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Self-regulated learning: the role
of motivation, emotion, and use of
learning strategies in students’ learning
experiences in a self-paced online
mathematics course
a
b
Moon-Heum Cho & Michele L. Heron
a
Department of Education, Sungkyunkwan University, Seoul,
South Korea
b
Click for updates
Curriculum and Instruction, Kent State University at Stark, North
Canton, OH, USA
Published online: 18 Mar 2015.
To cite this article: Moon-Heum Cho & Michele L. Heron (2015) Self-regulated learning:
the role of motivation, emotion, and use of learning strategies in students’ learning
experiences in a self-paced online mathematics course, Distance Education, 36:1, 80-99, DOI:
10.1080/01587919.2015.1019963
To link to this article: http://dx.doi.org/10.1080/01587919.2015.1019963
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Distance Education, 2015
Vol. 36, No. 1, 80–99, http://dx.doi.org/10.1080/01587919.2015.1019963
Self-regulated learning: the role of motivation, emotion, and use of
learning strategies in students’ learning experiences in a self-paced
online mathematics course
Moon-Heum Choa* and Michele L. Heronb
a
Department of Education, Sungkyunkwan University, Seoul, South Korea; bCurriculum and
Instruction, Kent State University at Stark, North Canton, OH, USA
Downloaded by [Ryerson University] at 05:26 27 April 2015
(Received 27 August 2014; final version received 12 February 2015)
Enrollment in online remedial mathematics courses has increased in popularity
in institutions of higher learning; however, students unskilled in self-regulated
learning (SRL) find online remedial mathematics courses particularly challenging. We investigated the role of SRL, specifically motivation, emotion, and
learning strategies, in students’ learning experiences in a remedial online mathematics course. With an online survey of 229 college students, we found that student motivation explained a small portion of variance in achievement; whereas
student motivation and emotion explained a significant portion of variance in satisfaction. In addition, significant differences in motivation and emotion were
found in passing and nonpassing students; however, learning strategies did not
influence student achievement and satisfaction. Implications for teaching and
learning in self-paced online remedial mathematics courses are discussed.
Keywords: self-regulated learning; motivation; emotion; learning strategies;
remedial online mathematics
Introduction
Higher education requires students to have a certain level of mathematical competence to pursue their academic degrees (National Mathematics Advisory Panel
[NMAP], 2008); however, pervasive deficits in mathematical skills have been
reported in higher education in the United States (Kim & Hodges, 2012; NMAP,
2008). Consequently, approximately 71% of institutions of higher education, including four-year colleges and community colleges in the United States, offer remedial
mathematics courses (National Center for Education Statistics, 2003). Unfortunately,
researchers have reported a consistently low success rate among students in remedial
mathematics courses (Carmichael & Taylor, 2005; Kim & Hodges, 2012).
Increasing numbers of institutions of higher education have offered remedial
mathematics courses online to accommodate working students’ needs and to reduce
their budgets, expecting that underprepared students will acquire necessary skills
and knowledge to complete their academic degree programs (Brants & Struyven,
2009; Rienties et al., 2012); however, online remedial mathematics courses have
been problematic for many students (Kim & Hodges, 2012). Students reported lack
of motivation and low self-efficacy for learning and frequently experienced negative
*Corresponding author. Email: mhcho@skku.edu
© 2015 Open and Distance Learning Association of Australia, Inc.
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81
emotions, such as anger, boredom, and frustration, which interrupted their engagement in learning (Kim & Hodges, 2012). Such problems may contribute to a pattern
of significantly lower completion rates in online remedial courses than in face-toface remedial courses (Xu & Jaggars, 2011). One of the possible reasons for low
completion rates in remedial mathematics courses online is the additional challenge
posed by the lack of social interaction and less than immediate feedback from the
instructor (Kim, Park, & Cozart, 2014).
Research has shown that self-regulation is critical in determining students’ successful learning experiences in an online learning environment (Cho & Kim, 2013).
That self-regulated learners set goals, plan ahead, and consistently monitor and
reflect on their learning process is well known. They effectively manage their time
and learning resources (Pintrich, 2004; Zimmerman, 2011) and persist in a challenging learning context; therefore, student self-regulation is important in determining
successful learning experiences in an online remedial mathematics course.
Little empirical research has been conducted in remedial online mathematics
courses with regard to self-regulated learning (SRL). Kim et al. (2014) found that
SRL is an important predictor of high school students’ achievements in remedial
online courses. They found that the subcomponents of SRL, such as motivation and
emotion, explained 37% of the final scores in an online remedial mathematics
course. Although their research is helpful in understanding high school students’
self-regulation in an online mathematics course, more empirical research is necessary in college settings. Because the majority of remedial mathematics courses are
offered in higher education, research in college settings would be helpful in understanding the role of SRL in college mathematics courses offered online; therefore,
the authors investigated how SRL explains students’ learning experiences in an
online remedial mathematics course.
Literature review
SRL and its subcomponents
SRL refers to students’ systematic effort to manage their learning process to achieve
goals (Pintrich, 2004; Zimmerman & Schunk, 2011). Often, SRL is explained with
motivation, emotion, and learning strategies (Abar & Loken, 2010). Motivationally,
self-regulated learners have mastery goal orientation or a tendency to seek to
develop competencies by mastering skills or tasks, are confident about their ability
to learn, and highly value the learning tasks (Pintrich, 2004; Zimmerman, 2011).
