the role of beliefs and motivation in asynchronous online learning in

J. EDUCATIONAL COMPUTING RESEARCH, Vol. 50(3) 315-341, 2014
THE ROLE OF BELIEFS AND MOTIVATION
IN ASYNCHRONOUS ONLINE LEARNING
IN COLLEGE-LEVEL CLASSES
KUI XIE
The Ohio State University
KUN HUANG
Mississippi State University
ABSTRACT
Epistemic and learning beliefs were found to affect college students’ cognitive engagement and study strategies, as well as motivation in classroom
settings. However, the relationships between epistemic and learning beliefs,
motivation, learning perception, and students’ actual learning participation
in asynchronous online settings have been under-studied and under-theorized.
In this study, 132 students participated in collaborative learning activities
through asynchronous online discussions in a college-level online course. The
results from correlation analysis and structural equation modeling indicate
that epistemic and learning beliefs have significant effects on students’
learning participation and perceived learning, through the mediation of
achievement goals. The findings provide theoretical and practical implications for the design of online learning.
1. INTRODUCTION
Research has demonstrated the interactive relationships between epistemic and
learning beliefs, motivation, and learning (Braten & Strømsø, 2004; DeBacker
& Crowson, 2006; Kardash & Howell, 2000; Ravindran, Greene, & DeBacker,
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doi: http://dx.doi.org/10.2190/EC.50.3.b
http://baywood.com
316 / XIE AND HUANG
2005). The few existing attempts also examined how these variables interact with
each other to collectively impact learning in classroom settings (Chen & Pajares,
2010; Kizilgunes, Tekkaya, & Sungur, 2009; Ravindran et al., 2005). As today’s
education moves toward online settings, students’ online learning experience is
significantly different from that in face-to-face settings. They have to rely on
computer-mediated communications and exercise self-regulation to achieve
success in online learning. Investigations become necessary on the interplay
between learners’ epistemic and learning beliefs, motivation, and learning in
online learning environments. In addition, previous studies on epistemic and
learning beliefs relied mostly on self-reported measures of students’ cognitive
engagement (e.g., DeBacker & Crowson, 2006; Ravindran et al., 2005) or study
strategies (e.g., Kardash & Howell, 2000; Schommer, Crouse, & Rhodes, 1992).
Recent research has suggested that students’ actual participation and learning
behavior can greatly reflect their motivational characteristics (Fredericks,
Blumenfeld, & Paris, 2004; Reeve, 2006). While student participation data are
difficult to obtain in face-to-face settings (Dennen, 2008), most Learning
Management Systems (LMS; e.g., WebCT®) have the capability to track a
variety of students’ participation records in online learning activities (Xie,
2013). The learning participation data, also being referred to as learning
analytics (Ferguson, 2012), become valuable resources for educational research.
More importantly, these data may provide researchers with the opportunity
to better understand online learning. Using a combination of self-reported
and learning analytics data, the present study built and tested a model among
epistemic and learning beliefs, motivation, and student participation and
perception in collaborative, asynchronous online discussions in college-level
online classes.
2. THEORETICAL FRAMEWORK
2.1 Relationship Between Beliefs and Learning
Epistemic beliefs refer to an individual’s implicit beliefs about knowledge and
knowing (Hofer & Pintrich, 2002). Research on epistemic beliefs started with
a unidimensional model with “dualist, absolutist, and objectivist” on one end,
and “relativist, subjectivist, contextual, constructivist, and evaluative” on the
other (Stathopoulou & Vosniadou, 2007, p. 146). Later, researchers advanced
multidimensional models in examining the relationship between epistemic beliefs
and learning (e.g., Schommer, 1990; Schommer-Aikins, Duell, & Hutter, 2005;
Schraw, Bendixen, & Dunkle, 2002; Wood & Kardash, 2002). Five general
dimensions of epistemic beliefs have been articulated, including the structure of
knowledge (ranging from the belief that knowledge can be described as isolated
pieces to the belief that knowledge structure is complex and highly interrelated),
the certainty of knowledge (ranging from the belief that knowledge is certain and
BELIEFS AND MOTIVATION IN ASYNCHRONOUS ONLINE LEARNING /
317
unchanging to the belief that knowledge is tentative and evolving), the source
of knowledge (ranging from the belief that knowledge comes from omniscient
authorities to the belief that knowledge emerges from personal construction), the
nature of ability to learn (ranging from the belief that ability to learn is innate
to the belief that learning ability can be acquired with effort), and the speed
of learning (ranging from the belief that learning takes place quickly or not at
all to the belief that learning is a gradual process) (Braten & Strømsø, 2005;
Schommer, 1990; Schraw et al., 2002). The five dimensions were later questioned
by Hofer and Pintrinch (1997), who argued that the nature of ability and the speed
of learning do not conceptually belong to the construct of epistemic beliefs.
Schommer-Aikins (2004) later theorized that the two sets of beliefs are in fact
beliefs about learning. She further advocated the need to investigate epistemic
beliefs in light of other closely related belief systems such as beliefs about learning. Research has found that epistemic beliefs (e.g., simple and certain knowledge)
and beliefs about learning (e.g., quick learning) could influence academic performance (e.g., Qian & Alvermann, 1995; Schommer, 1993; Schommer-Aikins
& Easter, 2006; Schommer-Aikins et al., 2005; Windschitl, 1997), cognitive
engagement (e.g., DeBacker & Crowson, 2006; Ravindran et al., 2005), and study
strategies (e.g., Kardash & Howell, 2000; Schommer et al., 1992).
