addition, it was found that most of the students did not differentiate between
steam and water vapor. They thought steam and water vapor were the same as indicated below:
Interviewer: How can you describe condensation of water?
Kate: There was steam. They were moving faster and as it meets something a little bit colder than so it
becomes water and the particles aren’t moving as fast, they’re slowing down a little bit (4.1.1).
When students were also asked to explain everyday the phenomena - “a cold beverage is taken out of the
refrigerator. After a few minutes water droplets form on the outer surface of the bottle. Where do these
droplets come from?” - their responses were highly varied and involved explanation that included water
vapor, air, hydrogen and oxygen gas, and ‘I don’t know’.
None of the students could relate the notion of saturated vapor across different formal or everyday
contexts. They thought that condensation was caused by the decrease in temperature without considering
saturated vapor concept. For example, the following excerpt shows that David had confusion regarding the
condensation of water in a closed system:
Interviewer: At room temperature, there is a tightly capped plastic bottle half-filled with water. If this
bottle is left for several days, you can see many tiny water droplets appear on the lid of the bottle.
Where do these water droplets come from?
David: The water in there evaporates and then condenses on the inside the cap in the plastic. So water
evaporates and then condenses on the inside of, the droplets came from what’s in the container (4.1.3).
Students’ Conceptions of Boiling
The students had partial understandings of boiling in terms of the particulate nature of matter. One student
with partial understanding, Kate, stated: “The particles are moving extremely fast and there are little air
bubbles that are often there. They are warming up so they are moving faster because they are boiling
(5.1.1).”
When asked to define boiling, most students indicated that when boiling, the particles would change phase
and break apart. This is consistent with what they said for evaporation earlier. When asked an everyday
question regarding boiling: “an amount of water is boiling, you see bubbles coming from the boiling water.
What do you think these bubbles are made of? - students’ responses for this question included impurities,
air, and hydrogen and oxygen gas.
When the students were posed another everyday phenomenon related to boiling, they indicated their
confusion between steam and vapor:
Interviewer: When water is boiling in a pan on a stove, you see a white fog coming out and rising from
the pan. What do you think the white fog is?
Aaron: Steam (5.1.3).
Interviewer: What do you mean by steam?
Aaron: Gaseous water.
Students’ Conceptions of Vapor Pressure
Only one student, David, had partial understanding of vapor pressure in terms of the particulate nature of
matter. Although his definition of vapor pressure below reflects sound understanding of the particulate
nature of matter, his response was categorized as partial understanding of vapor pressure since he thought
that vapor pressure depended on surface area. He said, “The gas molecules of vapor are colliding and
hitting the sides of the container that they’re in so the greater the temperature the more times they collide
and hit other things in a certain time frame (6.1).”
The other students showed no understanding of vapor pressure. For example, when Martha was asked to define
vapor pressure, she stated: “The amount of vapor that a container is able to hold (6.2)”.
Conclusions and Implications
This study highlighted a series of difficulties students had across seve ral fundamental concepts in
chemistry. Evidence from the interview data presented above indicated that when students had limited
understanding of the particulate nature of matter, they had difficulties in explaining the concepts of states
of matter, melting, evaporation, condensation, boiling and vapor pressure. For example, most of the
students thought that when a substance changed phase, bonds within a molecule were broken, that when
water boiled or evaporated, water molecules would break apart into hydro gen and oxygen gases, and that
hydrogen and oxygen gases come together when water condensed. All of these misconceptions indicate a
limited ability to invoke a component of the particulate nature of matter, namely the structure and bonding
of substances. Similar to the findings of this study, Johnson (1998a, b) indicated that students had
difficulty in understanding evaporation and condensation compared to understanding melting and freezing
since changes involving the gaseous state were more problematic for students. In addition, Johnson
(1998b) also cited the importance of particulate ideas in understanding the nature of bubbles in boiling
water. Likewise, Bar and Travis (1991) claimed that boiling preceded the understanding of evaporation.
However, Johnson (1998a) reported that although the mist rising from boiling water was helpful for
students to understand that water was leaving, it did not mean that students understood the state change
from liquid to gas in terms of what a gas was. In our study, David too did not understand boiling
completely, although he had a sound understanding of evaporation.
In addition, the students held inconsistencies when linking theoretical principles related to the
aforementioned concepts with everyday phenomena. For example, when the students were describing
evaporation, they indicated that water would only evaporate at or above 100 0C (Paik et al., 2004).
However, they could not maintain this idea when they were posed the everyday question about laundry
drying. A possible reason for this inconsistency could be their everyday experiences. For example, when
students were asked what the white fog was coming out and rising from boiling water, most of the students
claimed that the white fog was water vapor although it was tiny water droplets (this is consistent with a
findings of Johnson, 1998b). Since they experience this phenomenon in their everyday life, they might
think that evaporation could only occur at or above 100 0C.
This study showed that although students were taught the fundamental concepts investigated in this study,
they still do not have deep understanding of these concepts, their relationships to one another, nor do they
consistently invoke their understanding of the particulate nature of matter when explaining che mistry
concepts. Teaching and learning chemistry requires representations at the macroscopic, submicroscopic,
and symbolic levels (Johnstone, 1993; Gilbert and Treagust, 2009). Many students have difficulties in
relating and making transitions among these three perspectives (De Jong and Taber, 2007). In this study,
evidence from our interviews indicated that most students could not make transitions among these
perspectives. For example, when Martha was asked to draw a phase change graph and to explain where
105
evaporation occurred, she showed that evaporation occurred only at and above 100 0C for water. However,
when she was asked an everyday phenomenon like “after you wash your laundry and leave it for drying,
what happens to water”, she answered that water would evaporate, even the temperature was below 100 0C.
Furthermore, it was seen that she had difficulty in understanding submicroscopic perspective since she
thought that hydrogen bonding occurred within molecules when explaining evaporation in terms of
particulate nature of matter.
Some practical implications from this study are that teachers should expect students to link the concepts
they are learning at multiple levels of representation. Also, the ability of students to explain everyday
phenomena with a microscopic level of detail should be emphasized. Curriculum developers should also
integrate related topics and disciplines such as the particulate nature of matter, saturated vapor, heat and
temperature, and conservation of matter in logical ways to support better understanding of these
fundamental topics. Metaconceptual teaching activities such as poster drawing, journal writing, group
discussion, and class discussion could be helpful for students to connect the aforementioned concepts.
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Appendix. Sample interview questions
1.
How would you describe the difference between solids, liquids and gases?
 Why do solids stay the same shape while liquids and gases do not?
 How can you draw the picture of solids, liquids and gases in terms of the particles that make up each?
 How do the motion of particles in solids, liquids and gases compare?
2.
In a room, there is an open plastic bottle half-filled with water. If this bottle were left for several days, what
would happen to the level of water in the bottle?
 After you wash your laundry and leave it for drying, what happens to water?
 If I spill water on the ground, what happens to water when the ground dries?
 When pure water in an open container at 25°C (77 °F) is left out to 10°C (50 0F) for a while, what
would happen to the level of water?
 What is evaporation?
3.
At room temperature, there is a tightly capped plastic bottle half-filled with water. If this bottle is left for
several days, you can see many tiny water droplets appear on the lid of the bottle. Where do these water
droplets come from?
 A bottle of liquid beverage which is cold enough is taken out of the refrigerator. When you wait for
some time, you see water droplets formed on the outer surface of the bottle. What do you think where
these droplets come from?
How can you draw the picture of your idea for the above question in terms of the particulate nature of
matter?
 When you hold your hand above the boiling water, your hand gets wet. How can you explain this?
 What is condensation?
4.
At a particular constant temperature, the following closed three systems contain the same type of liquid.
System I have 1 L volume and contain 50 mL liquid, system II has 2L volume and contain 50 mL liquid
and system III has 1 L volume and contain 25 mL volume. How can you compare the vapor pressures of
these three systems?

What is vapor pressure?
ISSN: 2147-611X
Analysis of Scientific Epistemological Beliefs of Eighth Graders
Nilgün Yenice*, Barış Özden
Adnan Menderes University
Abstract
The aim of this study is to determine the levels of scientific epistemological beliefs of 8th grade students. The
sample of the study consisted of 355 students. The data of the study were collected through the use of the Scale
of Scientific Epistemological Beliefs, which was developed by Elder (1999) and adapted into Turkish by Acat,
Tuken and Karadag (2010). Personal Data Form was also used to obtain demographic data about the
participants. In order to determine the levels of scientific epistemological beliefs of the students, the means and
standard deviations were calculated for each scale. The findings of the study suggest that scientific
epistemological beliefs of 8th grade school students are closer to sophisticated beliefs and mid-level.
Key words: Scientific Epistemological Beliefs, Science Education, Elementary Students.
Introduction
Today the students with the following qualities are needed: search for, question, analyze, develop relationships
between daily life and science topics, use scientific method to solve daily problems, look at the world using a
scientific approach, use and comprehend the basic science concepts, principles and theories (MNE, 2006).
Therefore, the course, science and technology, is very significant in this regard.
In recent years, different approaches towards scientific thinking and scientific knowledge have affected the
educational programs, leading to the development of new standards concerning science and scientific
knowledge and the characteristics of scientists (AAAS 1993, NRC 1996). Such effects also exist in Turkey. For
instance, revised program of the course, science and technology, in 2004 emphasizes science literacy and uses
constructivist approach to teaching as its basis (Tüken, 2010). Therefore, one of the major objectives of science
education is stated to produce students with science literacy.
Current program of the course, science and technology, defines science and technology literate persons as
follows (MNE, 2006): “Science and technology literates are those who are competent in accessing and using
knowledge, solving problems, making decisions over problems about science and technology taking into
consideration the potential risks, uses and available options and producing new information.”
Ayvacı and Nas (2010) argue that science and technology literate people can effectively use scientific concepts
and have information about the nature of scientific knowledge. Furthermore, they are informed about the
qualities of scientific knowledge.
Comprehending the nature of science and scientific knowledge is one of the significant characteristics of science
and technology literacy. The term the nature of science has been defined in various ways. For instance,
Lederman (1992) defines it as the values and assumptions in the nature of science. Kıray (2010), on the other
hand, analyses the nature of science under four headings as follows:

*
Source of the scientific knowledge: Many scholars and philosophers developed various views
concerning the source of scientific knowledge (Kıray, 2010). Scientific knowledge has been produced
through observations and inferences, but imagine and creativity also played a role in this production.