Several empirical studies reported the role of motivation in online SRL. For example, Cho and Kim (2013) found that students’ mastery-oriented goals are positively
related to their self-regulation for interaction in online learning environments. In
addition, Cho and Shen (2013) found online students’ intrinsic goal orientation, or
disposition to master the content, is positively related to their self-efficacy for learning and performance as well as metacognitive self-regulation in an asynchronous
online learning environment.
Emotionally, self-regulated learners have positive emotions, including hope,
enjoyment, and pride in learning (Pekrun, 2006; Pekrun, Goetz, Daniels, Stupnisky,
& Perry, 2010). They control and regulate negative emotions, such as anger, anxiety,
boredom, and frustration. A series of studies have demonstrated the role of emotion in
SRL (Pekrun et al., 2010; Pekrun, Goetz, Titz, & Perry, 2002). Pekrun et al. (2010)
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M.-H. Cho and M.L. Heron
found that students’ negative emotion, such as boredom, is negatively related to
attention, intrinsic motivation, effort, self-regulation of learning, and academic
performance. In addition, Pekrun et al. (2002) found negative relationships between
negative emotions, such as anger, anxiety, and boredom on one hand, and level of
interest, effort, elaboration, and self-regulation on the other; but they found positive
relationships between negative emotions on one hand and irrelevant thinking and
external regulation on the other. Although little research has been conducted, similar
research findings were reported in online learning environments. Kim and Hodges
(2012) found positive relationships between positive emotions and motivation but
negative relationships between negative emotions and motivation in online learning
environments. Positive emotions include enjoyment and pride. Negative emotions
include boredom, anxiety, anger, shame, and hopelessness.
With regard to learning strategies, when challenged, self-regulated learners apply
deeper level learning strategies such as metacognitive or critical thinking strategies
to achieve goals (Boekaerts & Corno, 2005; Pintrich, 2004; Winne, 2001;
Zimmerman, 2011). They attribute their failure in achieving goals to unsuccessful
implementation of learning strategies and adjust their learning strategies through
reflection (Pintrich, 2004; Winne, 2001; Zimmerman, 2011). Numerous researchers
(Abar & Loken, 2010; Cho & Shen, 2013; Pintrich, 1999; Zimmerman & MartinezPons, 1988) found that skillful self-regulated students used metacognitive learning
strategies represented with planning, monitoring, and evaluating more often than less
skillful self-regulated students. Similar results were found in a Web-based learning
environment. Azevedo, Moos, Greene, Winters, and Cromley (2008) found that students who used more metacognitive strategies, such as monitoring their thinking
process, performed significantly better than students who used fewer metacognitive
strategies in Web-based learning environments.
Role of subcomponents of SRL in students’ online learning experiences
In this study, we viewed students’ online learning experiences in terms of three
aspects of learning outcomes: achievement, course satisfaction, and passing or nonpassing. Achievement is an important aspect of online students’ learning experience
in mathematics courses; for example, Kim et al. (2014) used the subcomponents of
SRL, specifically motivation, emotion, and learning strategies, as predictors for
achievement and found that emotions such as boredom, enjoyment, and anger significantly predicted students’ achievement in a self-paced online mathematics course.
In addition, Kramarski and Gutman (2006) found that metacognitive strategy support in online learning environments is closely related to student achievement in
mathematics.
Another learning experience often measured in online learning is course satisfaction. A significant portion of online studies show that the subcomponents of SRL
are related to students’ satisfaction with online course. For example, Artino (2009b)
found that motivation variables, such as self-efficacy and task value, are positive
predictors of course satisfaction; whereas negative emotional variables, such as
boredom and frustration, are negatively related to students’ course satisfaction in a
self-paced military course. In another study, Artino (2008) found that motivational
variables, including task value and self-efficacy, positively explain 43.4% of variance in course satisfaction in a self-paced online military training course along with
demographic variables, including age and the number of online courses taken.
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The other learning outcome in this study involves passing and nonpassing.
Because one of the most important reasons students take the online remedial mathematics course is to pass the course, we considered passing and nonpassing important
learning experience. In our review of literature, we were unable to find any empirical studies that compared the subcomponents of SRL in passing and nonpassing students in remedial online courses; therefore, the findings in our study will contribute
to understanding the difference in the subcomponents of SRL in passing and nonpassing students.
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Research questions
The overarching goal of the study was to examine the role of the subcomponents of
SRL, specifically motivation, emotion, and learning strategies, in online students’
learning experiences. More specifically, the research questions were as follows:
(1) To what extent do the subcomponents of SRL predict student achievement?
(2) To what extent do the subcomponents of SRL predict student online course
satisfaction?
(3) Do any differences exist in the subcomponents of SRL in passing and nonpassing students?