While previous research demonstrated a clear impact of epistemic and learning beliefs on learning, it is unclear whether the relationship is direct, indirect
through other mediating variables, or both. Some studies found that epistemic
and learning beliefs directly influenced learning (e.g., Cano, 2005; Kizilgunes
et al., 2009; Schommer, 1993; Schommer, Calvert, Gariglietti, & Bajaj, 1997;
Schommer-Aikins et al., 2005). Others suggested that epistemic and learning
beliefs affected learning indirectly through latent variables such as learning
approaches (e.g., Cano, 2005; Schommer et al., 1992), regulation of cognition
(e.g., Greene, Muis, & Pieschl, 2010; Muis, 2007), and motivation (e.g., DeBacker
& Crowson, 2006; Hofer & Sinatra, 2010; Kizilgunes et al., 2009). As a result,
in this study we modeled the relationship between beliefs and students’ participation and perception in online learning both directly and indirectly through
the mediation of motivation.
2.2 Relationship Between Motivation and Learning
Students’ motivation in achievement settings is often elaborated through goal
orientations, which emphasize the reasons for engaging in academic activities
(Eccles & Wigfield, 2002). Earlier research distinguishes between two types of
goals: mastery goals (or learning/task goals) focusing on increasing competence, and performance goals (or ego goals) focusing on gaining favorable
or avoiding negative judgment of competence (Ames, 1992; Dweck, 1986;
Nicholls, Cobb, Yackel, Wood, & Wheatley, 1990). In resolving controversial
findings regarding performance goals, Elliot and Church (1997) proposed a
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trichotomous achievement goal framework, which divided performance goals
into performance-approach (outperform others in a desirable event to show
competency) and performance-avoidance goals (avoid demonstration of incompetence in an undesirable situation). Achievement goals have important bearings
on learning. Studies have reliably shown that individuals with mastery goals
are likely to choose challenging tasks, show interest, persistence and effort,
demonstrate self-regulated learning, adopt meaningful study strategies, and
achieve better learning outcomes (Elliot & Dweck, 1988; Elliot, McGregor,
& Gable, 1999; Eppler & Harju, 1997; Graham & Golan, 1991; Licht & Dweck,
1984). Further, mastery goals were found to be more beneficial in social settings
(in comparison to isolated settings when individuals perform tasks alone)
(Utman, 1997). Comparatively, individuals with performance-avoidance goals
use shallow processing strategies, demonstrate less intrinsic motivation and
interest, and show poor academic performance (Church, Elliot, & Gable, 2001;
Elliot & Church, 1997; Elliot & McGregor, 1999; Greene & Miller, 1996; Pintrich
& Garcia, 1991). With regard to performance-approach goals, research has
shown their positive effects on academic performance and affect, especially in
competitive college classrooms (Church et al., 2001; Elliot & Church, 1997;
Harackiewicz, Barron, Tauer, & Elliot, 2002). On the other hand, performanceapproach goals may also lead to negative outcomes such as shallow cognitive strategies (Elliot et al., 1999; Greene & Miller, 1996; Midgley, Kaplan,
& Middleton, 2001).
Another important motivational construct, self-efficacy, is defined as “the
conviction that one can successfully execute the behavior required to produce
the outcomes” (Bandura, 1977, p. 193). It is a person’s inner belief of his or her
own competency. Compared with their less self-efficacious peers, the students
with higher levels of self-efficacy are likely to initiate effort when faced with
challenging tasks, show more persistence and effort, adopt meaningful learning
strategies, and achieve better academic performance (Greene & Miller, 1996;
Pajares & Miller, 1994; Walker, Greene, & Mansell, 2006; Zeldin & Pajares,
2000; Zimmerman & Bandura, 1994).
Achievement goal theories suggest that students’ goal adoption has strong
relations with their self-efficacy beliefs. Research has identified positive correlations between self-efficacy and mastery goals (Greene & Miller, 1996;
Sins, van Joolingen, Savelsbergh, & van Hout-Wolters, 2008). High self-efficacy
was found to predict mastery and performance-approach goals (Elliot &
Church, 1997; Greene, Miller, Crowson, Duke, & Akey, 2004; Walker et al.,
2006), whereas low competency expectancies predicted performance-avoidance
goals (Elliot & Church, 1997).
In light of the findings about the relationship between motivation and learning,
our model includes relational paths between motivation variables (i.e., mastery
goals, performance-approach goals, performance-avoidance goals, and selfefficacy) and learning variables (i.e., learning participation and perception of
BELIEFS AND MOTIVATION IN ASYNCHRONOUS ONLINE LEARNING /
319
learning). Our model also includes relational paths between achievement goals
and self-efficacy variables.
2.3 The Relationship Between Beliefs and Motivation
Epistemic and Learning Beliefs and Goal Orientation
Hofer and Pintrich (1997) posited that epistemic beliefs might influence academic performance through their impact on motivation, which includes achievement goals. They further suggested that individuals with more sophisticated
epistemic beliefs would be more likely to adopt mastery goals. DeBacker and
Crowson (2006) also argued that particular epistemic beliefs could create tendencies to adopt certain achievement goals. Although research has not yielded
consistent results on how different dimensions of epistemic and learning beliefs
might influence students’ adoption of achievement goals, recent studies generally
suggested that students who held less mature beliefs were less likely to adopt
mastery goals but more likely to adopt performance goals, and those who held
more mature beliefs were on the contrary. For example, studies have repeatedly
shown that students’ adoption of mastery goals was negatively related to beliefs
in certain knowledge (Braten & Strømsø, 2005), omniscient authority (Kizilgunes
et al., 2009), quick learning (Braten & Strømsø, 2004, 2005), and fixed ability
(Cury, Elliot, Da Fonseca, & Moller, 2006; Hong, Chiu, Dweck, Lin, & Wan,
1999; Kray & Haselhuhn, 2007). The adoption of performance-avoidance goals
was positively predicted by beliefs in certain knowledge and fixed ability,
while the adoption of performance-approach goals was positively predicted by
beliefs in omniscient authority (Chen & Pajares, 2010; Muis & Franco, 2009),
simple knowledge and fixed ability (Cury et al., 2006; Ravindran et al., 2005).
Epistemic and Learning Beliefs and Self-Efficacy
Would certain epistemic and learning beliefs give rise to certain levels of
self-efficacy beliefs? Research has been done to investigate whether epistemic
and learning beliefs would be possible antecedents for self-efficacy beliefs.