Scientific knowledge “is produced partly by imagination and inferences.” (Lederman, 1999). It is also
Corresponding Author: Nilgün Yenice, nyenice@gmail.com
108 Yenice & Özden



reported that socio-cultural environment also affect the scientific knowledge (Akerson, Abd-El-Khalick
& Lederman, 1999).
Degree of accuracy in the scientific knowledge: In regard to degree of accuracy of the scientific
knowledge, various views were offered (Kıray, 2010). The commonest approach to the scientific
knowledge is that scientific knowledge is not the one that is absolute. Because the scientific knowledge
is subject to modification through observations. The new findings and socio-cultural characteristics
may also lead to changes in the scientific knowledge (Lederman, Abd-El-Khalick, Bell and Schwartz,
2002). Therefore, although the scientific knowledge is reliable and can remain valid for a long period
of time, it is not absolute truth and certain.
Advances in the scientific knowledge: There are main approaches to the development in the scientific
knowledge; evolutionary and revolutionary. Evolutionary approach suggests that each new knowledge
is based on the previous ones. The latter approach, on the other hand, argues that each new knowledge
is produced bu falsifying the previous ones. Therefore, the philosophy of science that one adopts
determines which approach is followed. For those who follow the positivist science philosophy,
scientific knowledge develops through verification and is based on the previous knowledge. Kuhn, on
the other hand, suggests that advances in science would occur through revolutions (Kuhn, 1957). On
the contrary, Lakatos argues that science advances through evolutions. Science can be stated to be open
to both revolutions and evolutions (Kıray, 2010).
Consistency and validity of the scientific knowledge: In order to show the consistency and validity of
the scientific knowledge, there are many ways. For instance, verification, documentation, testing,
description, definition and falsifying are all used to show that the scientific knowledge is consistent and
valid (Sönmez, 2008). Scientific theories are well-organized and well-backed accounts of a
phenomenon. Scientific laws, on the other hand, are the descriptions of the relations observed between
the events or phenomena observed. For instance, Boyle law (1670) describes the relationship between
gas pressure and volume, the theory of kinetic molecular (1850) provides the reasons for this
relationship. Both theories and laws are subject to modification (Irez and Turgut, 2008). Therefore,
although both laws and theories are supported by good evidences, their validity is limited.
Studies on the nature of the scientific knowledge and science mostly include the scholars philopsophy of science
that ranges from positivist/realist/traditionalist to post-positivist/postmodern (Deryakulu and Bıkmaz, 2003;
Kaplan, 2006; Meral and Çolak, 2009; Terzi, 2005; Tsai, 1998; Turgut, 2009; cited in Tüken, 2010).
Constructivist approach that was resulted from the post-positivist philosophy of science states that knowledge is
not an independent entity, out of individuals; instead, it is context-based and individual (Yurdakul, 2005). The
constructivist approach emphasized the questions of what is knowledge? and how it is produced? As a reflection
of this approach, the scientific knowledge is also expressed through other terms such as “epistemological view”
and “epistemological belief” (Çoban and Ergin, 2008).
Scientific epistemological beliefs involve the individual philosophy over reliable and valid scientific knowledge,
their production and share (Deryakulu and Bıkmaz, 2003). Students’ epistemological beliefs govern their
attempts to understand the production and evaluation of the scientific knowledge, to learn the scientific concepts
and to understand the nature of science (Elder, 1999; Tsai, 1998, 1999, 2000). There are various scales used to
describe the students’ epistemological beliefs. For instance, Schommer (1990) developed the scale of multidimensional epistemological beliefs and suggested five dimensions of epistemological beliefs. Dimensions
included in the scale are as follows: i) Inborn ability, (ii) rapid learning, (iii) simple knowledge and (iv) absolute
knowledge. The dimension of inborn ability states that the ability to learn is fixed. The second dimension, rapid
learning, includes the fact that either learning takes place in a short period of time or it does not occur. The next
dimension, simple knowledge, involves the belief that knowledge is consisted of both independent parts and
interrelated concepts. The dimension of absolute knowledge is composed of the belief that knowledge is
absolute (Schommer, 1990). Çoban and Ergin (2008) also developed a scale concerning epistemological beliefs
with a sample of 505 students. This scale includes 16 items under three dimensions as follows: (i) Scientific
knowledge is closed, (ii) scientific knowledge is justifiable and (iii) scientific knowledge can be modified.
In recent years, studies in which students’ epistemological beliefs are analysed in relation to certain variables
become common. The epistemological beliefs of the students were analysed in relation to the following
variables: academic achievement (Schommer, 1990, 1993; Tolhurts, 2007), age (Schommer, 1998), the strategy
used (Cano, 2005; Chan, 2003; Holschuh, 1998; Tsai, 1998), culture (Chan & Elliott, 2002; Youn, 2000), and
gender and socio-economic status (Özkal, Tekkaya, Sungur, Çakıroğlu & Çakıroğlu, 2011). In Turkey, the
epistemological beliefs of undergraduate students and student teachers are analysed in various studies (For
instance, Akpınar, Dönder & Tan, 2010; Ayvacı & Nas, 2011; Eroğlu & Güven, 2006; Gürol, Altunbaş &
109
Karaaslan, 2010; Kaplan, 2006; Kaygın, Baş, Kanbolat & İneç, 2010; Kaynar, Tekkaya & Çakıroğlu, 2009;
Kızılgüneş, Tekkaya & Sungur, 2009; Meral & Çolak, 2009; Özşaker, Canpolat & Yıldız, 2011; Terzi, 2005).
On the other hand, a few studies have been carried out to analyse the epistemological beliefs of basic education
students in relation to certain variables (Boz, Aydemir & Aydemir, 2011; Kurt, 2009; Özkal et. al., 2011; Topçu
& Yılmaz-Tüzün, 2009; Tüken, 2010; Yenice, 2010). For instance, Boz, Aydemir & Aydemir (2011) identified
the epistemological beliefs of the fourth, sixth and eighth graders and concluded that their epistemological
beliefs significantly vary based on grade level and gender. Tüken, (2010) determined the epistemological beliefs
of rural and urban eighth grade students and found that the epistemological beliefs of the students significantly
differ based on certain variables
It is thought that in order to reach the objectives set by the Ministry of National Education (2006), students
should comprehend the nature of the scientific knowledge, its limitations and the production. Therefore, analysis
of the students’ epistemological beliefs regarding science education is significant. Thus, the aim of this study is
to identify the level of eighth grade students’ epistemological beliefs.
Statement of problem
Statement the research problem was defined “What is the level of eighth grade students’ epistemological
beliefs?
Method
Model and participants of the study
The study has a descriptive design and uses the scanning model. The participants of the study are randomly
selected eight-grade students attending public basic schools in Nazilli district of Aydın province during the
school year of 2011–2012. The number of the participants is 355, 170 males (47.9 %) and 185 females (52.1 %).
Data collection tools
In order to determine the level of the students’ epistemological beliefs, the “Scale of Scientific Epistemological
Beliefs”, which was developed by Elder (1999) and adapted into Turkish by Acat, Tüken and Karadağ (2010),
was used. Demographical form was also used to obtain information concerning the demographical
characteristics of the participants. The epistemological beliefs scale includes 25 items in the form of likert-type.
It is consisted of five sub-dimensions of authority and accuracy, the process of knowledge production, the
source of knowledge, reasoning and the changeability of knowledge. The Cronbach Alpha reliability coefficient
of the scale was found to be 0.82. Its reliability was analyzed again before its use in this current study and found
to be 0.75.
Analysis of data
Means (X) and standard deviations of the student scores in the subdimensions were calculated. The beliefs of
the students are labelled under three headings as follows: traditional (underdeveloped) beliefs for those with the
score from 1.0 to 2.5; mixed (medium level) beliefs for those with the score from 2.6 to 3.5 and developed
(contemporary) beliefs for those with the score from 3.6 to 5.0. For the subdimensions of authority and
accuracy, and the source of the knowledge, higher means refer to traditional beliefs (Tüken, 2010).
Findings
The answers of the students to each item in the subdimensions of the scale were analyzed. Table 1 provides the
mean scores and standard deviations in regard to the items included in the subdimension of Authority and
Accuracy.
110 Yenice & Özden
Table 1. Mean scores and standard deviations in regard to the items included in the subdimension of Authority
and Accuracy
Subdimension
Items
N
Mean
SD
1. In science, all questions have only one correct answer.
355
3.20
1.40
5. Scientists know almost everything about science, so there
355
2.08
1.32
is nothing new to be known.
12. Whatever teachers say in the courses are right.
355
2.63
1.26
15. The findings of an experiment are the sole truth about the
355
2.74
1.28
phenomenon at hand.
16. Everybody should believe in what scientists says.
355
2.14
1.23
Authority and
20. Only scientists know the truth in science.
355
2.45
1.33
Accuracy
23. Scientists have the same ideas about the truth in science.
355
2.44
1.27
24. Scientists never say “maybe”, because they always know
the truth.
355
2.49
1.31
25. Teachers and scientists always express scientific views.
355
2.27
1.35
Means and standard deviation of the scores
355
2.49
.86
As seen in the Table, mean score of the students in the subdimension of Authority and Accuracy is 2.49.
Therefore, the students’ beliefs in regard to this subdimension are developed. On the other hand, the students
have traditional beliefs about the following item in this subdimension: “In science, all questions have only one
correct answer.”
Table 2 provides the mean scores and standard deviations in regard to the items included in the subdimension of
the process of knowledge production.
Table 2. Mean scores and standard deviations in regard to the items included in the subdimension of the process
of knowledge production
Subdimension
Items
N
Mean
SD
3. The most significant role of scientific study is to reveal
355
1.87
1.05
the truth.
4. The most important role of science is to carry out
experiments to obtain new ideas about the functioning of 355
4.13
.89
the universe or objects.
Process of
7. If scientists work hard, they can answer all questions.
355
2.07
1.09
8. More than one experiment should be done to be sure
Knowledge
355
4.47
.78
about the discovery.
Production
11. Experiments are good ways to know whether or not
355
4.23
.95
anything is true.