Methodology
Participants
A total of 229 students enrolled in self-paced remedial online mathematics courses
participated in the study: 83 males and 146 females. The average age of the participants was 21.64 (SD = 7.11). Most students were freshmen (N = 170, 74.2%) and
identified themselves as Caucasian (N = 183, 79.9%). In addition, most had no previous experience in a self-paced online mathematics course (N = 180, 78.6%). Furthermore, 70.3% (N = 161) of the students reported that they were employed and
worked on average 16.63 h (SD = 13.21) per week. See Table 1 for detailed demographic information.
Context or learning environments
Students were placed into one of four levels of the remedial online courses. Because
these courses were required prior to enrolling in college-level mathematics courses,
their timely completion was necessary. All the learning materials for the course were
online, including practice problems, e-book, mid-term tests, and final exams. The
Assessment and LEarning in Knowledge Spaces (ALEKS) system (see http://www.
aleks.com/about_aleks) was used for the main content delivery system. By using the
ALEKS system, students were able to choose time and place to study at their own
pace; however, information was presented in a linear way to help them solve certain
designated types of mathematical problems. Although students were allowed to skip
certain topics if they so desired, most tended to learn linearly because they could
not solve the problems without connecting knowledge; therefore, the ALEKS system
provided a high level of learner control for choosing time and place for self-paced
learning while supporting a low level of learner control for the ways students learn
the content.
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M.-H. Cho and M.L. Heron
Table 1. Demographic information for participants.
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Demographic variables
Gender
Male
Female
Ethnicity
African/African American
American Indian/Alaskan Native
Asian, Asian-American or Pacific Islander
Latino/Latino American
Caucasian/Caucasian American
Other
Grade level
Freshmen
Sophomore
Junior
Senior
Other
Total
N
%
83
146
36.2
63.8
26
3
3
2
183
12
11.4
1.3
1.3
.9
79.9
5.2
170
31
12
13
3
229
74.2
13.5
5.2
5.7
1.3
Although all the course materials were delivered through ALEKS, a class session
twice a week was available for those students who wanted to attend a traditional class
with a teacher assigned to approximately 40 students. In the classroom, students
learned the material via ALEKS, and teachers worked individually with students if
requested; therefore, the interaction between students and teacher tended to be limited
to explanations of specific problem types suggested by individual students. In addition, students were allowed to discuss the mathematics problems with other classmates if they wanted to. The mid-term and final exams were to be completed in class.
No points were given for class attendance, and each course lasted 7.5 weeks. A grade
of 73% or better on the comprehensive final exam was required to pass the course.
Measures
Several instruments were used to measure the subcomponents of SRL, specifically
students’ motivation, emotion, and learning strategies as well as course satisfaction
(see Appendix 1). More specific information appears below.
Motivation
Motivation included intrinsic goal orientation (N = 4), task value (N = 6), and selfefficacy for learning and performance (N = 8). All the motivation items came from
the motivated strategies for learning questionnaire (MSLQ) (Pintrich, Smith, Garcia,
& Mckeachie, 1993). Cronbach’s alphas with our samples were .77 for intrinsic goal
orientation, .86 for task value, and .94 for self-efficacy for learning and performance.
Seven-point Likert scales were used, in which 1 indicated not at all true of me and
7 indicated very true of me.
Emotion
Emotion included test anxiety (N = 4), boredom (N = 5), and frustration (N = 4). We
chose the three emotional variables that we thought most relevant to remedial online
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mathematics courses. Items on test anxiety derived from the MSLQ, and those on
boredom and frustration were adapted (Artino, 2009a) from the achievement emotions questionnaire (Pekrun, Goetz, & Perry, 2005). Cronbach’s alphas with our samples were .79 for test anxiety, .92 for boredom, and .92 for frustration. Seven-point
Likert scales were used, in which 1 indicated not at all true of me and 7 indicated
very true of me.
Learning strategies
Learning strategies included metacognitive self-regulation (N = 12) and critical
thinking strategies (N = 5). Items for metacognitive self-regulation and critical thinking strategies came from the MSLQ. Items were slightly changed to best represent
an online learning environment. For example, the original wording “I try to think
through a topic and decide what I am supposed to learn from it rather than just reading it over when studying for this course” was changed to “I try to think through a
concept and decide what I am supposed to learn from it rather than just practicing
the problems when studying for this online course.” Cronbach’s alphas with our
samples were .82 for metacognitive self-regulation and .79 for critical thinking strategies. Seven-point Likert scales were used, in which 1 indicated not at all true of
me and 7 indicated very true of me.
Satisfaction
Satisfaction was measured with three items adapted from Artino’s (2009a) research.
Wording was changed to accommodate ALEKS, the online remedial mathematics
system. For example, “Overall, I was satisfied with my online learning experience”
was changed to “Overall, I was satisfied with my ALEKS course experience.” Cronbach’s alpha with our samples was .93 for course satisfaction. Seven-point Likert
scales were used, in which 1 indicated not at all true of me and 7 indicated very true
of me.
Procedures
The research was approved by the Institutional Review Board and conducted ethically. In the middle of the course semester, the authors of the study, who visited
self-paced online courses with the permission of the instructors, introduced the purpose of the study, research procedures, possible risks, and contact information to students; and then administered the online survey during class time. Participation in the
research was completely voluntary, and no incentive was given for participation.
Also, no penalty was given for nonparticipation. Students were able to quit the
online survey at any time without any penalty.