Paulsen and Feldman’s (1999) correlation study showed that beliefs in simple
knowledge and quick learning were related to lower self-efficacy. In Kizilgunes
et al.’s (2009) study, path analysis found significant associations between three
dimensions of epistemic beliefs (source, development, and justifications) and
self-efficacy. Specifically, mature beliefs about the source and development of
knowledge were associated with a high level of self-efficacy; on the other hand,
those with mature beliefs about the justification of knowledge tended to be less
self-efficacious. Different from Kizilgunes et al.’s (2009) findings, Chen and
Pajares (2010) used the same instrument to measure epistemic beliefs and found
that beliefs about the certainty and justification of knowledge predicted selfefficacy, with more advanced beliefs leading to higher self-efficacy. In Neber and
320 / XIE AND HUANG
Schommer-Aikin’s (2002) study, path analysis found that naïve beliefs that
success is unrelated to work predicted low levels of self-efficacy. Braten and
Strømsø (2005) found three significant predictors of Norwegian student teachers’
self-efficacy: beliefs about the speed of learning, beliefs about control of knowledge acquisition, and implicit theories of intelligence. In a recent study by
Komarraju and Nadler (2013), less self-efficacious students were found to be more
likely to hold naïve theories of intelligence. Overall, while the findings provided
some evidence about the relationship between epistemic and learning beliefs and
academic self-efficacy, the findings lack consistency with regard to the impact
of specific dimensions of epistemic and learning beliefs on self-efficacy.
In light of previous research findings, our study intended to find out whether
and how specific dimensions of beliefs interact with students’ motivation.
Therefore, our model depicts the relationship between the five dimensions of
epistemic and learning beliefs and motivation variables (achievement goals and
self-efficacy).
3. TOWARD A THEORETICAL MODEL IN ONLINE
LEARNING ENVIRONMENT
Online learning environments have brought great benefits to education.
Among them, asynchronous online discussion is the most common approach to
facilitating knowledge construction and collaborative learning. Therefore, we
chose asynchronous online discussion as the learning context in the present
study. The recent trend of research in asynchronous online learning focuses on
the emerging interaction, discourse, and participation processes among members
of a learning community (Lipponen, Hakkarainen, & Paavola, 2004). Students’
participation in asynchronous online discussions was found to be a strong indicator of learning (Xie & Ke, 2011). Students exhibit two types of participation
behaviors in asynchronous online discussions: posting and non-posting behaviors.
Posting behaviors include posting new messages and replying to others’
messages, both of which leave visible textual input in an LMS. As an indicator of
learning, students’ posting behaviors have been investigated in many studies (e.g.,
content of messages, number of posted or responded messages, length of message,
etc.) (e.g., Cheung, Hew, & Ling-Ng, 2008). Students demonstrate non-posting
behaviors when they visit a discussion forum to simply read or evaluate others’
messages without actually posting a message. Several recent studies argued that
non-posting behaviors should also be studied as an indicator of online learning
(Beaudoin, 2002; Dennen, 2008; Xie, 2013).
Furthermore, learning analytic data tracked by LMSs involve both quantitative
data, which may reflect the frequency of student learning participation, and
qualitative data, which may reflect the quality of student learning participation. The body of research has extensively used quantitative data to investigate students’ learning engagement in online discussions. For example, posting
BELIEFS AND MOTIVATION IN ASYNCHRONOUS ONLINE LEARNING /
321
frequency has been investigated as an engagement variable in numerous studies.
Learning analytics data of non-posting behavior have also gained attention in
recent studies (e.g., Wise, Speer, Marbouti, & Hsiao, 2013; Xie, Yu, & Bradshaw,
2014). On the other hand, qualitative data of student participation, the content
of discussions, may be the most valuable source to reflect student learning in
online discussions. However, it is still considered to be a challenging task in
current research to analyze discussion content and interpret complex natural
language in a meaningful way in order to reveal latent constructs (e.g.,
motivation). Researchers have also recognized technical limitations of content
analysis in terms of inter-rater reliability, unit of analysis, generalizability, etc.
(Lombard, Snyder-Duch, & Bracken, 2002; Rourke, Anderson, Garrison, &
Archer, 2001; Wever, Schellens, Valcke, & Keer, 2006). Therefore, quantitative
data, which can be collected and transformed automatically for analyses, provide
a reliable and feasible means of evaluating student performance in online learning.
The present study collected quantitative data as indicators of student participation
in online discussions.
Research investigating the relationships among epistemic and learning beliefs,
motivation, and learning has been largely focused on classroom situations. A
number of studies have begun to examine the relationship among these variables
in the online learning environment. For example, Tu, Shih, and Tsai (2008) found
that students who had more advanced epistemic beliefs, concurring with a constructivist perspective, had richer learning experience in an open-ended Internetbased learning context. Wu and Hiltz (2004) identified the positive impact of
motivation and enjoyment on students’ perceived learning from asynchronous
online discussions. Chen and Wu (2012) studied Taiwanese students’ motivation
and learning strategies in online learning and found that students’ learning goals
and cognitive preferences predicted their use of metacognitive strategies and
later influenced their performance. Xie and his colleagues found that posting
participation (i.e., messages posted in an asynchronous online discussion system)
had a significant relationship with students’ motivation, which suggested that
students with higher levels of intrinsic motivation had higher participation rates
than those with lower levels of intrinsic motivation (Xie, DeBacker, & Ferguson,
2006; Xie, Durrington, & Yen, 2011). Further, Xie (2013) found significant
influences of motivation on the frequency of students’ posting and non-posting
participations in asynchronous online learning. The findings also suggested that
students’ posting participations were more influenced by extrinsic motives (e.g.,
course requirements), and non-posting participations were more voluntary in
nature and might be a better indicator of students’ intrinsic motivation.