18. Correct answers are based on the findings obtained
355
4.21
.95
from many experiments.
Means and standard deviation
355
3.50
.38
Table 2 shows that the mean score of the students in the second subdimension, the process of knowledge
production, is 3.50., however, the third and seventh items in this subdimension are reversely coded. The mean
scores for these items are 1.87 and 2.07, respectively. Therefore, the students appear to have traditional or
underdeveloped beliefs. However, regard to other items, it can be argued that the students have developed
beliefs.
the source of knowledge.
As seen Table 3, the mean score of the students in the subdimension of the source of the knowledge is 2.97.
therefore, they have mixed beliefs in regard to this subdimension. However, in regard to the item, “We should
be sure about what we read in the scientific books.”, the students appear to have traditional belief.
111
Table 3. Mean scores and standard deviations in regard to the items included in the subdimension of the source
of knowledge
Subdimension
items
N
Mean
SD
6. Scientific knowledge is always correct.
355
3.23
1.20
10. We have to believe in what we read in the scientific
355
2.41
1.21
books.
Source of
13. We should be sure about what we read in the scientific
355
3.25
1.12
books.
Knowledge
14. We should believe in what our teacher say about
355
3.00
1.29
science, although we cannot fully understand.
Mean and standard deviation
355
2.97
.87
reasoning.
Table 4. Mean scores and standard deviations in regard to the items included in the subdimension of reasoning
Subdimension
Items
N
Mean
SD
2. The views about experiments are resulted from curiosity and
355
4.36
.74
thinking about events and facts.
21. Before doing an experiment, one should be informed about
355
4.51
.82
it.
Reasoning
22. Curiosity over the reasons for events and facts is the best
355
4.30
.90
way to be informed about a scientific phenomenon.
Mean score and standard deviation
355
4.39
.60
The mean score of the students at the subdimension of reasoning is 4.39. Therefore, their beliefs in relation to
this subdimension are developed.
the changeability of knowledge.
Table 5. Mean scores and standard deviations in regard to the items included in the subdimension of the
changeability of knowledge
Subdimension
Items
N
Mean
SD
9. In science, views sometimes change.
355
3.98
.99
17. New discoveries lead to changes in the views of
355
4.14
.97
scientists about truth in science.
Changeability of
19. Scientists change their views about the truth in
Knowledge
355
3.89
.95
science.
Mean score and standard deviation
355
4.00
.69
The mean score of the students at the subdimension of changeability of the knowledge is 4.00, suggesting that
the students have higher than mixed beliefs.
Discussion and Conclusion
The findings of the study indicate that the students participated in the study have developed epistemological
beliefs in relation to three subdimensions; Authority and Accuracy, Reasoning and Changeability of the
Knowledge. However, it is also found that their beliefs are underdeveloped in regard to the remaining two
subdimensions; the Source of the Knowledge and the Process of the Knowledge Production.
At the subdimension of Authority and Accuracy, there are beliefs about science and the source of the scientific
knowledge, absoluteness of the knowledge and outside sources of it. As stated above, the students participated
in the study have developed epistemological beliefs in this regard. Therefore, they believe that science has a
112 Yenice & Özden
nature that is evolving and that scientific knowledge is based on authority. However, they are also found to
believe that there is only one correct answer. Their belief in single correct answer is certainly traditional. Songer
and Linn (1991) argue that students compare the findings of different scientists and believe that scientists
working on the same experiment may reach different conclusions and that scientist makes use of evidence to
solve the disputes. In the current study, it is also found that students have developed beliefs in regard to the fact
that scientists may not always reach the correct answer and that they cannot agree on a single truth.
Furthermore, the level of the students’ belief is mixed regarding the fact that the findings of an experiment are
the single truth about the phenomenon at hand. Therefore, it is safe to argue that students do not have developed
understanding of science. Tüken (2010) found that students have generally mixed beliefs in regard to the
subdimension of Authority and Accuracy. It was also found that students believe in single correct answer, the
evolving nature of science and the correctness of the findings obtained from experiments. Therefore, these
findings support those of the current study.
The subdimension of the Process of the Knowledge Production includes the methodological characteristics of
science. The students are found to have mixed beliefs in regard to this subdimension. Mean scores of the
students at this subdimension suggest that they understand the empirical quality of science. However, students
also believe that more than one experiment is needed to reach the correct answer. Therefore, it can be argued
that the students’ related epistemological beliefs are developed. In parallel to this finding, Carey, Evans, Honda,
Jay and Unger (1989) found that majority of the seventh grade students understand the fact that scientific
research is directed with certain views and thoughts and that experiment refer to testing of these views. Muşlu
(2008) found that students attach importance to experiments and observations. Tüken (2010) also found that the
beliefs of the students at the subdimension of the Process of the Knowledge Production are mixed and that they
attach significance to experiments. Therefore, the present finding is consistent with that of Tüken’s study.
However, in regard to two items in this subdimension students are found to have traditional beliefs. The students
appear to focus on the results of the experiments and correct answers. Therefore, it can be argued that they do
not have well developed beliefs about the nature of science. The reason for this may be in-class practices of
teachers. Tsai (2003) argues that those teachers with positivist approach to science regard experiments as a way
to verify the scientific knowledge.
The beliefs of the students at the subdimension of the source of the knowledge are between traditional and
developed. They are found to view books and teachers as the source of knowledge and to believe that scientific
knowledge is always correct. It is further found that students accept what they read in the books as correct
knowledge. There are previous findings that are consistent with this finding (Roth and Roychoudhury, 1993;
Saunders, 1998; Tüken, 2010; Boz et. al., 2011; Savaş, 2011). Saunders (1998) found that students strongly
believe in that knowledge taken from outside sources and that they have mixed epistemological beliefs. Tüken
(2010) found that students have generally mixed beliefs in regard to this subdimension and that students mostly
believe the correctness of the scientific knowledge. Similarly, Boz et. al. (2011) concluded in the study with a
sample of the fourth, sixth and eighth grade students that they have underdeveloped epistemological beliefs
regarding the certainty and source of the scientific knowledge. However, Lehrer, Schauble and Lucas (2008)
suggest that in a classroom environment in which students are active participants of the learning process,
students focus on their own activities. Therefore, it can be stated that if students are made active participants of
the learning process, they will less regard teachers as an authority.
At the subdimension of reasoning in which curiosity and prior knowledge are emphasized, the students are
found to have developed beliefs. Students believe that curiosity leads to be informed about scientific
phenomenon and prior knowledge is needed to make experiments. Therefore, the student beliefs at this
subdimension are developed. Some other findings support this finding of the study (Smith, Maclin, Houghton
and Hennessey, 2000; Tsai, 2000; Tüken, 2010). Tüken (2010) also found that the student beliefs regarding this
dimension are generally developed.
In regard to the subdimension of the changeability of the knowledge, the students are also found to have
developed beliefs. In other words, students believe that the views of scientists may change and that new
discovery and inventions lead to changes in the views about the truth in science. Therefore, students seem to
have those beliefs very close to scientific approach. This finding is supported by the findings of some previous
studies (Muşlu, 2008; Kurt, 2009; Tüken, 2010; Savaş, 2011). Muşlu (2008) also found that students believe
that the views of scientists may change. Similarly, Tüken (2010) also concluded that student beliefs at this
subdimension are developed. This finding is also consistent with that of Smith, Maclin, Houghton and
Hennessey (2000) in that the students in constructivist classroom settings are aware of the changeability of
scientific views.
113
In conclusion, the scientific epistemological beliefs of the eighth grade students participated in the study are
either mixed or developed. On the other hand, classroom activities can be developed to reduce the student
beliefs regarding the fact that the scientific knowledge is always correct. Additionally, since students seen to
focus on the results of the scientific research, necessary classroom activities can be employed to show them that
methodology is also an important part of scientific endeavor. The sample of the study included the eighth grade
students and their epistemological beliefs were quantitatively analyzed. Therefore, the epistemological beliefs of
other students at different educational levels can be analyzed, following a qualitative research.
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ISSN: 2147-611X
Integrated Programs for Science and Mathematics: Review of Related
Literature
Kürşat Kurt1*, Mustafa Pehlivan2
1
Kökpınar Primary School
2
Abstract
This study presents a review of literature on the integration of science and mathematics, focusing on the
dominant trends in the related studies. Majority of the studies conclude that the concept of the integration
between science and mathematics is still vogue. On the other hand, there are various methods, techniques and
models to achieve this integration. Although these distinct models, methods and techniques are employed in the
integration efforts, the results are the same. The integration improves the student achievement. However, there
are also some barriers in these efforts. One of such problems is the lack of teachers’ and pre-service teachers’
content knowledge and pedagogical content knowledge. The other deficiency is about the fact that teachers do
not have sufficient experience for delivering integration programs since their pre-service education do not
provide them with the opportunity to use it.
Key words: Integration, Integration of Science and Mathematics, Integrated Curriculum.
Introduction
Interdisciplinary practices are emphasized increasingly in recent years (Rutherford & Ahlgren, 1990; NCTM,
2000; MEB, 2004; Kıray & Kaptan, 2012). One of such relations is between science and mathematics. These
two fields are similar and interrelated, making them more suitable for integrated programs. The relations
between these two fields are natural, rather than forced. The content knowledge of the fields is resulted from
their interaction or cooperation. Additionally, the relationship of both fields has a long period of time (Hurley,
2001).
Integration of science and mathematics has a long history. However, early integration lacked instructional
dimension; instead, it attempted to make use of mathematics. The acceptance of education as a discipline by
positivist philosopher started at the beginning of the 20th century and then the bonds between science and
mathematics attracted the attention of educators. Various studies were made with regard to the integration
between these two disciplines. Later, instead of the term integration, other terms began to be used such as
blended, connected, correlated, core, cooperation, coordinated, cross-disciplinary, fused, immersed, integrated,
integrative, interactions, interdependent, interdisciplinary, linked, multidisciplinary, nested, networked,
thematic, threaded, trans-disciplinary, sequenced, shared, unified and webbed (Berlin, 1991; Berlin & White,
1994; Lederman & Niess, 1997; Mathison & Freeman, 1997; Gehrke, 1998; Czerniak, 2007, Deveci, 2010;
Kıray, 2012). Some of these terms are used interchangeable. However, some of these terms have distinct
meanings. Kıray (2012) argues that if these terms refer to research in science and mathematics, then they should
be grouped under the heading of “integrated science and mathematics”. On the other hand, such variety of terms
has led to different definitions for the integrated science and mathematics.