Results
Descriptive statistics
Table 2 presents means and standard deviations for the subcomponents of SRL, specifically motivation, emotion, and learning strategies as well as course satisfaction
and achievements. Means for intrinsic goal orientation, task value, self-efficacy for
learning were 4.38 (SD = 1.21), 4.43 (SD = 1.32), and 4.70 (SD = 1.38), respectively.
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M.-H. Cho and M.L. Heron
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Means for negative emotion, such as test anxiety, boredom, and frustration, were 3.91
(SD = 1.42), 3.78 (SD = 1.77), and 3.32 (SD = 1.90), respectively. Means for metacognitive self-regulation and critical thinking strategies were 4.36 (SD = .99) and
4.21 (SD = 1.30), respectively. In addition, means for course satisfaction and achievements were 3.99 (SD = 1.82) and 72 (SD = 17.24), respectively.
Pearson correlations
Pearson correlations indicate that students’ final grades in a self-paced online mathematics course were positively correlated with motivation, including task value
(r = .15, p < .05) and self-efficacy for learning (r = .29, p < .01); whereas final
grades were negatively related with negative emotions, including test anxiety
(r = −.24, p < .01), boredom (r = −.16, p < .05), and frustration (r = −.24, p < .01).
Intrinsic goal orientation, metacognitive self-regulation, and critical thinking were
not related with students’ final grades.
Another analysis of Pearson correlations showed that students’ satisfaction with
the online mathematics course was positively related with motivation, such as intrinsic goal orientation (r = .47, p < .01), task value (r = .62, p < .01), self-efficacy for
learning (r = .70, p < .01), and learning strategies, including metacognitive self-regulation (r = .31, p < .01) and critical thinking (r = .33, p < .01); however, negative
emotions were negatively related with satisfaction, including test anxiety (r = −.45,
p < .01), boredom (r = −.52, p < .01), and frustration (r = −.69, p < .01).
Regression analysis
A three-step hierarchical regression analysis was conducted to investigate the relationships between final grades and the subcomponents of SRL, specifically motivation, emotion, and learning strategies, as shown in Table 3. In step 1, motivation
variables were entered into the model, followed by emotion in step 2, and learning
strategies in step 3. The overall model with the subcomponents of SRL explained
11.9% of the variance in final grades in online mathematics courses F(8, 220)
= 3.71, p < .01. In step 1, intrinsic goal orientation, task value, and self-efficacy for
learning explained 9.1% of the variance in students’ final grades, F(3, 225) = 7.52,
p < .001. Self-efficacy for learning was the only variable that significantly predicted
final grades among motivational variables (β = .36, p < .01). In step 2, motivation
and emotion significantly explained 10.5% of variance in students’ final grade F(6,
222) = 4.33, p < .001; however, emotions, including test anxiety, boredom, and
frustration, did not predict any variance in final grade. Only self-efficacy for learning
in motivation significantly explained final grades (β = .28, p < .01). In step 3, motivation, emotion, and learning strategies explained 11.9% of variance in final grades,
F(8, 220) = 3.70, p < .001; however, emotion and learning strategies did not contribute any variance in explaining final grades. Only self-efficacy for learning significantly and consistently contributed to explaining students’ final grades (β = .30,
p < .01).
Another three-step hierarchical regression analysis was conducted to investigate
the relationships between course satisfaction and the subcomponents of SRL (see
Table 4). The overall model with the subcomponents of SRL explained 63.1% of
the variance in satisfaction with online mathematics courses F(8, 102) = 26.05,
p < .01. In step 1, intrinsic goal orientation, task value, and self-efficacy for learning
Note: 7-point Likert scales were used.
*p < .05; **p < .01.
1. Intrinsic goal orientation
2. Task value
3. Self-efficacy for learning
4. Test anxiety
5. Boredom
6. Frustration
7. Metacognitive self-regulation
8. Critical thinking
9. Satisfaction
10. Final
4.38
4.43
4.70
3.91
3.78
3.32
4.36
4.21
3.99
72.00
M
1.21
1.32
1.38
1.42
1.77
1.90
.99
1.30
1.82
17.24
SD
.77
.86
.94
.79
.92
.92
.82
.79
.93
α
1
.74**
.60**
−.26**
−.39**
−.48**
.50**
.53**
.47**
.12
1
1
.67**
−.23**
−.53**
−.53**
.51**
.50**
.62**
.15*
2
1
−.53**
−.37**
−.64**
.30**
.38**
.70**
.29**
3
.29**
.55**
−.01
−.03
−.45**
−.24**
1
4
.59**
−.43**
−.21**
−.52**
−.16*
1
5
Table 2. Descriptive statistics, Cronbach’s alphas and Pearson correlations for the subcomponents of SRL.
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1
−.28**
−.23**
−.69**
−.24**
6
1
.74**
.31**
−.01
7
1
.33**
−.03
8
1
.26**
9
1
10
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88
M.-H. Cho and M.L. Heron
Table 3. Model summaries for hierarchical regression analysis predicting achievement with
a self-paced online course.