In an effort to understand how epistemic and learning beliefs, motivation, and
student participation and perception simultaneously interact with each other in
asynchronous online learning activities, the present study built and tested a
model among the variables by utilizing both self-reported measures and learning
analytics. The study also sought to investigate the direct or indirect influence of
322 / XIE AND HUANG
epistemic and learning beliefs on online learning. According to the aforementioned
theoretical framework, we built a hypothetical model (Figure 1) in which epistemic
and learning beliefs (i.e., simple knowledge, certain knowledge, omniscient
authority, fixed ability, and quick learning) are related to online learning
participation (i.e., posting and non-posting participation) and perception both
directly, and indirectly through the mediation of motivation variables (i.e., mastery
goals, performance-approach goals, performance-avoidance goals, and
self-efficacy).
4. Method
4.1 Participants and Context
One hundred thirty-two undergraduate students (32 males and 100 females) at
a large university in the southeastern United States participated in this study.
The participants’ ages ranged from 19 to 61. Proportion of participants by
academic level was: freshmen 5 (3.8%), sophomores 16 (12.1%), juniors 39
(29.5%), and seniors 72 (54.5%). They reported their ethnicity as follows: White
75 (56.8%), African American 50 (37.9%), and others 7 (5.3%). The participants
were recruited from eight sections of an undergraduate online course in instructional technology, four of which were offered in the spring semester and four in
the fall. Each section had 14 to18 students who chose to participate in this study.
Figure 1. Relations between Epistemic and Learning Beliefs,
Motivation and Online Learning.
Note: Several variables were grouped into categories in order to simplify the
presentation of the full model. Epistemic & Learning Beliefs have five subscales:
simple knowledge, certain knowledge, omniscient authority, fixed ability, and
quick learning. Achievement Goals have three subscales: mastery goals,
performance-approach goals, and performance-avoidance goals. Online
learning participation has two subscales: posting participation and
non-posting participation.
BELIEFS AND MOTIVATION IN ASYNCHRONOUS ONLINE LEARNING /
323
All the sections were taught by the same instructor and followed identical learning
procedures. One hundred nineteen participants (90.2%) rated their confidence
level as high in the use of technology to complete the coursework.
4.2 Measurement
Four groups of variables were measured in this study: epistemic and learning
beliefs, motivation (i.e., achievement goals and self-efficacy), online learning
perception, and online learning participation (i.e., posting and non-posting
participations).
Epistemic and Learning Beliefs
Epistemic beliefs inventory (EBI) was used to assess epistemic beliefs (Schraw
et al., 2002). The reason for choosing EBI is because of its better psychometric
quality in assessing epistemic and learning beliefs (DeBacker, Crowson, Beesley,
Thomas, & Hestevold, 2008). EBI consists of 23 items measuring five dimensions.
Three of the dimensions measure epistemic beliefs: simple knowledge indicating
the belief about the structure of knowledge (four items), certain knowledge
indicating the belief about the certainty of knowledge (six items), omniscient
authority indicating the belief about the source of knowledge (five items). The
other two dimensions probe learners’ beliefs about learning: innate ability indicating the belief about the nature of ability to learn (five items), and quick learning
indicating the belief about the speed of learning (three items). For each dimension,
high scores indicate naïve beliefs. In their validity study, Schraw et al. (2002)
documented that the instrument’s subscale a ranged from 0.50–0.60s.
Motivation
Achievement goals questionnaire developed by Elliot and Church (1997) was
adopted to assess students’ goal orientations. This questionnaire is composed of
six items for each of the three achievement goals in the trichotomous framework
(i.e., mastery goal, performance-approach goal, and performance-avoidance
goal). Several studies have provided evidence for the reliability, construct
validity, and predictive validity of these measures, where the subscale a’s
typically ranged from 0.70–0.90s (see Elliot & Church, 1997; Elliot & McGregor,
1999; Elliot & Thrash, 2001). Dweck’s (1999) confidence measure was used
to assess students’ self-efficacy in these online classes (e.g., “I usually think
that I’m good in this online class”). The scale is composed of six items and a
previously reported coefficient a was .90 (Cury et al., 2006).
Perceived Learning
Perception of learning from an online discussion scale (Wu & Hiltz, 2004)
was administered, which has 10 questions inquiring about students’ perceived
324 / XIE AND HUANG
learning in online discussion activities (e.g., “The online discussions helped me
to think more deeply”). This scale has been adopted in numerous studies and
shown high internal reliability.
Online Learning Participation
Two components of students’ online learning participation were measured:
1. posting participation index including two subscales: standardized score of
the total number of posts and standardized score of the total length of posts
by individual students; and
2. non-posting participation index including two subscales: standardized score
of the total number of times logged in without posting and standardized
score of the total length of logins by students.
4.3 Procedure
In this study, students participated daily in online discussion activities in a
16-week duration of an instructional technology course. They were assigned to
small groups of 8 to 10. The online discussion activities invited the students
to share information and contribute to knowledge construction. The instructor
monitored and facilitated the online discussion activities. The online course was
delivered in the WebCT® LMS. Asynchronous online discussions were facilitated
by an online discussion system (iDiscuss) developed by the researchers. One
special feature of this system is its easy-to-access data collection and management
functions, which has the capability to automatically track and record students’
posting and non-posting participation data. Instructors and researchers can easily
download students’ online discussion data for analysis (i.e., time of login, total
message posts, total topics read, etc.) (Xie, 2013; Xie, Miller, & Allison, 2013).
At the end of the semester, the students were asked to complete a survey voluntarily. The survey was delivered online, consisting of measures of epistemic and
learning beliefs, goal orientations, self-efficacy, and perceived learning toward the
online discussion activities. Students’ learning analytics data were exported at
the end of the semester. Posting and non-posting participation indices were calculated from the data downloaded from iDiscuss for further statistical analyses.
5. RESULTS
Three steps of analysis were performed in order to test the relationships among
epistemic and learning beliefs, achievement goals, self-efficacy, students’ perceived learning, and their actual online participations in the asynchronous online
learning activities. The first step calculated descriptive statistics and reliability
coefficients for the instruments measuring each of the variables. In the second
step, a correlation matrix was generated to examine the relationships among
BELIEFS AND MOTIVATION IN ASYNCHRONOUS ONLINE LEARNING /
325
variables. The third step utilized Structural Equation Modeling (SEM), using
maximum likelihood techniques with AMOS® version 18.