Definitions of the integrated science and mathematics
Berlin and White (1992) defined the integrated science and mathematics as the mixture of two courses in a way
that they cannot be separated each other. They argued that this integration can be achieved through the use of
methods in science in the other course and vice versa. Lederman and Niess (1997, 1998), Roebuck and Warden
*
Corresponding Author: Kürşat Kurt, kursatkurt33@gmail.com
117
(1998) and Huntly (1999) defined this integration as a blended case. Lehman (1994), Frykholm and Glasson
(2005) and Furner and Kumar (2007) regarded the integrated science and math as the expansion of two
disciplines. Kıray (2012) suggests that after identifying the objectives of the integrated science and math, all
possible interactions between two disciplines may be part of this integration ranging from simple connections to
blended practice. A similar approach was adopted by Berlin and Lee (2005). In addition to various definitions,
there are also various methods and models with regard to the integrated science and math.
Methods and models with regard to the integrated science and math
Berlin and White (1994) developed a model called BWISM that is the first one in the field. BWISM is add-up of
six steps as follows: 1- ways of learning, emphasizing the active participation of students in the learning
process, 2- ways of knowing, using both induction and deduction and qualitative and quantitative data to reach
new information, 3- process and thinking skills, recognition that the math skills are also that of science and that
scientific process skills are also employed in mathematics, 4- content/conceptual knowledge, recognition that
the integrated science and math refers to similar concepts, 5- attitudes and perceptions, emphasizing that some
attitudes, values and perceptions are common in mathematics and science and 6- teaching strategies,
emphasizing that there are methods that can be used for the instruction of science and mathematics.
Davison, Miller and Metheny (1995) argued that the integrated science and math includes five principles as
follows: 1- discipline specific integration in which two or more subcategories of science and mathematics are
combined through an instructional activity, 2- content specific integration, in which some objectives from the
existing objectives of the science and math programs are chosen and combined, 3- process integration, in which
skills of science and math are combined, 4- methodological integration, in which teaching-learning techniques,
methods and strategies of discovery and learning cycle are employed, and 5- thematic integration, in which
science and mathematics are integrated around a theme. Therefore, Davison, Miller and Metheny (1995)
suggested that the use of at least of the above options refers to as the integrated science and math.
Lonning and DeFranco (1997) divided the interaction of science and math into five subcategories: 1independent mathematics, which is the instruction of pure mathematics, 2- mathematics focus, in which the
concepts of science are employed to support the math concepts, 3- balanced mathematics and science, in which
the concepts and activities of science and math are integrated 4- science focus, in which the concepts of math
are employed to support the science concepts, 5- independent science, which is the instruction of pure science.
A similar approach was also adopted by Huntly (1998). Likewise, she developed five different interaction
between science and math as follows: 1- math for the sake of math, referring to as math course, 2- math with
science, referring to as the use of science content or methods in the math problems, 3- math and science,
referring to as the use of both content and methods of science and math together to give explanations, 4- science
with math, referring to as the use of math to solve science problems, and 5- science for the sake of science,
referring to as science course.
Hurley (2001), on the other hand, developed five distinct types of integration as follows: 1- sequenced
integration, involves the sequential instruction of science and math, 2- partial integration, involves both
combined and separate instruction of science and math, 3- enhanced integration, involves the use of either of the
disciplines as a major one and the other as a dependent one, 4- total integration, involves the simultaneous and
equal instruction of science and math and 5- parallel integration, involves separate but simultaneous instruction
of science and mathematics. The types developed by Hurley are independent. Each emphasized a distinct
integration option.
Kıray (2012) focused on the development of an instructional program for the integrated science and math. He
suggested that the content knowledge of science and math can be organized and the related objectives can be
identified. However, he also argued that the integration of science and math cannot be always possible or
suitable. Based on these suggestions, he developed a model called balanced model for the integrated science and
math. In this model, the organization of the content knowledge is at the center and it is combined with skills, the
process of teaching and learning, affective characteristics, measurement and assessment. Kıray’s (2012)
balanced model involves five steps as follows;
1- Content knowledge: The content knowledge in this model is similar to the models developed by Lonning and
DeFranco (1997) and Huntly (1998). These scholars also refer to balance model in their accounts of the
integrated science and math, suggesting that content of science and math should be represented equally in the
program for the integrated science and math. There are seven dimensions with regard to the content knowledge:
118 Kurt & Pehlivan
a) Mathematics: At this dimension, only the objectives of the math course is taken into consideration, b) Mathcentered science-assisted integration: Either content knowledge or the objectives of science is included in the
objectives determined for math, c) Math-intensive science-connected integration: Math is much more
emphasized in the topics in which both science and math are taught and the objectives identified for math are
correlated with science, d) Total integration: The objectives are developed in what that science and math are
completely blended, e) Science-intensive mathematics-connected integration: Science is much more emphasized
in the topics in which both science and math are taught and the objectives identified for science are correlated
with math, f) Science-centered mathematics-assisted integration: Either content knowledge or the objectives of
math is included in the objectives determined for science, and g) Science: At this dimension, only the objectives
of the science course is taken into consideration. These dimensions are also called the types of the integrated
science and math.
2- Skills: The step of skills states that math skills such as problem solving, reasoning, communication,
connections and representation are also common skills for science in all types of the integrated science and
math. Process skills of science are divided into two subcategories: Common skills that are regarded as primary
and common skills that are regarded as secondary. Primary common skills are connections, problem solving,
reasoning, reaching conclusions and interpreting, organizing the data and formulating models, comparisonclassification, measurement, collecting information and data, estimation, making inference, prediction,
recording the data, communication, observation. Secondary common skills are those of math and science. Of
them, those skills regarded as primary are used the types of Science, Mathematics, Science-centered
mathematics-assisted integration and Mathematics-centered science-assisted integration. Secondary common
skills are used at the remaining types, namely Science-intensive mathematics-connected integration,
Mathematics-intensive science-connected integration and Total integration.
3- The processes of teaching and learning: It predicts that both science and math are taught and learned based
on the constructivist approach. It recommends inquiry-based processes for both courses.
4- Affective characteristics: In the model, it is stated that when the program for the integrated science and math
begins to be used, the affective characteristics defined separately for each will affect student achievement. Such
an effect may be mostly seen at the integration type of total integration. It will be less in the following types in
which integration is limited to simple connections, such as mathematics-centered science-assisted integration
and science-centered mathematics-assisted integration.
5- Measurement and assessment: At this step, the objectives of the program should be consistent with the
measurement and assessment attempts. The attempts of measurement and assessment will be shaped by the
integration type preferred. However, both outcome and process should be assessed.
Research about the integrated science and math
Berlin and Lee (2005) compared the number of published articles on the integrated science and math in two
periods, namely the period of 1901-1989 and of 1990-2001. They found that a total of 401 researches were
published in the period of 1901-1989 and that 449 research was published in the period of 1990-2001. The
findings clearly show that the studies on the integrated science and math have increased in the last 20 years.
Bütüner and Uzun (2011) found that science teachers have complaints about the lack of connections between
science and math in the existing programs of science and technology and math courses. They also stated that
they have to deal with math topics before certain science units such as physical science since prior math
knowledge is required for the learning of such topics. Watanabe and Huntly (1998) found that science teachers
mostly regard math as a tool for science or the language of science.
Kıray, Gök, Çalışkan and Kaptan (2008) concluded that math teachers are not aware of the necessity of math
knowledge for the course of science and technology. However, both science teachers and math teachers believe
that student achievement in either of these courses affects the achievement in other course. Frykholm and
Glasson (2005) found that pre-service teachers are aware of the blending nature of science and math and of the
significance of the connections between these two courses. However, the participants were found to have the
fear of using the program for the integrated science and math. The reasons for this fear included the lack of
teaching experience and deficiency of content knowledge.
119
Lehman (1994) found that more than half of the teachers participated in the study did not believe that they had
necessary background knowledge to use the program for the integrated science and math. Similarly, Başkan,
Alev and Karal (2010) concluded that although the teachers have positive attitudes about the integration of
science and math, they do not have necessary knowledge to integrate these two courses. Mason (1996) also
concluded that secondary teachers have deficit content knowledge and pedagogical content knowledge for the
other courses and do not know how to integrate the programs of such courses. Kıray and Kaptan (2012) also
reached a similar finding. The lack of necessary content knowledge and pedagogical content knowledge to
integrate science and mathematics is one of the most important barriers of the successful integration.
Huntly (1999) argued that although integrated science and mathematics courses are much more effective, the
success of the integration is based on the teachers’ content knowledge. Since the understanding of teachers with
regard to science and mathematics is limited, connections to be developed between science and mathematics
will also be limited. The teachers participated in Huntly’s study argued that the lack of instructional materials
and models for the programs for the integrated science and mathematics do not allow them to use this program.
Huntly further suggested that teachers’ pedagogical content knowledge should be improved in order to have
successful integration and teachers should know the objectives of the integrated program. Meisel (2005)
suggested that teachers should be offered in-service training concerning the integrated programs for science and
mathematics, since such training activities have positive effects on the implementation of the integrated
programs.
Kıray (2010) concluded that the integrated science and mathematics program should not involve all topics.
Instead, only the most suitable ones should be covered in the integrated program. The teachers participated in
Kıray’s study stated that they do not endorse the total integration, but they prefer those integration attempts in
which some knowledge and skills are transferred to the other course. All participants also argued that science
teachers do not know mathematics well and mathematics teachers do not know science well. Therefore, they
objected to integration attempts between these two courses. Additionally, each category of teachers seemed to
claim that their field is superior to the other field.
James, Lamb, Householder and Bailey (2000) found that mathematics teachers experience difficulties in the
implementation of the integrated program for science and mathematics. They also found that mathematics
teachers less tend to use the integrated program in contrast to science teachers. Berlin and White (2010)
concluded that student teachers are also not willing to use the integrated science and the math program due to its
difficulty.