Step 1
B
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Step 1: Motivation
Intrinsic goal
orientation
Task value
Self-efficacy for
learning
SE
β
−1.04 1.37 −.07
−.43 1.35 −.03
4.47 1.09
.36***
Step 2: Emotion
Test anxiety
Boredom
Frustration
Step 3: Learning
strategies
Metacognitive selfregulation
Critical thinking
Model summary
statistics
R2
F value for model
df for model
R2 change
F ratio for R2
change
Step 2
B
SE
Step 3
β
−1.12 1.38 −.08
B
SE
β
−.36 1.44 −.03
−.68 1.48 −.05
3.49 1.33
.28**
−.26 1.50 −.02
3.69 1.33
.30**
−.99
−.61
−.41
−.72 1.00 −.06
−.59 .89 −.06
−.37 .91 −.04
.99 −.08
.83 −.06
.92 −.05
−.19 1.86 −.01
−1.82 1.38 −.14
.091
7.523***
3, 225
.091
7.523***
.105
4.330***
6, 222
.014
1.124
.119
3.701***
8, 220
.014
1.731
**p < .01; ***p < .001.
explained 53.9% of the variance in students’ satisfaction, F(3, 225) = 87.73,
p < .001. Task value (β = .33, p < .001) and self-efficacy for learning (β = .54,
p < .001) were the variables that significantly predicted final grades. In step 2, motivation and emotion variables explained 62.6% of variance in satisfaction. Among
the variables, task value (β = .24, p < .01), self-efficacy for learning (β = .37,
p < .001), boredom (β = −.12, p < .05), and frustration (β = −.29, p < .001)
explained a statistically significant variance in satisfaction. In step 3, motivation,
emotion, and learning strategies explained 63.1% of variance; however, learning
strategies, including metacognitive self-regulation, and critical thinking, did not
explain a statistically significant variance in students’ satisfaction. Among variables,
intrinsic goal orientation (β = −.14, p < .05), task value (β = .22, p < .01), self-efficacy for learning (β = .34, p < .01), boredom (β = −.14, p < .05), and frustration
(β = −.29, p < .01) explained a statistically significant variance in satisfaction.
Mean comparison
Of the 229 students, 141 students passed the course, and 88 students did not. In
order to pass, they were required to achieve more than 73% on their final exam. We
conducted the independent samples t test to compare the subcomponents of SRL in
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Table 4. Model summaries for hierarchical regression analysis predicting overall satisfaction
with a self-paced online course.
Step 1
B
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Step 1: Motivation
Intrinsic goal
orientation
Task value
Self-efficacy for
learning
Step 2
β
SE
B
−.15 .10 −.10
.45 .10
.72 .08
β
SE
−.18 .09 −.12
.33***
.54***
Step 2: Emotion
Test anxiety
Boredom
Frustration
Step 3: Learning
strategies
Metacognitive selfregulation
Critical thinking
Model summary
statistics
R2
F for model
df for model
R2 change
F ratio for R2 change
Step 3
.32 .10
.49 .09
.24**
.37***
B
β
SE
−.21 .10 −.14*
.31 .10
.47 .09
.22**
.36***
−.04 .07 −.03
−.05 .07 −.04
−.12 .06 −.12*
−.15 .06 −.14*
−.28 .06 −.29*** −.28 .06 −.29***
−.13 .13 −.07
.16 .10
.539
87.733***
3, 225
.539
87.733***
.626
61.978***
6, 222
.087
17.234***
.11
.631
46.997***
8, 220
.005
1.394
*p < .05; **p < .01; ***p < .001.
Table 5. Comparison of the subcomponents of SRL and satisfaction in passing and nonpassing groups.
Passing
(N = 141)
Motivation
Intrinsic goal orientation
Task value
Self-efficacy for learning
Emotion
Test anxiety
Boredom
Frustration
Learning strategies
Metacognitive self-regulation
Critical thinking
Satisfaction
Nonpassing
(N = 88)
M
SD
M
SD
t
Sig.
4.50
4.61
4.95
1.21
1.34
1.36
4.12
4.15
4.30
1.20
1.25
1.33
1.97
2.56
3.57
.050
.011*
.000***
3.70
3.54
2.92
1.41
1.78
1.85
4.26
4.17
3.95
1.38
1.71
1.82
−2.99
−2.64
−4.11
.003**
.009**
.000***
4.40
4.21
4.37
.99
1.30
1.75
4.31
4.21
3.39
.99
1.30
1.77
.70
.02
4.11
.487
.981
.000***
*p < .05; **p < .01; ***p < .001.
passing and nonpassing students. The results in Table 5 show a significant difference
between those students who passed and those students who did not pass in terms of
task value, self-efficacy, test anxiety, boredom, and frustration. More specifically,
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students who understood task value and students with higher self-efficacy for learning were more likely to pass the course than students who do not possess those qualities. Similarly, students who were not as bored or frustrated or who had low test
anxiety would be more likely to pass; however, no significant difference was found
between the groups with regard to the use of metacognitive self-regulation or critical
thinking strategies.
Discussion
The purpose of this study was to examine the role of the subcomponents of SRL in
students’ learning experiences in remedial self-paced online mathematics courses.
