Self-regulation in online learning

Transcription

1 Distance Education, 2013 Vol. 34, No. 3, , Self-regulation in online learning Moon-Heum Cho a* and Demei Shen b a Lifespan Development & Educational Sciences (LDES), Kent State University-Stark, North Canton, OH, USA; b Shanghai Engineering Research Center of Digital Education Equipment, East China Normal University, Shanghai, China (Received 7 December 2012; final version received 17 June 2013) The purpose of this study was to examine the role of goal orientation and academic self-efficacy in student achievement mediated by effort regulation, metacognitive regulation, and interaction regulation in an online course. The results show that intrinsic goal orientation and academic self-efficacy predicted students metacognitive self-regulation; however, extrinsic goal orientation did not predict any type of regulation. Effort regulation and the amount of time spent in Blackboard predicted students academic achievement in the course, and interaction regulation predicted the amount of time spent in the online course. Results show the importance of individual students intrinsic goal orientation and academic self-efficacy in academic achievement. Discussion relates to current research and implications for online teaching and learning practice. Keywords: goal orientations; academic self-efficacy; metacognitive regulation; effort regulation; interaction regulation; online learning Introduction Self-regulated learning (SRL) involves a student s effort to manage learning processes systematically oriented to achieve goals (Zimmerman & Schunk, 2011). Often, multiple constructs explain students SRL (Artino, 2009; Azevedo, 2005; Cho & Jonassen, 2009; Zimmerman & Schunk, 2011). These constructs include goal orientation, academic self-efficacy, and regulations in the learning contexts (Pintrich, 2004). Skillful self-regulated learners have been reported to have higher intrinsic goal orientation and higher academic self-efficacy than less skillful students. In addition, skillful self-regulated learners better regulate and adjust their learning process in learning contexts than less skillful learners (Pintrich, 1999, 2004; Zimmerman & Schunk, 2011). Studies have reported that students SRL is important to determine successful learning experiences (i.e., satisfaction and achievement) in technology-mediated learning environments (Artino, 2008; Greene & Azevedo, 2009). For example, Artino (2008) found that academic self-efficacy and task value significantly explain students satisfaction with web-based courses. Greene and Azevedo (2009) found SRL is also related to students acquisition of conceptual knowledge in a web-based science course. * Corresponding author. mhcho@kent.edu 2013 Open and Distance Learning Association of Australia, Inc.

2 Distance Education 291 However, SRL is challenging to many students in a technology-mediated learning environment, especially in an online learning environment, where they may lack immediate support and feel lost or socially isolated (Cho, Shen, & Laffey, 2010; Sun & Rueda, 2012). Ali and Leeds (2009) found significant higher dropout rates in online courses than in face-to-face courses. In their review of research on dropouts, Lee and Choi (2011) found that online students lack of ability to self-regulate learning is a significant reason for high dropout rates, but other variables, such as personal reasons, explain a significant portion of dropout rate (Nichols, 2010). If students dropped out of online courses because of lack of SRL, these students tended to show lack of goal commitment, locus of control, and academic self-efficacy. In addition, they showed lack of coping strategies and resilience and underestimated the time required to complete tasks; therefore, SRL is an important factor in determining students success in online learning environments (Artino, 2008; Cho & Jonassen, 2009; Dabbagh & Kitsantas, 2005; Puzziferro, 2008). Because few empirical SRL studies have been conducted in online learning environments, the current researchers sought to determine the role of students SRL in their academic achievement in an online course. SRL was measured with several constructs, such as goal orientation, academic self-efficacy, and three types of regulation (effort regulation, metacognitive regulation, and interaction regulation). Thus, this study offers a comprehensive view of SRL in an online learning environment. Goal orientation Goal orientation refers to students intentions, implicitly set while choosing or engaging or persisting in diverse learning activities (Meece, Anderman, & Anderman, 2006; Schunk, 2012). Two types of goal orientation intrinsic and extrinsic have been explained by goal theorists (Pintrich, 1999). Intrinsic goal orientation refers to students disposition toward mastering the task, and extrinsic goal orientation refers to students disposition toward getting good grades in achievement situations. In general, intrinsic goal orientation is known to be positively related to students self-regulation and performance; and extrinsic goal orientation, negatively related (Meece et al., 2006; Pintrich, 1999; Rakes & Dunn, 2010; Sungar, 2007). Rakes and Dunn (2010) found that online students intrinsic goal orientation is negatively related with procrastination. Academic self-efficacy Academic self-efficacy, which refers to the confidence of students about their learning and performance, is known to be positively related to their self-regulation and academic performance (Artino, 2007; Meece et al., 2006; Pintrich, 1999). These findings seem to be replicable in online learning environments. Yang, Tasi, Kim, Cho, and Laffey (2006) found that students academic self-efficacy is associated with social interaction in online learning settings. They also discovered that the more confidence students have about learning and performance, the more they are likely (a) to feel comfortable sharing personal information with others (e.g., peers and instructors) and (b) to connect to instructors. In addition, Shea and Bidjerano (2010) found that the higher the academic self-efficacy, the more likely students are to regulate their effort in online learning environments.