5.1 Descriptive Statistics and Reliability Coefficients
Cronbach a reliability coefficients were computed for each of the selfreported subscales. These values were consistent with the reliability values
reported in the literature, providing evidence of internal consistency. For each
of the subscales, Table 1 shows a sample item, descriptive statistics, and its
Cronbach a reliability coefficient.
5.2 Subscale Inter-Correlations
An examination of zero-order correlations, shown in Table 2, provided the
validity of composite variables in epistemic and learning beliefs, goal orientations, self-efficacy, online learning participation, and online learning perception.
Consistent with theory, the results suggested that epistemic and learning belief
variables were significantly correlated with each other, except for the relationship
between omniscient authority and quick learning. Among the goal orientation
variables, performance-approach goal was significantly correlated with both
mastery goal and performance-avoidance goal. In addition, all inter-correlations
between the online learning participation and perception variables were significant at .01 level.
The correlations among epistemic and learning beliefs, goals, and self-efficacy
suggested that there could be potential influential effects of epistemic and learning beliefs and self-efficacy on achievement goals. Omniscient authority, quick
learning, and simple knowledge had a significantly positive correlation with
performance-avoidance goal. Also, simple knowledge had a positive correlation
with performance-approach goal, and quick learning had a negative correlation
with mastery goal. Self-efficacy had a positive correlation with mastery goal
and performance-approach goal, but a negative correlation with performanceavoidance goal. The correlations between goals, self-efficacy, and online learning
variables suggested that goals and self-efficacy might contribute to students’
online learning engagement. The correlation matrix showed that both mastery
goal and self-efficacy significantly correlated with all online learning variables.
Yet performance-approach goal and performance-avoidance goal did not have
a significant correlation with any of the online learning variables.
In addition, the correlation matrix did not show significant correlations
between epistemic and learning beliefs and online learning variables. The result
might be due to the existence of mediators or latent variables between these two
groups of variables, which necessitated further analysis using a more advanced
statistical technique.
4.09
3.07
4.91
3.80
2.46
5.96
4.38
4.61
5.20
5.26
Simple knowledge: Too many theories just complicate things.
Certain knowledge: What is true today will be true tomorrow.
Omniscient authority: People who question authority are troublemakers.
Fixed ability: How well you do in school depends on how smart you are.
Quick learning: Students who learn things quickly are the most successful.
Mastery goals: I want to learn as much as possible from this class.
Performance-approach goals: It is important for me to do better than the other students.
Performance-avoidance goals: My goal for this class is to avoid performing poorly.
Self-efficacy: I usually think that I’m good in this online class.
Perceived learning: The online discussions helped me to think more deeply.
Note: SD = standard deviation; a = Cronbach a coefficient.
Mean
Variable: Sample Item
.90
.95
.92
.91
.82
.75
SD
1.31
1.03
1.24
1.49
Table 1. Sample Items, Descriptive Statistics, and Reliability Coefficients
1.00-7.00
1.67-7.00
1.00-7.00
1.00-7.00
2.50-7.00
1.00-5.40
1.71-6.00
2.40-6.80
1.50-5.25
1.86-6.14
Min-Max
.95
.86
.82
.91
.88
.69
.71
.61
.58
.62
a
326 / XIE AND HUANG
*p < .05 significant level (two-tailed).
**p < .01 significant level (two-tailed).
12. Perceived learning
11. Non-posting participation
10. Posting participation
9. Self-efficacy
8. Performance-avoidance goals
7. Performance-approach goals
6. Mastery goals
5. Quick learning
4. Fixed ability
3. Omniscient authority
2. Certain knowledge
1. Simple knowledge
1.00
1
.33**
1.00
1.00
.24**
3
.18*
2
1.00
.22*
.18*
.32**
4
6
.16
1.00
1.00
–.22*
.55** –.04
.08
.26** –.11
.33** –.01
5
1.00
.21*
.15
.16
.06
–.00
.21*
7
1.00
.24**
–.05
.22*
.16
.22*
.12
.22*
8
–.06
10
–.05
.25**
–.11
–.12
.13
1.00
1.00
.25**
–.27** –.12
.18*
.42**
–.05
–.15
–.13
–.35** –.06
–.04
9
Table 2. Correlation among Epistemic and Learning Beliefs, Motivation, Online Participation,
and Learning Perception
1.00
.62**
.27**
–.17
–.02
.23*
–.08
–.15
.05
–.05
–.13
11
1.00
.22**
.29**
.21*
–.04
.11
.35**
–.11
–.09
.09
–.06
.01
12
BELIEFS AND MOTIVATION IN ASYNCHRONOUS ONLINE LEARNING
/ 327
328 / XIE AND HUANG
5.3 Structural Equation Modeling
In the third step of data analysis, SEM was used to test the relationships
among epistemic and learning beliefs, achievement goals, self-efficacy, and online
learning participation and perception from a perspective of structured framework.
SEM takes into account the modeling of latent variables and nonlinearities to
test the model from an integrated perspective, namely to test whether the overall
web of causal relationships adequately describes the data (Fox, 2002; Hatcher,
1994). SEM method has been widely used in educational research (e.g., DeBacker
& Crowson, 2006; Greene et al., 2004).
To evaluate the hypothetical model, a mixture of recommended fit indices
was used. The value of the likelihood ratio chi-square statistic (c2) indicates
whether the hypothetical model deviates from the data. The root mean square of
approximation (RMESA) estimates the lack of fit compared to the saturated
model. The goodness of fit index (GFI) represents the proportion of the variance
in the sample variance-covariance matrix accounted for by the model. According
to previous research (e.g., Bollen & Curran, 2006; Byrne, 1998; Kline, 2005;
MacCallum, Browne, & Sugawara, 1996; Tabachnick & Fidell, 2001), models
with c2/df less than 2, GFI greater than 0.90, an RMSEA less than or equal to
0.05 are considered to be a reasonable fit to the data.