We found that motivational and emotional variables significantly predicted students’
achievement and satisfaction; whereas cognitive strategies did not predict achievement and satisfaction. More specifically, our study results demonstrate that only
motivation – in particular self-efficacy for learning – significantly contributed to
explaining 11.9% of the variance in achievement. The finding supports the social
cognitive view of learning that self-efficacy is a strong predictor of student achievement (Bandura, 1986; Pajares, 1997; Zimmerman, 2011). Given that the course is
remedial, students do not arrive with a strong base of knowledge of the content;
therefore, they may not have much confidence about their success in the course, and
consequently the level of confidence about their success significantly influences their
achievement.
In addition, we found that only motivational and emotional variables significantly predicted 63.1% of the variance in satisfaction. Intrinsic goal orientation is
negatively related to students’ satisfaction with the course; whereas task value and
self-efficacy for learning are positively associated with course satisfaction. More
specifically, students who are intrinsically motivated are apparently dissatisfied with
a self-paced remedial online mathematics course (Artino, 2009a; Kim et al., 2014).
Although the ALEKS system promises a high level of learner control in that students can decide time and place to study, it provides a low level of learner control in
ways to learn the course materials. Because the current format of the ALEKS system
presents the information in quite a linear way and emphasizes mechanical solutions
to mathematics problems, students have little flexibility in learning topics on their
own. Lack of learner control in ways to learn the content may lead intrinsically oriented students to dissatisfaction with the online course (Scheiter & Gerjets, 2007).
Moreover, unsurprisingly, students who value the task and have high self-efficacy
for learning are likely to find satisfaction with the online course. The findings of the
current study support previous research results: Artino (2008) found that task value
and self-efficacy for learning positively predict students’ satisfaction with an online
course.
Emotion plays an important role in explaining student satisfaction. Our study
shows students who felt bored and frustrated with the course were not likely satisfied with the course. Our findings support previous research indicating that emotions
predict student learning behaviors (Pekrun et al., 2010). More specifically, Pekrun
et al. (2010) found that student boredom is positively associated with their attention
problems and negatively related to motivation (e.g., intrinsic motivation), learning
strategies (e.g., effort, elaboration, and self-regulation), and academic performance;
therefore, reducing students’ negative emotion in a remedial online mathematics
course is important because satisfaction is a critical variable that influences a
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student’s decision to take another online course (Kuo, Walker, Schroder, & Belland,
2014).
Significant differences in motivation and emotion were found in passing and
nonpassing students. Students who passed the course reported significantly higher
task value and self-efficacy for learning. Emotionally, students who passed the
course showed lower anxiety, boredom, and frustration than students who did not
pass. Considering that skillful self-regulated learners (a) performed better than less
skillful students, (b) demonstrated higher self-efficacy and task value, and (c) controlled negative emotions in learning (Abar & Loken, 2010; Artino, 2007; Azevedo
et al., 2008; Boekaerts & Corno, 2005; Kuo et al., 2014; Pekrun et al., 2010;
Pintrich, 2004; Winne, 2001; Zimmerman, 2011), our research findings add
empirical evidence to the body of SRL research.
No significant difference in the use of learning strategies by passing and nonpassing students was found. In addition, learning strategies did not contribute to
explaining student achievement and satisfaction in this study. Kim et al. (2014)
found similar results in that cognitive strategy and metacognitive self-regulation did
not contribute to explaining student achievement in a remedial online mathematics
course. A possible explanation is that the ALEKS system presented information in a
linear way, which emphasized mechanical solutions to mathematics problems; therefore, students can succeed if they simply follow the instructions presented. In addition, because the likely goal of many students in a remedial mathematics course is
to pass the course instead of achieving a high grade, students may minimize their
efforts in their use of metacognitive self-regulation and critical thinking strategies.
Suggestions for teaching and learning practice
Intervention strategies are necessary to help students succeed in remedial online
mathematics courses. We propose several suggestions to improve students’ motivation, cognition, and emotion in self-paced online mathematics courses.
Enhance students’ self-efficacy about learning
Self-efficacy for learning is a key factor in successful online mathematics courses in
terms of achievement and satisfaction (Artino, 2009a, 2009b). The link between
self-efficacy and achievement is unsurprising in this type of setting, in which individuals are expected to work independently to set goals, practice content, and successfully complete the course. One of the possible ways to help students improve
self-efficacy is through social interaction between students and the instructor
(Bandura, 1986; Zimmerman, 2011). For example, the instructor helps students set
short-term and manageable goals to support them as they build confidence as the
course progresses. Students are likely to achieve short-term and manageable goals,
which will lead them to feel more confident about their learning in the short term
(Cho & Shen, 2013). Readers should be aware, however, that the improvement of
self-efficacy through social interaction is more feasible in a small, blended-learning
course such as the one in which the current study was conducted than in a large
online course.