Hurley (2001) analyzed 31 studies on student achievement using effect size. It was found that the integrated
science and mathematics has positive effect on student achievement in two courses, but its effects are much
higher in science. Ross and Hogaboam-Gray (1998) also found the integrated students are much more successful
than those in control group. Hill (2002) found the similar findings for the sixth grade students. Kaya, Akpınar
and Gökkurt (2006) also determined higher levels of achievement for the students who are given the integrated
instruction. Kıray (2010) evaluated the effects of the integrated science and math program on student
achievement. He found that the students of the integrated science and mathematics program could more easily
solve the problems in the category of “science-math”. However, in other categories there were no differences
between the integrated instruction group and the control group. Deveci (2010), on the other hand, found that the
program for the integration of science-centered mathematics-assisted did not lead to any significant difference in
terms of student achievement. However, the program contributed to the permanence of knowledge. Kıray and
Kaptan (2012) who tested the effects of science-centered mathematics-assisted integration program found that
those students in the integrated group were much more successful than those in the control group.
Conclusions and Implications
There are numerous studies indicating the significance of the interaction between science and math. However,
there is still uncertainty over how to reflect this interaction into the program and classroom environment. On the
other hand, the definition of the integrated science and math is not clear-cut. The efficiency of the integrated
science and math programs has not been evaluated extensively. Those studies dealing with the efficiency of
these programs employ distinct models or methods.
Emprical studies analyzing the efficiency of the integrated science and math program show that the integrated
programs have positive effects. However, these studies also document the deficiency in the implementation of
these programs. Studies on pre-service teachers show that the most significant barrier to implement the
120 Kurt & Pehlivan
integrated programs is the lack of teachers’ and pre-service teachers’ content knowledge and pedagogical
content knowledge. The lack of experience is cited as another barrier. Science teachers seem to need and be
volunteer to use the integrated science and mathematics programs.
Therefore, it can be suggested that the integrated programs should be developed for long period of time in order
to provide an effective program for the integrated science and mathematics. Additionally, teacher-training
programs should be reorganized in order to improve the content knowledge and the pedagogical content
knowledge of the pre-service teachers.
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ISSN: 2147-611X
Influence of Scientific Stories on Students Ideas about Science and
Scientists
Sinan Erten1*, S. Ahmet Kıray2, Betül Şen-Gümüş1
1
Hacettepe University
2
Abstract
This study was conducted to determine whether a lesson, in which context-based learning approach and
scientific stories were used, changed students' (aged 11-12) stereotypical images of science and scientists. Data
was collected from two separate sources: Interviews conducted with six students and Draw a Scientist Test
(DAST) document that was given to 80 students (before and after the intervention). In the study, context-based
learning approach with scientific stories was used as intervention after which a change in students’ ideas about
science and scientists was observed. At the end of the study, changes were observed in various categories of
stereotypical images of scientists, such as use laboratory tools (test tubes, glass bottles, magnifying glasses,
chemicals, etc.), use of technological appliances (computers, microscopes, telescopes, machines, robots, etc.),
scientists who study living things (plants, animals, humans), scientists who study inside a laboratory, scientists
who study outdoors (nature, space, etc.). At the same time changes in students’ understanding of nature of
science were observed. After the intervention, clues about student ideas such as, there is more than one scientific
method, there is no single criteria for doing science, scientists use their imagination in their studies, and
scientists’ studies are not limited to one field were observed. In the course of the study, student’s ideas about
science changed from a positivist philosophy toward a heuristic philosophy.
Key words: Scientists Images, Scientific Stories, Nature of Science, Context-based Learning
Introduction
School programs that are designed based on constructivist approach, which suggest students should discover
knowledge by following the methods of scientists, are common in contemporary education. Educators
emphasize inquiry-based learning based on this approach. With this type of educational approach, educators are
faced with questions such as “how scientists work?” and “is there a single way of doing science?” more often.
Guiding students to work like scientists change or reinforce their images of scientists. The Process of gaining
knowledge by doing experiments and observations, which was popularized by Galileo, in time, contributed to
the perception of science as an activity that is mainly done in laboratories. Together with positivist philosophy
and empiricism, this perception contributed to the image of science and method of doing science (Lederman,
1992; Tao, 2003). In the period after Einstein, assumptions about nature of science started to shatter and ideas
about science and scientific method have been replaced with new ones (Abd-El-Khalick & Lederman 2000).
The change in nature of science has brought about flexibility in the image of scientists in students’ minds.
Nature of Science and Scientist Image
The discussion on whether doing science or producing scientific knowledge has a method has been brought
about by the positivist philosophy. Aguste Comte, who established positivism in the 19th century, believed that
it is possible to know the natural world through methods of physics. In Comte’s time, methods of physics were
based on Galileo’s experimental approach. Carnap, a member of the logical positivists of the Vienna School,
took the issue a step further and claimed that the only science is physics and its related fields. Philosophers of
the Vienna School claimed that there is a method of doing science. They accepted experimental way of
*
Corresponding Author: Sinan Erten, serten@hacettepe.edu.tr
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obtaining knowledge in physics as the method of science. With positivist philosophy, it is accepted that science
has a method, which is the scientific method. Dewey, a pragmatist, explained the scientific method as a six level
problem solving method (Dewey, 1933; Hermanovicz, 1961; Kıray & İlik, 2011). These developments in
science and philosophy inevitably reflected in society through various means. Newspapers, magazines, and
television in the 19th and 20th century popularized scientific discoveries. Frequent news about these discoveries
created interests in society. Interest in new discoveries necessitated primary school programs to include
scientific inquiry in their programs.
From the middle of the 20th century, inclusion of all scientific process skills in science education programs has
become important. Today science process skills are one of the seven learning areas in Turkish science education
program (MEB, 2011). It can be argued that positivist and pragmatic philosophy in school programs created an
image of science as something being done in laboratories and scientists as the people who do science. However,
today, philosophers of science reject the idea that science has a definite method. As Einstein dethroned Newton
with a scientific revolution, increased discussions about science and scientific method took place.
Einstein's physics is not based on experiments done in laboratories but rather on thought experiments. The
situation shook the idea that science has a definite method, popularized by positivist and pragmatic philosophy.
Philosophies, such as heuristic philosophy after Einstein, accepted that the source of knowledge could be the
mind as well as the experiments. Because of this, the idea of science does not have a definite method have been
accepted more widely (Kiray, 2010). Feyarabend took it a step further by saying that defending a scientific
method is bigotry (Sonmez, 2008).
Scientific knowledge is not obtained only through experiments and observations. To some extent, it is obtained
as a result of all human imagination and inferences. There is a consensus among scientists that scientific
knowledge depends on observations and experiments, but not totally. Scientific knowledge can change, it is
subjective, and it includes social and cultural activities (Lederman, 1999; Ibanez-Orcajo ve MartinezAznarbanez, 2007). Science also has historical dimensions. Historical dimensions of science include how
scientists worked, how they discovered, what were the difficulties in their discoveries, how many scientists
worked on a discovery, why were they successful or unsuccessful (Aydoğan, 2008).
Today, changes in views on nature of science by the influence of history and philosophy of science, necessitates
a change in students’ images of scientists. The old image of science limited science to laboratories, shaped
image of scientists as male, middle aged with untidy appearance, wearing glasses, mostly working in a
laboratory with a lab coat and various technological equipment and glassware around, performing dangerous
experiments, unsocial with no interactions outside the laboratory (Dikmenli, 2010). Research shows that this
traditional view of scientists still exist in students’ minds (Mead and Metraux, 1957;Chambers, 1983; Rosenthal,
1993; She, 1998; Song & Kim, 1999; Finson, 2002; Rubin, Bar, & Cohen, 2003; Losh, Wilke, & Pop, 2008;
Fralick, Kearn, Thompson, & Lyons, 2009; Korkmaz & Kavak, 2010; Çakmakçı, Tosun, Turgut, Örenler,
Şengül, & Top, 2011). It is important to be aware of the sources of the stereotypical images of scientists in
students’ mind to expand and change these images in accordance with the changes in nature of science.
Research in this area showed that media that students interact with such as comics, novels, newspapers, movies,
television and other forms of mass media as well as bibliographic information about scientists in text books,
friends and family have influence on students’ images of scientists (She, 1995; Song & Kim, 1999; Jane, Fleer
& Gipps, 2007; Turkmen, 2008; Çakmakcı et al., 2011).
Currently to make students' stereotypical images of scientists more flexible and more compatible with the
current nature of science perspective, educators continue to try different techniques and strategies. Flick (1990)
found that inviting scientists to classroom is effective in changing students’ ideas about scientists. Çakmakcı et
al (2011) suggested four approaches, namely 1) “Using a Concept Cartoon to Present a Scientist’s Life”,
2)“Visiting Scientists”, 3)“A Scientist’s Visit to the School”, and 4)“A Presentation on Scientists’ Lives” are
effective in changing students ideas about scientists. Mason, Kahle, and Gardner (1991) investigated “a teacher
intervention program” that they developed to change high school students' stereotypical images of scientists.
Their program was applied to 549 high school students by 14 biology teachers. Half of all participants were
given strategies that included materials about role models, sexual equality and career information. When the
DAST data from their research was analyzed with chi-square test, they found that the students in the
experimental group have drawn more female scientist pictures than the ones in the control group. Reap, Cavallo,
and McWhirter (1994) designed a treatment for pre-service teachers that utilized inquiry strategies and learning
cycle. They found that the treatment was effective in decreasing pre-service teachers’ stereotypical science
images. Literature suggests that one of the effective methods for changing students’ stereotypical scientist
images is scientific stories.
124 Erten, Kıray & Şen-Gümüş
Science Stories and Scientist Image
Scientific stories are usually stories about scientists’ real lives and scientific phenomena and events. Scientific
phenomena and events that students have difficulty understanding may become easier to understand when given
with in a story. Because of this, scientific stories are occasionally given in textbooks. Milne (1998) has
separated scientific stories into four groups. 1– Heroic science stories: science hero single-handedly contributed
to the development of science. 2 – Discovery science stories: scientific information that was discovered as a
result of an accident or coincidence. 3–Declarative science stories: science stories that presents scientific
concepts or scientific processes objectively. 4– Politically correct science stories: stories that tell the
contribution of people from different cultural, sexual and ethnic backgrounds. There are many evidential
supports that using these story forms improve the value of teaching and learning (Martin & Brouwer, 1991;
Klassen, 2007; Klassen, 2010, Frisch, 2010).