For the purpose of this study, one hypothetical model was built (Figure 1)
in which epistemic and learning beliefs were related to students’ online learning
participation and their perceived learning, both directly and indirectly through the
mediation of motivation variables. The results indicate that the model explained
the data well and fit indices provided evidence of adequate model-to-data fit: the
Chi-square goodness-of-fit indices (c2 = 2828, c2/df = 1.693) being less than 2,
indices GFI (.911) being greater than 0.9, and RMSEA (.049) falling below 0.05.
With respect to the predicted paths, our model was partially supported. Many,
but not all, of the proposed paths were supported by the data. The significant
relationship among variables in the final model (Figure 2) supported that the
influence of epistemic and learning beliefs on learning was significant but was
mediated through motivation variables. Due to the complexity of the original
model, Figure 2 depicts only the significant paths found in the SEM.
Both learning beliefs significantly predicted achievement goals: quick learning
(b = –.51, p = .026) had significantly negative effects on mastery goal, while
fixed ability significantly predicted both performance-approach goal (b = .28,
p = .043) and performance-avoidance goal (b = .12, p = .029). Out of the three
epistemic beliefs, omniscient authority (b = .33, p = .009) significantly and positively predicted mastery goal. No other relationship was found in the final model
between epistemic and learning beliefs and motivation variables.
Within the motivation variables, the relationships between self-efficacy and
goals were significant in the resultant model. Self-efficacy significantly and positively predicted both mastery goal (b = .39, p < .001) and performance-approach
BELIEFS AND MOTIVATION IN ASYNCHRONOUS ONLINE LEARNING /
329
Figure 2. Final model with significant paths.
Note: By default, path coefficients were tested using a two-tailed test.
goal (b = .23, p = .041). It also negatively predicted performance-avoidance
goal (b = –.42, p < .001). The results were echoed by the correlation results,
which denoted that self-efficacy was positively correlated with mastery goal
and performance-approach goal, while negatively correlated with performanceavoidance goal.
With respect to the relationships between motivation and online learning
participation and perception, several significant paths were found in the final
model. Mastery goal significantly predicted all of the online learning variables:
perceived learning (b = .43, p < .001), online posting participation (b = .51,
p = .004), and online non-posting participation (b = .46, p = .020). All the
effects were positive. In addition, performance-avoidance goal had a significantly
negative relationship to perceived learning (b = –.14, p = .041) and non-posting
behavior (b = –.22, p = .044). The relationships between self-efficacy and online
learning participation were also significant. Self-efficacy significantly predicted
both posting participation (b = .36, p = .05) and non-posting participation
(b = .50, p = .018). No significant relationship was found between self-efficacy
and perceived learning.
The SEM final model suggested that epistemic and learning beliefs did not
have direct effects on online learning participation and perceptions. The result
corresponded to the correlation results in Table 2, which found no significant
correlation between epistemic and learning beliefs and online learning variables.
6. DISCUSSION
This study examined the relationship between epistemic and learning beliefs,
motivation, learning perception, and students’ actual learning participation in
330 / XIE AND HUANG
asynchronous online learning. The SEM results suggested that the data provided
good support for the overall model, in that the fit statistics were moderately strong
and most of the proposed paths were supported by the empirical findings. More
specifically, the model indicated the following significant causal relationships:
beliefs in omniscient authority, quick learning, and self-efficacy can influence
mastery goals; beliefs in fixed ability and self-efficacy can influence both
performance-approach and performance-avoidance goals; mastery goals can positively influence all online learning variables (i.e., posting participation, nonposting participation, and perceived learning); performance-avoidance goals can
negatively influence non-posting online participation and perceived learning;
self-efficacy can influence both posting and non-posting participation.
6.1 Indirect Influence of Epistemic and Learning Beliefs on Online
Learning
Previous research found that epistemic and learning beliefs were directly and
indirectly related to learning (Cano, 2005; DeBacker & Crowson, 2006; Greene
et al., 2010; Kizilgunes et al., 2009; Schommer-Aikins et al., 2005). The body
of research mainly focused on academic performance that was often reflected
by students’ learning outcomes (e.g., average grades) or self-reported learning
approaches, yet little attention was paid to students’ actual learning behavior.
This study made use of learning analytics as a means to examine the relationship between epistemic and learning beliefs and students’ learning behaviors
in an online learning context. The results indicated that epistemic and learning
beliefs did not directly influence online learning participation and perception,
but indirectly through the mediation of achievement goals. These results are
supported by the existing literature. For example, Buehl and Alexander (2005)
suggested that learner’ epistemic beliefs can be indirectly associated with their
achievement and academic performance through motivation. Hofer and Pintrich
(1997) and Muis (2007) further contended that epistemic beliefs may function
as implicit theories that influence the adoption of goals for learning, and these
goals can serve to mediate the relations between epistemic beliefs, cognition, and
learning performance, as well as learning approach and achievement (Kizilgunes
et al., 2009; Muis & Franco, 2009). Therefore, this study suggested that the
influence of epistemic and learning beliefs on online learning participation is
indirect and implicit, while achievement goals may play more direct roles in
students’ participation in online learning.
It is important to note that the learning participation measured in this study
does not necessarily reflect the quality of students’ participation. Nonetheless, the
participation data served as quantitative indicators of students’ learning engagement in online learning settings. Hence, examining students’ participation behaviors
and patterns provides researchers with a new angle for looking into a facet of
online learning which may have important bearings on students’ performances.