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M.-H. Cho and M.L. Heron
Design supporting tools in online courseware
If large numbers of students are to undertake these remedial programs online,
designing supporting tools in online courseware would be beneficial. For example,
critical questions for students to ask themselves and answer on their own could be
embedded in the guidance notes. Kramarski and Gutman (2006) found students in a
metacognitive supported online mathematics course performed better than students
in a control group. Azevedo et al. (2008) also found students who received external
promptings by tutors performed significantly better than students who did not
receive them in a Web-based learning course. System-generated prompting that
causes students to use a deep level of learning strategies is, therefore, suggested. In
addition, video clips could be included, featuring former students explaining how
they set short-term goals for themselves, boosted their motivation, and discovered
SRL methods with which they were comfortable. Kim and Hodges (2012) had students watch an online video explaining how to control and regulate emotional difficulties while taking a self-paced remedial mathematics course. Kim and Hodges
compared students’ emotion and motivation in a control and an experimental group
and found that students who were trained by watching the video reported higher
positive emotions, such as enjoyment and pride as well as higher motivation, in the
self-paced remedial course than students in the control group. These methods could
also be used to show how the motivational, emotional, and cognitive aspects of
learning mathematics can be better understood and managed by learners.
Provide an orientation to the course
Providing an orientation prior to the beginning of the course could help students
acclimatize to the course requirements, general resources, and software resources
(Cho, 2012; Lee & Choi, 2011). The orientation reduces potential confusion and
emotional frustration once the course begins, allowing students to focus on contentspecific challenges instead of the self-paced online environment. The orientation
should include demonstration of resources, such as video tutorials, access and
maneuvering through the e-book, and location of example problems that demonstrate both solutions and procedures. In addition to the ALEKS resources, the orientation should also address the resources available on campus, such as the classroom
teacher, office hours, additional lab time with other teachers, peer support, and the
tutoring center.
Provide SRL support through social media
Social media can be used to promote motivation, positive emotion, and learning
strategies (Cho & Cho, 2013). In a self-paced online mathematics course students
may feel less motivated, more isolated, and alone and may use inappropriate learning strategies. Providing social interaction opportunities through social media is a
means to helping students collaboratively control and regulate motivation, emotion,
and learning strategies. Cho and Cho (2013) found that through social media like
Twitter, college students regulate learning motivationally, emotionally, and cognitively. By using social media they posted not only their own methods of self-regulation but also supported one another’s regulation processes. We suggest that
instructors create a virtual community through social media in which students share
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motivation, emotions, and learning strategies related to the course and socially regulate their learning.
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Restructure the format of the course
A possible format in a blended remedial online course would be to flip the class
(Love, Hodge, Grandgenett, & Swift, 2014). In this format, students work on the
remedial course independently at home in a self-paced online learning environment,
and then class time is spent working in small groups or as a whole group on challenging mathematical ideas. Because the ALEKS provides information to the
instructor about where the class is struggling, focus groups can be established to
work on specific content needs. In this way, students would be able to connect with
other students and the content.
Limitations of the study
Several limitations should be carefully considered when readers interpret the results
of this study. These limitations also can be considered in conducting future studies.
First, the survey was administered in the middle of the course (from the 4th to 5th
weeks) instead of at the end of the semester (from the 7th week to the middle of the
7th week). We did so for two reasons: First, because students take their final course
exam during the middle of the 7th week, we did not want to interrupt their final
exam effort. Second, students’ class attendance rates typically decrease as semesters
draw to a close. We understand, however, that student satisfaction may change as
the course proceeds; so we suggest that future researchers pursuing goals similar to
ours administer the survey at the end of the semester to collect more accurate data.
Second, in order to identify the role of emotion in online remedial mathematics
courses, we used only negative emotional variables such as test anxiety, boredom,
and frustration that we thought were most relevant to our study context. However,
positive emotional variables including enjoyment, hope, and pride also found to be
related to students’ learning strategies including elaboration, effort regulation, and
metacognitive regulation (Pekrun, Goetz, Frenzel, Barchfeld, & Perry, 2011). We
suggest that future researchers use both positive and negative emotional variables to
examine roles of emotion in remedial online mathematics courses.
Significance of the study
Despite the limitations of the study, the current research is important in the field of
online learning research for several reasons. First, we included emotions as one of the
subcomponents of SRL to understand students’ learning experiences in a self-paced
online mathematics course. SRL is explained with multidimensional constructs, primarily motivation and learning strategies (Pintrich, 1989; Winne, 2001; Zimmerman,
2011). Many SRL researchers have examined how motivational variables predict the
use of learning strategies (Pintrich, 1989, 1999) or how motivation and learning strategies are related to student achievement (Vrugt & Oort, 2008; Yukselturk & Bulut,
2007; Zimmerman & Martinez-Pons, 1988). Recently, researchers have agreed that
emotion plays an important role in student learning, not only in face-to-face (Pekrun
et al., 2010) but also in online learning environments (Artino, 2009a, 2009b; Kim
et al., 2014). Increasing numbers of researchers have argued that SRL is explained
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with motivation, emotion, and learning strategies (Boekaerts & Corno, 2005; Pekrun,
2006). The current research contributes to the body of research explaining SRL with
motivation, emotion, and use of learning strategies.
Second, we investigated students’ learning experiences from multiple angles. A
significant portion of online research used involved only one dependent variable,
such as online course satisfaction (Kuo et al., 2014); however, we used three dependent variables to understand students’ learning experiences from multiple perspectives. These variables consisted of achievement and satisfaction as well as passing
and nonpassing. Our study results show how the subcomponents of SRL (motivation, emotion, and learning strategies) influence student learning experiences differently, indicating the value of a holistic view of learning in online remedial
mathematics courses.