In inquiry-based courses, where students see scientists’ successes and failures through stories, help them
identify themselves with scientists. Story becomes more meaningful when it is combined with students’ dreams
(Solomon, 2002). Especially biography type stories and documentaries about scientists’ lives are effective in
shaping students images of all scientists (Milne, 1998; Tao, 2003; Koch, 2005, Dagher & Ford, 2005). Because
of this, to identify students’ ideas about scientists or to change their ideas about scientist, scientific stories can
be utilized. Reis & Galvao (2004) have identified students’ concepts about scientists through science fiction
stories. In their other study, Reis and Galvao (2007) had high school students write fiction stories through which
they obtained their stereotypical images about scientists. Tao (2003) found that scientific stories influenced
students’ ideas about the nature of science significantly. Ermani (2010) claim that stories about DNA changed
students’ scientific ideas.
This study was focused on determining whether scientific stories used in a fifth grade science and technology
class for seven weeks were effective in changing students’ stereotypical images of science and scientists rather
than evaluating scientific stories. The following research questions were explored for this purpose:
1. What are students’ ideas about the materials that scientists use in their studies? How do scientific
stories may influence these ideas?
2. What are students’ ideas about scientists’ working conditions? How do scientific stories may change
these ideas?
3. How do scientific stories may influence students’ ideas about scientists working environments and
methods?
4. How do scientific stories may influence students’ ideas about doing science and accepting a study as
scientific?
Methods
Data Collection Instruments
We combined data from two sources: The DAST and individual interviews. To determine changes in students’
images of scientists and their perception of science DAST document was given to students at the beginning and
end of the treatment that was developed by Chambers (1983). Later six students, who had differences in the preand post-drawings, were interviewed.
Draw a scientist test (DAST)
DAST was developed by Chambers (1983) is an easy to use instrument. A piece of paper is given to students
and they're asked to picture a scientist (Fung 2002). DAST is not based on a verbal ability (Newton & Newton,
1992). The biggest advantage of it is that it does not require writing or reading skills. Because of this, it can
easily be used in all levels from preschool to university.
In his/her study, Chambers (1983) suggested seven standard indicators. DAST is still a valid document to
determine the stereotypical indicators about scientists that students still have today such as eyeglasses,
laboratory coat, facial growth of hair (beards, moustaches, sideburns, etc.), research symbols such as scientific
instruments and laboratory equipment, knowledge symbols such as books and file cabinets, technology products
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and relevant captions such as formulae, taxonomic classification. Other researchers in other countries also
found similar indicators after Chambers (Fung, 2002).
Interviews
Interviews were conducted with students who were in the experimental group and who had differences in their
pre-and post-drawings of scientists in categories such as use of laboratory tools, use of technological equipment,
scientists working in a laboratory environment, scientists working outdoors, scientists working on living things.
Interviews were utilized in two ways, i) to determine reasons for changes in students drawings, ii) to determine
whether changes in drawings reflect current understanding of nature of science and scientific method.
Reliability and Validity of Data Collection Instruments
To determine the reliability coefficient, the DAST documents were scored by three scorers based on five criteria
and the correlation coefficient between the first scorer and the others were determined to be 0.902 and 0.806
respectively.
The interview protocol and the questionnaire was reviewed by an academician , who had studies in nature of
science and philosophy of science, and a doctoral student, who studied nature of science in his Masters’ degree.
Necessary revisions were made based on these two experts’ opinions and the interview protocol and the
questionnaire were finalized based on their suggestions. Their opinions suggested that the data that will be
obtained from the interviews would serve the purpose of the study. Content validity for the interview
instruments was obtained this way.
Figure 1 and Figure 2 shows two examples of pre and post drawings obtained by DAST during the study.
before
after
Figure 1: Drawing of student 3 before and after the application.
before
after
Figure 2. Drawing of student 5 before and after the application.
Example interview questions that probed the changes in drawings were;
Why did you prefer a different drawing than the one you did seven weeks earlier? Explain your thoughts.
Probe 1: Do you think your previous drawing was wrong? Explain your thoughts.
Probe 2: Can you compare both drawings? What are the differences?
Probe 3: Why do scientists do science?
Probe 4: Is there a method that scientists use to obtain knowledge?
Probe 5: In which environments do scientists do their studies that you talked about?
Pilot study was conducted by one of the researchers in a classroom one year earlier than the main study.
Interviews were conducted with five students to determine unclear questions which were eliminated. The
remaining questions were evaluated to be understood and answered properly by students. This process provided
empirical support for the instruments.
Participants
80 participants (11-12 years of age) took the DAST test, 40 of them were in the experimental group and 40 of
them were in the control group. Six participants, who had different pre and post drawings from the experimental
group, were chosen for interviews.
Data Collection Process
The students completed DAST at the beginning and at the end of the application. Students were asked to draw a
scientist to a white paper that was provided to them. This process took about 20 minutes.
Interviews were conducted individually. Each interview took about 15 minutes. Pre-prepared interview and
probe questions were asked to obtain explanations about changes in drawings.
The Instruction
The research started with developing five stories related to a fifth grade Science and Technology course unit
named “Explore and Learn the World of Living Things.” The stories were developed based on the Turkish
Ministry of Education curriculum guidelines. Sentences in the stories were not longer than 15 words and were
easy to understand. In the pilot study, to determine whether students understood the task, they were given a
story map and asked to fill it based on the story. In the pilot study, students were able to complete the story
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maps successfully which indicated that the story texts were suitable for the fifth grade level. In the control
group, the textbook that was normally used in the school was used. In the experimental group, content was
given based on science stories. In both groups, content was structured with a context based and 5E learning
cycle model. The main difference between the applications was that in the experimental group scientific stories
were used during the “engage” stage, while in the control group, scientific stories were not used. In the
“explore” stage, same activities were used in both groups. The conclusions after activities were related to
scientific stories in the experimental group, while in the control group, conclusions were related to given
situations. Five different contexts were used to teach unit objectives with a context-based approach. Each
context was divided into subtitles and unit objectives were taught within these subtitles.
Context 1:
Story 1: First Step to Classification with Aristo
In very old times, a child was born in Macedonia. He was named Aristo and his father was a doctor… He
investigated and observed animals’ way of life, their movements, their body shapes and their habits. As a result
of his observations, he concluded that he can classify animals with four features. He thought that if I don’t share
my observations with other people, they would be worthless… He continued his studies as a scientist.
Context 2:
Story 2: Linneaus’s Scientific Stories
Linneaus was always interested in plants… As a scientist, he thought he should find a way to distinguish them.
He thought people should be able to distinguish them easily. He needed to specify the steps of his investigation
and continue with those steps. First of all he distinguished plants with flowers from others. With the results he
obtained from this, he was able to distinguish others easily. He worked for months. He made observations. He
investigated. At the end, he noticed the differences among the structure of the flowers. He classified them into
kinds… Linneaus became a famous scientist in history.
Context 3:
Story 3: Ray’s Observations
John Ray was a young person who lived in England in the 17 th century. He was interested in plants since he was
a child… Ray and his family were living in a rural area. Because of this he saw plants with different kinds of
roots, trunks, leaves and flowers. He investigated them. He shared his investigations with his friend Francis…
Ray stood up quickly. He ran toward the lake. He saw a plant that he never saw before. This plant had a root, a
trunk, leaves but no flowers. He took a leave from the plant and investigated. He was excited to discover a new
plant. … He became history as the first person who classified plants.
Context 4:
Story 4: Alexander Fleming’s Journey to Discover Penicillin
… He noticed that a new formation was emerging in the dish where bacteria were forming. The bacteria in this
dish died spontaneously. He started to investigate right away. He found that a mold fungus coming from the air
caused this… It formed the penicillin in the dish. Fleming noticed that this showed an effect of a medicine. He
thought he should share this medicine called antibiotic with everyone. This was a scientific investigation.
Sharing makes science more valuable. Patients could be cured this way…
Context 5:
Story 5: On the Road to Science, Louis Pasteur
… Diseases such as anthrax, cholera, rabies attracted his attention. He was not a doctor. He was a scientist. He
thought that he should do what he has to do. He started to investigate these diseases. Medical doctors of his time
criticized him a lot… He thought, “I should not give up on this research as a scientist.” … After a long study, he
found the rabies vaccination. One day, a boy who was bitten 14 times was brought to him by his parents. They
asked him to try the vaccination on their child, which was only been tried on animals. There was no treatment
for the disease at the time and the child was likely to die. So he applied the vaccination to the child with the
permission of medical doctors. The child was cured. It was discovered that the vaccination that he developed
was effective on humans as well. He took his place among the scientists who served the humankind.
Subject titles within the unit (experimental group)
1. How would Aristo classify animals, if he lived today?
2. Classifying animals based on Aristo’ method.
3. Do the animals that Aristo observed still exist?
4. Why did Linneaus separated plants that have flowers from others?
5.
6.
7.
8.
9.
10.
How did Linneaus discovered the differences in plant leaves?
Classifying plants based on Ray’s method.
Which category did Ray put mushrooms in?
Why some of the plants that Ray observed don’t exist today?
Were the bacteria that Fleming observed alive?
Let’s learn about the diseases that Pasteur worked on.
The following was aimed by using the stories:
1. To make students stereotypical understanding of nature of science more flexible by using the stories as
a context.
2. To make students stereotypical images of scientists more flexible by using the stories as a context.
Data Analysis
Phenomenographic analysis was used to analyze DAST data. Phenomenography was developed by Marton
(1981, 1986) in the beginning of the 1980s and it became popular worldwide in educational research. Initially it
was proposed as an approach for empirical research (Akerlind, 2012). Later, it was widely used as a descriptive
qualitative research method. In this method, the main strategy used is conducting clinical interviews to collect
data and analyzing the data through content analysis. Also to collect information about individuals’ experiences
or their opinions about concepts, data collected through group interviews, observations, answers given to open
ended questions, drawings and historical documents are also within the scope of phenomenographic studies
(Didiş, Özcan, & Abak, 2008). In this study, from the methods mentioned, collecting and analyzing data from
students’ drawings was preferred. To increase the persuasiveness of the research questions, two more
approaches were utilized beyond the phenomenographic analysis of students’ drawings. These were, i) using
chi-square test when needed, ii) conducting face-to-face interviews with students who exhibited changes in
images of scientists in their drawings.
