This article was downloaded by: [Ryerson University] On: 27 April 2015, At: 05:26 Publisher: Routledge Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK Distance Education Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/cdie20 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 PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. 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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: firstname.lastname@example.org © 2015 Open and Distance Learning Association of Australia, Inc. Downloaded by [Ryerson University] at 05:26 27 April 2015 Distance Education 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) Downloaded by [Ryerson University] at 05:26 27 April 2015 82 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. Distance Education 83 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. Downloaded by [Ryerson University] at 05:26 27 April 2015 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. 84 M.-H. Cho and M.L. Heron Table 1. Demographic information for participants. Downloaded by [Ryerson University] at 05:26 27 April 2015 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 Distance Education 85 Downloaded by [Ryerson University] at 05:26 27 April 2015 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. 86 M.-H. Cho and M.L. Heron Downloaded by [Ryerson University] at 05:26 27 April 2015 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. Downloaded by [Ryerson University] at 05:26 27 April 2015 1 −.28** −.23** −.69** −.24** 6 1 .74** .31** −.01 7 1 .33** −.03 8 1 .26** 9 1 10 Distance Education 87 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 Downloaded by [Ryerson University] at 05:26 27 April 2015 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 Distance Education 89 Table 4. Model summaries for hierarchical regression analysis predicting overall satisfaction with a self-paced online course. Step 1 B Downloaded by [Ryerson University] at 05:26 27 April 2015 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, 90 M.-H. Cho and M.L. Heron Downloaded by [Ryerson University] at 05:26 27 April 2015 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 Downloaded by [Ryerson University] at 05:26 27 April 2015 Distance Education 91 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. Downloaded by [Ryerson University] at 05:26 27 April 2015 92 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 Distance Education 93 motivation, emotions, and learning strategies related to the course and socially regulate their learning. Downloaded by [Ryerson University] at 05:26 27 April 2015 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 94 M.-H. Cho and M.L. Heron Downloaded by [Ryerson University] at 05:26 27 April 2015 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. 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Retrieved from http://www.ifets.info/ Zimmerman, B. J. (2011). Motivational sources and outcomes of self-regulated learning and performance. In B. J. Zimmerman & D. H. Schunk (Eds.), Handbook of self-regulation of learning and performance (pp. 49–64). New York, NY: Routledge. Zimmerman, B. J., & Martinez-Pons, M. (1988). Construct validation of a strategy model of student self-regulated learning. Journal of Educational Psychology, 80, 284–290. doi:10.1037/0022-06188.8.131.524 Zimmerman, B. J., & Schunk, D. (2011). Handbook of self-regulation of learning and performance. New York, NY: Routledge. Distance Education 97 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) Downloaded by [Ryerson University] at 05:26 27 April 2015 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. 98 M.-H. Cho and M.L. Heron 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, Downloaded by [Ryerson University] at 05:26 27 April 2015 (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) Distance Education 99 (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) Downloaded by [Ryerson University] at 05:26 27 April 2015 (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.