BELIEFS AND MOTIVATION IN ASYNCHRONOUS ONLINE LEARNING /
331
6.2 Influence of Epistemic and Learning Beliefs on Motivation
Previous studies examining the relationship between epistemic and learning
beliefs and motivation were mainly conducted in the context of face-to-face
settings. The findings of this study provide insights into the contribution of
epistemic and learning beliefs to students’ adoption of achievement goals in
online collaborative learning settings. The results denoted that both epistemic
beliefs (i.e., source of knowledge) and learning beliefs (i.e., the speed of
learning, the nature of the ability to learn) significantly predicted students’
achievement goal orientations in online learning. These results are partially
in support of previous research suggesting that naïve beliefs may prevent
learners from investing effort on the mastery of knowledge. Instead, they
pay more attention to satisfying performance requirements (e.g., DeBacker &
Crowson, 2006).
Regarding the relationship between specific learning beliefs systems and
achievement goals, our study suggests that students’ naïve beliefs about quick
learning and fixed ability to learn often lead to the deviation from mastery goals
and the adoption of performance goals. For the beliefs about quick learning,
the results indicate that students who believed that learning occurs quickly were
less likely to adopt mastery goals. The results are in line with previous studies
(e.g., Braten & Strømsø, 2004). Therefore, it appears that the impact of beliefs
about quick learning on achievement goals is similar across face-to-face and
online settings. If a student holds the belief that learning has to take place quickly
or it will never occur, that student is less likely to set a thorough understanding
and mastery of content as his or her goal. Regarding the beliefs about fixed
learning ability, the results suggest that, when students believe that their learning
ability is innate and stable, they will focus more outwardly on performance
goals demonstrating to others or themselves (performance-approach goal), or
showing ego-protective behaviors, such as avoiding situations in which the lack
of knowledge would be revealed (performance-avoidance goal). This finding is
consistent with achievement motivation theories, which suggest that a mastery
goal is characterized by the belief that academic effort will result in an incremental achievement. In contrast, a performance goal reflects the belief that
innate ability alone leads to success (Topping & Ehly, 1998).
Regarding the epistemic beliefs about the source of knowledge, the results
indicate that students who believe that knowledge comes from authority are
more likely to adopt mastery goals in online learning. The results seem to
contradict the general findings in literature that naïve beliefs are often less likely
to result in mastery goal adoption (e.g., DeBacker & Crowson, 2006). The
common agreement in the literature generally holds that the omniscient authority
beliefs about the source of knowledge tend to be naïve while the beliefs that
knowledge emerges from personal construction are more mature and sophisticated (e.g., Hofer & Pintrich, 1997; Schommer-Aikins et al., 2005). The study
332 / XIE AND HUANG
results challenge this general assumption and suggest that not all naïve epistemic
beliefs will lead to lower levels of motivation. This finding supports Greene
et al.’s (2010) suggestion that the utility of beliefs may be context-specific.
In some cases “naïve” beliefs about the source of knowledge may be helpful to
learning. Although the tradition of epistemic beliefs research usually associates
naïve beliefs with negative learning approaches and outcomes, this study suggests
that in the context of online learning, a belief that knowledge comes from authority
may actually lead students’ motivation toward mastery and learning, rather than
performance. To explain this finding, we make the following propositions: as
students’ beliefs about the source of knowledge grow from naïve to mature, it
is possible that their interpretation of the meaning of mastery also evolves from
simple to complex, thereby the mastery goals they adopt also vary from simple
to complex. When students hold the belief that knowledge is handed down
from authority, their understanding about mastery can be simple. In the case of
this study, that is, as students learn in online classes, the beliefs of omniscient
authority may have promoted students’ perception that mastery means learning
from authority sources. Adopting a mastery goal with such a simple understanding, students participated in the class activities because they expected to learn
from their instructor or the learning materials (e.g., the book). Together, the naïve
omniscient authority belief and the associated simple mastery goal may have made
the relationship between these two constructs relatively simpler, clearer, stronger,
and easier to establish. On the other hand, when students hold the belief that
knowledge comes from personal experience and experiment, their understanding
about mastery can become sophisticated. In the case of this study, that is, with a
more sophisticated mastery goal, students participated in the class activities
because they expected to gain competence and master the learning content from
their own knowledge construction by interacting with peers in class. The mature
epistemic belief and the associated sophisticated mastery goal orientation may
have made the relationship between these two constructs relatively more complicated and also ambiguous. Our propositions raise some questions: Does
mastery goal orientation always lead to deep learning strategies and positive
learning performance and outcomes? Will simple or sophisticated mastery goals
impact learning differently? These propositions warrant investigation in future
research on the effects of simple and complex mastery goals on learning variables.
In addition, our study did not find any significant relationship between
epistemic and learning beliefs and self-efficacy, although literature suggests
that several epistemic belief dimensions can influence students’ self-efficacy
(e.g., Paulsen & Feldman, 1999). A possible explanation lies in the difference
in the extent to which the beliefs were contextualized. In the case of this
study, students’ epistemic and learning beliefs were measured at a general level
(i.e., students’ general beliefs about knowledge and learning without a connection to specific domains or tasks), while self-efficacy measures were tied to the
context of particular academic tasks (i.e., their confidence in completing learning
BELIEFS AND MOTIVATION IN ASYNCHRONOUS ONLINE LEARNING /
333
tasks in the online class). This difference may have caused the non-significant
results. Previous studies that found associations between epistemic and learning
beliefs and self-efficacy were both tied to a particular domain, for instance,
science learning (Chen & Pajares, 2010; Kizilgunes et al., 2009). Yet in this
study students’ general epistemic and learning beliefs might not be powerful
enough to directly influence their self-efficacy in the specific learning activities
in these online classes.
6.3 Influence of Motivation on Online Learning
The study results suggested that motivation, being manifested through achievement goals and self-efficacy, played a significant role in predicting students’
perceived learning and their actual learning participation in asynchronous
online discussions.