Conclusion
The current study adds empirical evidence to the existing body of literature on SRL
in remedial online mathematics courses, showing that motivation and emotion significantly influence student learning experiences, including achievement, satisfaction,
and passing vs. nonpassing; whereas the use of learning strategies did not. In addition, the study contributes to expanding our understanding about reasons students
enrolled in online remedial mathematics courses often fail. The study demonstrates
that motivational and emotional supports are necessary to enhance students’ success
rate in online remedial courses. More empirical research investigating the role of
SRL in remedial online mathematics courses will extend SRL theories to online
environments and improve efforts to enhance student success in online courses more
generally.
Notes on contributors
Moon-Heum Cho is an associate professor in the Department of Education at Sungkyunkwan
University in South Korea. His primary research interests include SRL in technology-mediated learning environments, data science, supporting co-regulation in computer supported collaborative learning, and technology integration in classrooms.
Michele L. Heron is an instructor in the Department of Teaching, Learning, and Curriculum
Studies at Kent State University at Stark. Her primary research is SRL and sociomathematical
norms in mathematics classes.
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Appendix 1. Questionnaire
The questionnaire utilized to measure motivation, cognition, learning strategies, and satisfaction. Specific questions are presented below.
Motivational variables (adapted from the MSLQ: Pintrich et al., 1993)
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Intrinsic goal orientation
(1) In this ALEKS course, I prefer course material that really challenges me so I can
learn new things.
(2) In a class like this, I prefer course material that arouses my curiosity, even if it is difficult to learn.
(3) The most satisfying thing for me in this course is trying to understand the content as
thoroughly as possible.
(4) When I have the opportunity in this ALEKS class, I choose course content that I can
learn even if it is not easy to understand.
Task value
(1)
(2)
(3)
(4)
(5)
(6)
I think I will be able to use what I learn in this course in other courses.
It is important for me to learn the course material in this ALEKS class
I am very interested in the content area of this course.
I think the course material in this class is useful for me to learn.
I like the subject matter of this ALEKS course.
Understanding the subject matter of this course is very important to me.
Self-efficacy for learning
(1) I believe I will receive an excellent grade in this class.
(2) I’m certain I can understand the most difficult content presented in the ALEKS materials for this course.
(3) I’m confident I can learn the basic concepts taught in this ALEKS course.
(4) I’m confident I can understand the most complex material presented through ALEKS
in this course.
(5) I’m confident I can do an excellent job on the practice problems and tests in this
ALEKS class.
(6) I expect to do well in this ALEKS class.
(7) I’m certain I can master the knowledge taught in this ALEKS class.
(8) Considering the difficulty of this ALEKS course, the teacher, and my skills, I think I
will do well in this class.
Emotional variables
Test anxiety (adapted from the MSLQ: Pintrich et al., 1993)
(1) When I take a test in ALEKS I think about items on other parts of the test I can’t
answer.
(2) When I take tests I think of the consequences of failing.
(3) I have an uneasy, upset feeling when I take an ALEKS test.
(4) I feel my heart beating fast when I take an ALEKS test.
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Boredom adopted from Artino (2009a)
While studying in this ALEKS course,
(1)
(2)
(3)
(4)
(5)
I was bored.
I felt the course was fairly dull.
My mind wandered.
I was uninterested in the course material.
I thought about what else I would rather be doing.
Frustration adopted from Artino (2009a)
While studying in this ALEKS course,
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(1)
(2)
(3)
(4)
I
I
I
I
was frustrated.
was angry.
felt as though I was wasting my time.
was irritated.
Learning strategies adapted from the MSLQ (Pintrich et al., 1993)
Critical thinking
(1) I often find myself questioning things I hear or read in this course to decide if I find
them convincing.
(2) When different ways of problem solving are presented in the learning materials, I
consider which method will be most appropriate for me to use.
(3) I treat the course material as a starting point and try to develop my own ideas about it.
(4) I try to play around with ideas of my own related to what I am learning in this ALEKS
course.
(5) Whenever I learn procedures for solving problems, I think about possible alternatives.
Metacognitive self-regulation
During class time I often find I’m thinking of other things(R).
When studying for this ALEKS course, I make up questions to help focus my
learning.
(3) When I become confused about course material for this class, I go back and try to
figure it out.
(4) If course content is difficult to understand, I change the way I approach the learning material.
(5) Before I study new course material thoroughly, I often skim it to see how it is organized.
(6) I ask myself questions to make sure I understand the material I have been studying
in this class.
(7) I try to change the way I study in order to fit the course requirements.
(8) I often find that I have been practicing problems for this class but don’t know what
they were all about (R).
(9) I try to think through a concept and decide what I am supposed to learn from it
rather than just practicing the problems when studying for this ALEKS course.
(10) When studying for this course I try to determine which concepts I don’t understand
well.
(1)
(2)
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(11) When I study for this ALEKS class, I set goals for myself in order to direct my
activities in each study period.
(12) If I get confused while studying ALEKS materials, I go back and try to figure out.
Satisfaction adapted from Artino (2009a)
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(1) Overall, I was satisfied with my ALEKS course experience.
(2) This ALEKS course met my needs as a learner.
(3) I would recommend this ALEKS course to a friend who needed to learn the material.