Findings
In this study, data obtained from two different methods were used. First, data obtained from student drawings
related to scientists’ working environments, tools and methods they use when doing science were used. Second,
based on these findings, data obtained from face-to-face interviews with students that probed their images of
scientists and nature of science was used.
Table 1. Categories of students’ drawings
Category 1: Use of laboratory tools (test tube, glassware, magnifying glass, chemicals, etc.)
Category 2: Use of technological equipment (computers, microscopes, telescopes, machines, etc.)
Category 3: Scientist studying living things (plants, animals, humans)
Category 4: Scientists working in a laboratory (indoors)
Category 5: Scientists working outdoors (nature, space, etc.)
The frequencies of the occurrence of above categories in students’ drawings are given in Table 2.
Table 2. The frequency of the occurrence of five categories in students’ drawings in control and experimental
groups
Category
Group C (Control) Group E (Experimental)
N
%
N
%
Category1
28
70.0
34
85.0
Category2
17
42.5
16
40.0
Category3
7
17.5
1
2.5
Category4
32
80.0
35
87.5
Category5
4
10.0
2
5.0
Laboratory equipment was exhibited in most student drawings in both control (70%) and experimental (85%)
groups (test tube, glassware, magnifying glass, chemicals, etc.). There was no statistically significant difference
between Group C and Group E ([ϰ2(1)=2,581; p=0.108]). Group C and Group E exhibited similar percentages of
use of technological equipment (computers, microscopes, telescopes, machines, etc.) and there was no
statistically significant difference between the groups ([ϰ2(1)=0,052; p=0.820]). One of the least exhibited
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feature in both groups was drawing of scientist studying living things; Group C (17.5%), Group E (2.5%) and
there was no statistically significant difference between the groups ([ϰ2(1)=5,000; p=0.025]). The most common
drawing in both groups (80% in control, 87.5% in experimental) was scientists working indoors and there was
no statistically significant difference between groups ([ϰ2(1)=0,827; p=0.363]). The other least exhibited feature
in both groups was drawing of scientist working outdoors (5% in control, 10% in experimental) and again there
was no statistically significant difference between groups ([ϰ2(1)=0,721; p=0.396]).
Table 3. The frequency of the occurrence of five categories in students’ drawings in control and experimental
groups after the application (treatment)
Category
Group C
Group E
N
%
N
%
Category1
20
50.0
7
17.5
Category2
17
42.5
5
12.5
Category3
5
12.5
21
52.5
Category4
28
70.0
12
30.0
Category5
6
15.0
23
57. 5
After the application, there was a significant difference between drawings of control (50%) and experimental
(17.5%) group students regarding laboratory equipment (test tube, glassware, magnifying glass, chemicals, etc.)
in category 1. There difference was statistically significant between Group C and Group E ([ϰ2(1)=10,769;
p=0.001]). There was an important difference between control (42.5%) and experimental groups (12.5%) in
drawings of the use of technological equipment (computers, microscopes, telescopes, machines, etc.) in category
2 and the difference was statistically significant ([ϰ2(1)=9,028; p=0.003]). In category 3, there was a statistically
significant difference between drawings of scientist studying living things in Group C (12.5%) and Group E
(21%) ([ϰ2(1)=14,587; p=0.001]). Category 4, scientists working indoors, was another statistically significant
difference between the groups (70% in control, 30% in experimental) ([ϰ2(1)=12,800; p=0.001]). Finally, there
was statistically significant difference between groups in category 5 drawings, scientist working outdoors (15%
in control, 57.5% in experimental) ([ϰ2(1)=15,632; p=0.001]).
In category 5, there were small changes between pre and post drawings in the control group, however, no
statistically significant difference was found in any of the categories (p>0.05). In the experimental group, there
were statistically significant differences between pre and post drawings (p<0.05).
Equipment in the work environment of scientists
After the application, there were significant differences between students’ pre and post drawings. Students’
initial drawings dominantly included laboratory equipment (test tube, glassware, magnifying glass, chemicals,
etc.) in category 1; however, in later drawings equipment for observation of nature became more dominant. The
following conversation about this issue took place with Student 3.
Researcher: In your first drawing, there were glass bottles on the table, paintings on the wall, and
cabinets. In your last drawing, there is a scientist with a magnifying glass investigating a forest,
and dreaming about flowers and trees.
Student 3: I have forgotten what I have drawn in the first picture. I redraw it. That is okay too but
I draw this like the story of Aristotle and Linneous. I didn’t think they were scientists initially.
Actually in the cartoons that I watched before, there were people like that, but I taught of them as
detectives. After reading the stories, I understood that scientists don’t always work in laboratories.
I realized that they also work like detectives… of course some of them work in the laboratory, but
some are like detectives.
Researcher: Which drawing do you think is correct? The previous one or the later one?
Student 3: I think both are correct. Here (later drawing) he/she can take the works from nature to
laboratory. He/she can investigate the gathered living things in the laboratory like Fleming.
A similar situation was observed for the use of technological equipment (computers, microscopes, telescopes,
machines, etc.) as in category 2. In the following, Student 1’s views are given.
Researcher: Your first and second drawings are different. Why?
Student1: Yes. In this drawing (previous drawing) I was thinking that scientists are using weird
appliances. This year, I learned that they work in different ways… even when they are wandering about
in a forest. In this drawing (post drawing) I draw based on what I learned this year.
Researcher: Which drawing do you think is right? The previous one or the later one?
Student1: In fact, mostly the previous drawing is right. What I knew until now was like this (previous
drawing). We learned in this class… they can work differently. They can also work in laboratories. We
worked like scientists in the science course this year.
Some students’ drawings include changes in both categories at the same time. Student 4’s views that include
both categories are given below.
Researcher: Your first and second drawings are different. Why?
Student 4: Yes. This year we read about many scientists’ studies. Before I read them, I used to
think that all of the scientists were working with test tubes, glass bottles, and big equipment. I
taught that they used computers. But none of the scientist we read about had a computer. So I
thought about them while drawing.
Researcher: Which one of your drawing is correct do you think? Previous one or later one?
Student 4: The second one of course.
Researcher: Do you think that your previous drawing is wrong?
Student 4: No. Before reading about Aristotle and of course others I always taught like that
(previous drawing). What I saw on TV was like that. But the scientists we read about this year are
different. I learned later on. Scientists can work without computers or microscopes.
Scientists’ working environments and methods
After the application, significant differences were observed in students’ drawings of scientists’ working
environments and methods. In students’ initial drawings, laboratory (indoor) working environments were
dominant, as in category 4, however, in later drawings, category 5, outdoor environments, became dominant.
Student 4’s views about this issue are in the following.
Researcher: How do you think scientists work?
Student 4: I think most of them work in laboratories. I used to think all of them were like that.
Now, I learned that they also work in other places.
Students’ views changed so that they started to think that scientists may use laboratories when they need to, but
they don’t always have to do so. Student 3 expressed the following views about this.
Researcher: Do you think scientists work this way? Do they do their research like this? Do they
bring everything they collected to the laboratory to investigate?
Student 3: I think they have to bring small living things. For example, when they work on
bacteria, they have to. They can’t see them without bringing them to a laboratory. They need a
microscope. Of course, they don’t need to go for big animals. For example, they can’t take whales
to a laboratory. I saw it when my father was watching. They were watching them from a ship. I
think they are scientists too. They have computers. They look from a computer. They find out
how they live.
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Some students’ views changed to think that scientists may use different scientific methods. Student 1’s views
about this are as follows.
Researcher: How do you think scientists work?
Student 1: They work like us. They find solutions to problems. They also do experiments. They
wonder about in nature. They are interested in animals and living things. They find animals’
properties. They find very small living things. Of course they also know the space… but the ones
we heard about were interested in living things. I like living things as well. I have a cat. I wrote
about its features. My teacher liked it very much. She said “you became a small Aristotle.”
Some students’ views change in a way that they thought scientists may conduct scientific research in different
ways in different environments. Student 5 had the following views about this.
Researcher: How do you think are the working environments of scientists?
Student 5: In the old days, scientists have worked everywhere. For example, Fleming… He found
bacteria in a dish when he was tidying his room. Now the ones on TV always work in places
where there is equipment.
Researcher: Is this your last decision? Do you prefer to separate scientists’ work places as old and
new?
Student 5: Scientist may work in places like forests today as well… but on TV… my father watch
them all the time… they find big snakes in the forest; they observe ants’ properties that no one
knows about. They magnify small things. My father likes watching them.
Scientists’ working areas
After the application, important differences in students’ drawings regarding scientists’ working areas were
detected. In students initial drawings, the number of scientists working on living things (plants, animals,
humans) (category 3) was very low, but after the application, in the experimental group, nearly half of the
drawings included scientists working on living things. As a result of combining students’ prior knowledge with
their new learning, nearly half of them preferred to draw scientists working on living things, while other half
preferred to draw scientists working on other things. Student 2 provided following views related to this finding.
Researcher: What do you think about the working environments of scientists? How do they
determine their work subjects?
Student 2: They work outdoors a lot. There are more things to investigate outdoors. There are
many things in a forest and near a lake. There are trees. There are plants. They can investigate all
of it. They can also do camping with camp fire…
Researcher: But they are working in a closed environment in your first drawing. Aren’t they doing
scientific study?
Student 2: They can do that as well. But there are more things to investigate outdoors, of course,
in laboratory as well. We went to the laboratory a lot this year. We also investigated stuff that we
brought from outside. They bring from a forest to laboratory of course. They can also bring pets.
They can investigate without bringing them too.
Researcher: Please think that you become a scientist in the future. Would you want to work
indoors or outdoors?
Student 2: Outdoors of course. It is nice to work outdoors. I would work outdoors. Of course,
sometimes I would go to the laboratory but not much. I would want to work in a forest where
there are birds and animals. I would like to be like a scout.
Criteria for doing science
The differences in students’ drawings indicated a change in their thinking that for a study to be considered
scientific, it must contain an experiment. Student 3’s views about this are in the following.
Researcher: Do you think that the work of a person who observes worms or whales can be
considered scientific?