Mastery goals positively predicted all of the learning participation and perception variables. Previous research has well documented the evidence that
mastery goals can have positive influences on students’ learning strategies, cognitive engagement, learning outcomes, academic achievement (e.g., Eppler &
Harju, 1997; Graham & Golan, 1991; Licht & Dweck, 1984). This study found
new evidence that students with mastery goals were motivated to participate
in asynchronous online learning activities. They had frequent participation in
online discussions (including both posting and non-posting activities), and perceived that they had learned a great deal from the online learning activities. On
the other hand, performance-avoidance goals negatively predicted students’
non-posting behavior and perceived learning in online classes. Students with
performance-avoidance goals tend to focus on avoiding normative incompetence
and its associated unfavorable judgment of potential failure (Elliot & Church,
1997). Research shows that performance avoidance is negatively associated with
academic achievement, and can often lead to avoidance behaviors such as strategic
withdrawal of effort, self-handicapping, and procrastination (Rhodewalt, 1990;
Rothblum, 1990). Therefore, in the case of this study, students who were driven by
the fear of performing poorly in the class would only actively engage in posting
participations so that they wouldn’t show their incompetence or receive a bad
grade in their classes, yet they were less likely to actively engage in non-posting
activities (e.g., reading, evaluation, etc.) because such participations were not
required, not visible to, nor could be graded by teachers or peers. Further,
participating in non-posting activities would not help them to avoid normative
judgment. Consequently, they reported less perceived learning in these online
classes. The study results revealed a significant impact of beliefs on motivation,
therefore suggesting that research should examine the collective impact of beliefs
and motivation on learning, instead of studying them in isolation.
In addition, self-efficacy beliefs played a critical role in these online classes.
They influenced students’ goal adoptions as well as their learning participation.
334 / XIE AND HUANG
This study found that self-efficacy beliefs were positively connected to
both mastery and performance-approach goals while negatively connected to
performance-avoidance goals. The finding is in line with the existing literature,
which suggests that the same relationship between self-efficacy and goal orientations holds true in both face-to-face and online learning settings. Further, the
SEM model enabled the examination of the relationship between self-efficacy
and goal orientations in light of students’ beliefs about fixed ability. Particularly,
for those who believed that one’s ability is fixed and effort does not make a
difference, if they held high self-efficacy beliefs, then they tended to adopt
performance approach goals because they were confident in their capability to
outperform others. Conversely, if their perceived competence was low, they
tended to adopt performance avoidance goals since they were reluctant to demonstrate their incompetency in front of others. This study also found that self-efficacy
positively predicted both posting and non-posting participations. Similar findings
can be found in both classroom research (e.g., Elliot & Church, 1997; Greene
& Miller, 1996; Pintrich & Garcia, 1991) and online learning research (e.g.,
Xie, 2013; Xie & Ke, 2011). With online learning analytic data, the present
study provided new evidences in supporting such relationships.
7. IMPLICATIONS AND LIMITATIONS
This study contributes to the literature by moving beyond the face-to-face
classrooms to investigate the relationships among critical constructs of epistemic
and learning beliefs, motivation, and students’ participation and perception in
authentic online learning settings. Further, this study moved beyond self-reported
methods in educational research and incorporated students’ actual participation
data in the modeling through analytical methods supported by technology. Based
on the findings, we offer a few suggestions for online learning and teaching. First,
online instructors should take care to foster appropriate epistemic and learning
beliefs to help students develop productive goals for learning. In their interactions
with students, instructors should communicate a clear message that learning does
not take place quickly, especially in online learning where it takes time and active
participation to acquire knowledge and master skills. Instructors should also try
to provide affirming feedback to recognize the impact of students’ effort on their
performance. Further, online instructors should not assume a definite connection
between learners’ naïve epistemic beliefs (e.g., the source of knowledge) and
poor motivation and learning participation. Instead, they should recognize that
specific learning contexts may lead to different relationships between epistemic
beliefs and learning. Second, given the findings that epistemic and learning beliefs
influenced students’ online learning participation through their impact on motivation, it may be more efficient for online instructors and instructional designers to
develop interventions that foster students’ motivation. Epistemic beliefs of college
students are often moderately well established and consequently it is relatively
BELIEFS AND MOTIVATION IN ASYNCHRONOUS ONLINE LEARNING /
335
difficult to intervene and promote changes in their beliefs within a class session,
a week, or even a semester (Kienhues, Bromme, & Stahl, 2008; Muis, Bendixen,
& Haerle, 2006). On the other hand, students’ motivation can be contextualized
and malleable. For example, research suggests that students’ motivation toward
academic activities often changes across a semester or academic year (e.g., Stipek
& Ryan, 1997; Wigfield & Eccles, 2000; Xie et al., 2006), and motivation
generally is specific to learning tasks (e.g., Anderman & Wolters, 2006; Schraw
& Lehman, 2001). Hence, while recognizing that students often hold different
beliefs about knowledge and learning, instructors can focus on fostering students’
motivation, which may be more efficient and effective in achieving positive
impact on student learning.
This study has a few limitations. First, in alignment with other similar studies
(e.g., Braten & Strømsø, 2005; DeBacker & Crowson, 2006; Kizilgunes et al.,
2009), this study adopted the trichotomous framework of achievement goals.
Elliot and McGregor (2001) further differentiated mastery goals according to their
valence, thereby creating a 2 × 2 achievement goal framework with approach
and avoidance dimensions for both mastery and performance goals. With the 2 × 2
framework, a replication of this study might be able to explain the relationship
between beliefs and motivation in some learning contexts where mastery approach
and avoidance goals were more prominent. Secondly, this study represents an
initial exploration to utilize learning analytics to explain learning behaviors. This
preliminary exploration only involved the frequencies of posting and non-posting
participation as indicators of students’ learning engagement. Future research may
involve more dimensions of learning analytics (e.g., sequential information and
interaction quality) in order to identify and model learning engagement patterns.
Finally, the learning analytics data from online discussion forums could only
partially reflect students’ interactions yet did not represent their engagement
through face-to-face meetings or offline interactions. Future research should
examine the role of beliefs and motivation in both online and offline engagements
in asynchronous learning settings.
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Direct reprint requests to:
Dr. Kui Xie, Ph.D.
Department of Educational Studies
The Ohio State University
310K Ramseyer Hall
29 West Woodruff Avenue
Columbus, OH 43210
e-mail: xie.359@osu.edu