Student 3: Of course… For example, the stories we read were like that. They were all scientists. If
they didn’t do scientific studies, I think they could not have been scientists. For example there was
Ray. He only saw something that nobody did before and because of this he became a scientist. He
was a scientist too. So was Linneous. He separated plants, flowers. Since nobody did this before,
he became a scientist.
Researcher: Did you see a scientist before?
Student 3: Yes, in cartoons all the time.
Researcher: What kind of a person a scientist is do you think? Would you explain?
Student 3: In the cartoons that I used to watch when I was little, they always boiled something.
Usually some colorful thing was boiling in a glass. Scientist was next to it.
Researcher: Do you think the studies of the ones in stories or the ones in cartoons are scientific?
Student 3: The ones in the cartoons can’t be scientists, I think. They are cartoon versions of
scientists. Since they are important people, the things that they do in cartoons are more difficult.
They are scientists.
Researcher: What about the things they do in the stories.
Student 3: They are easier. Also enjoyable.
Researcher: Okay, which one you think is a scientific study?
Student3: I think, both of them.
Researcher: Why do you think that?
Student 3: The ones in the cartoons are copy of the scientists. The ones in the stories are like that,
since they are scientists. One wouldn’t be scientist for no reason. What they do is important, that
is why they are scientists.
Researcher: So the things they do are different, doesn’t this effect the scientific acceptance of their
work?
Student 3: It doesn’t, I think. Only one is difficult, the other is easy. The ones in cartoons are more
difficult. We can’t do them. Maybe grownups can. We did what they did in the stories. They are
easier. We also did science, but the things we did were already known, so we did not become
scientists. Maybe we will when we grow up.
Researcher: How do you think scientists do their discoveries?
Student 3: They are smart. They find things that nobody knows about. A cloud suddenly appears
in their heads. The find in that could, in their head suddenly.
It was observed that, students’ views about science started to become more flexible towards the idea that there is
no single method or criteria for doing science. Student 6’s views about this are as follows.
Researcher: We are now chatting face-to-face. Do you think this is a scientific work?
133
Student 6: No.
Researcher: What do you think needs to be done for a study to be considered scientific?
Student 6: Experiments should be conducted in a laboratory. It could be found from experiments.
Researcher: Do you think studies of Aristotle, Linneaus and Ray are scientific?
Student 6: Hmmm. I don’t know. But I think it was written that they were scientists in the stories.
Researcher: Did they work in a laboratory?
Student 6: No, but maybe they didn’t write that part. Maybe they could become scientists even if
they didn’t do things in laboratory. Because, they did not have a laboratory. They didn’t have
computers either. In fact, most of them work in a laboratory. They use weird appliances. Now I
think they could become scientists even if they didn’t.
Researcher: What kind of people do you think scientists are? Did you see a scientist before?
Student 6: No I didn’t. But I saw in movies. They were working in places where there were weird
huge machines. They make smart people scientist. They know everything.
Researcher: Do scientist work in places where there are advanced technological devices?
Student 6: Yes but some don’t. I used to think that way. Now I think some work in the nature.
Some work while wondering in nature. They try to see new things. When they see it, they become
scientists. I drew them in this picture for example.
Discussion
This study was conducted to make students stereotypical images of scientists flexible with the help of scientific
stories. Through interviews, it was found that while this flexibility developed, their view of science also
changed. Because of the content of the stories, not all stereotypical images reported in the literature were
observed, only the images indicated in the stories were changed. The stereotypical image of male, middle aged
with untidy appearance, wearing glasses and lab coats, having no social activities and lonely, which was
reported for scientists in the literature (Mead & Metraux, 1957; Chambers, 1983; Rosenthal, 1993; She, 1998;
Song & Kim, 1999; Dikmenli, 2010) continued to exist after the application in the study. Çakmakcı et al. (2011)
reported that, to change students’ stereotypical images, different interventions are needed. During the
application of this study, no intervention was done to change the aforementioned images, which may be the
reason for their continuity.
Regarding scientists’ working environments and equipment in these environments, important changes took
place in experimental group students’ images. In the literature, the stereotypical image of scientists was
described as a person working in a laboratory with advanced microscopes, telescopes, technological appliances,
experimental setups, glassware, and test tubes around, conducting dangerous experiments, and being stuck in the
laboratory (Mead & Metraux, 1957;Chambers, 1983; Rosenthal, 1993; She, 1998; Song & Kim, 1999;
Dikmenli, 2010). This image was changed through the use of structured stories and discovery based courses.
When scientific stories were used in an inquiry setting, they help students better understand how scientists work,
how do they construct knowledge and apply and evaluate it (Tao, 2003). The interviews that took place with
students revealed that the source of change in their images was context based learning, structured with scientific
stories. At the same time, findings from the interviews showed that the changes in students’ images did not lead
to new stereotypical images of scientists that, for example, they would not use tools or equipment. Besides the
interview transcripts, the fact that some students exhibited changes in their drawing while some continued to
draw similar images to their first images support that no new stereotypical images was developed. One of the
factors that caused change may be the teaching method.
The fact that one of the units was designed with a context based approach may have improved the influence of
scientific stories. There are evidences in the literature that suggest that context based teaching and learning
improves meaningful learning (Klassen, 2009). Besides increasing student motivation, teaching scientific issues
and concepts based on stories conform to constructivist principles. After hearing a story, to solve a problem
derived from the story, students enter an inquiry process (Klassen, 2007). The context based learning approach
used was not limited to providing scientific stories or a problem scenario in the beginning. The objectives of a
teaching unit were associated with the stories to construct the content knowledge. This allowed a continuous
mentioning of stories about scientists to students. At the same time, discovery based design of unit objectives
allowed students to learn new information through scientific stories. The fact that new information was
associated with the stories may have increased the effectiveness of the stories.
In the study, presenting stories with a context based learning approach gave flexibility to some of students’
stereotypical views. Drawings before the application showed that almost all students saw science as an activity
that is conducted in laboratories. After the application, interviews showed that students started to think that
science is not necessarily done in laboratories. Positivist philosophers argued that experimentation is a must
have criteria for a study to be accepted as scientific. Later on, heuristic philosophers rejected the criteria of
experimentation and argued that experiments in laboratories are not a necessity for doing science (Kıray, 2010).
After the application, students’ views shifted from a positivist view toward a heuristic view. However limited,
flexibility of students’ views about nature of science may be the result of scientific stories about scientists. The
fact that stories were in Milne’s (1998) heroic science and discovery science category may have contributed to
changes in students’ views. In the interviews, students often referenced story characters, which support these
possibilities.
Another finding that was found in the study was that students started to think that there was no single scientific
method. History of science shows that there is no single scientific method that encompasses all scientific
studies. It is possible to see that in a laboratory environment, besides data obtained from step by step performed
experiments, there are many discoveries that are not based on structured observations, which were the results of
curiosity, creativity, and imagination or just luck (Kıray, Bektaşlı & Erbatur, 2012). The stories of scientists
who did their discoveries outside laboratories may have made students ideas about scientific method more
flexible. Besides scientific method, students’ ideas about scientists’ work environments also changed. Before the
application, most student drawings have fallen into the category of physics or chemistry experiments conducted
in a laboratory. After the application, student drawings shifted towards biology. This may be the result of
scientists working on living things expressed in the stories. Before the application, drawings and interviews
showed that most students had a positivist outlook of science, which accepted physics and fields that used
methods of physics as science. After the application, students’ views shifted from a positivist view toward a
heuristic view.
In some of the drawings that students had after the application, there were speech balloons that indicated
scientists’ thinking. A similar finding was observed during interviews. They taught that scientists use their
imagination in their studies. This situation showed that students’ ideas shifted toward contemporary
understanding of nature of science. After Galileo, science was viewed as an objective activity, independent of
humans. This view continued until the heuristic philosophers. Today, this view of science has changed and now
it is accepted that science is a subjective activity that is influenced by scientists conducting it (Kıray, Bektaşlı &
Erbatur, 2012). Contemporary thinking also rejected the idea that science is an activity conducted in a laboratory
independently from the observer. The idea that science is an activity influenced by creativity and imagination of
scientists was accepted (Akerson, Abd-El-Khalick, & Lederman, 2000).
The findings of the study are parallel to the findings of Flick (1990), Mason et al. (1991), Reap et al. (1994), and
Çakmakcı et al. (2011) in that certain treatments may change students’ views of science and scientists. At the
same time, this study support the findings of Martin and Brouwer ( 1991), Milne (1998), Tao (2003), Reis and
Galvão (2004), Dagher and Ford (2005), Klassen (2007), Klassen (2010), Frisch (2010), and Emani (2010) in
that scientific stories are effective in changing students’ images of science and scientists.
Conclusion
With the educational approach of having students learn like scientists, the images of scientists that students have
become important. Making students stereotypical ideas about scientist more flexible also made their ideas about
nature of science more flexible. After using scientific stories, students’ stereotypical images of scientists
working indoors and using experimental tools and technological equipment have decreased while images of
scientists working outdoors with living things have increased. At the same time stereotypical images of male,
untidy scientists wearing lab coats have not changed.
135
The changes in students’ stereotypical scientist images have also influenced their views of nature of science.
Drawings before the application showed that students viewed science as an activity that is mainly conducted in
laboratories and the criteria for doing science is experimentation. After the application, students’ views started
to shift towards the idea that science is not necessarily done in laboratories and science can be done without
experiments. Students’ initial image of a single method for doing science shifted toward the idea that there was
no single scientific method after the application. Students views about scientists’ working environments have
fallen into the category of physics or chemistry experiments conducted in a laboratory. After the application,
student drawings included biology besides chemistry and physics. This change is important for students’
learning of the idea that science cannot be limited to one field. At the same time, in some students’ views,
changes toward the idea that scientists use their imagination in their studies took place. Before the application,
students’ view of science was closer to the positivist philosophy, which was dominant 100 years ago; after the
application students’ ideas shifted toward currently dominant heuristic view of science. The results of the study
are important in helping students understand that there is no one type of science or scientist. The applications of
the study conducted to change students’ stereotypical view of scientists also changed their views of science and
scientific method, which shows that images of nature of science and scientists are connected.
For researchers who do research in this area, we suggest thinking of images of science and scientists together.
We suggest using context based learning approach and scientific stories for teachers who wish to bring their
students’ views of science and scientist closer to contemporary views of nature of science.
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