3 292 M.-H. Cho and D. Shen Effort regulation Effort regulation refers to students level of commitment to manage tasks and challenges with regard to their learning (Puzziferro, 2008). Through path analysis, Sungar (2007) reported that student effort regulation is explained mostly in terms of two variables: metacognitive regulation and self-efficacy. Furthermore, in online learning environments, Puzziferro found significant differences in effort regulation between withdrawn students and low-achieving students on the one hand and higher-achieving students on the other. She found students who acquired higher grades (e.g., A, B, or C) showed higher effort regulation than students who were either withdrawn or achieved lower grades (e.g., D or F) in online learning environments. In addition, Rakes and Dunn (2010) found online student effort regulation negatively associated with student procrastination. Metacognitive regulation Perhaps one of the most common constructs that have been extensively studied to explain SRL is metacognitive regulation, that is, students ability to plan, monitor, reflect, and adjust their learning process while studying learning materials (Duncan & McKeachie, 2005; Puzziferro, 2008). For example, Artino (2009) reported that students who have clear career goals are reported more likely to use metacognitive regulation in computer-mediated self-paced learning environments than students who do not have clear career goals. Puzziferro (2008) found high-achieving students are likely to show more metacognitive regulation and more satisfaction with online learning than low-achieving students. Interaction regulation Interaction regulation refers to students ability to regulate social interaction with others (Cho & Jonassen, 2009). Researchers have agreed that students should develop regulation skills for interaction with others in online learning environments (Cho & Jonassen, 2009; Garner & Bol, 2011). Cho et al. (2010) found that students interaction regulation, for example, monitoring for interaction with others is positively related to students perceived peer social presence, instructor social presence, connectedness to the community, and perceived learning; but self-regulation for learning tasks is not. Cho et al. concluded that in addition to self-regulation for learning tasks, multiple types of self-regulation, such as self-regulation for interaction with others, must be considered in online learning environments. Considering that interaction with others (e.g., peers) is a common task in online learning environments (Cho & Summers, 2012; Dabbagh & Kitsantas, 2005), one can easily recognize that interaction regulation is an important variable to explain online learning; however, very little research has been conducted to investigate the role of interaction regulation in online settings. Methods Participants A total of 64 students enrolled in Introduction to Gerontology participated in the study. The course was delivered to students via Blackboard without face-to-face

4 Distance Education 293 meetings. All communication took place through or online discussion. The same online instructor taught two sections of the course with 34 and 30 students, all of whom participated in the study. Among the 64 participants, the majority were female (N=58, 91%) and Caucasian (N=54, 84%). Their average year in school, age, and number of online courses taken prior to this online course were 2.66 (SD =.86), (SD = 9.03), and 2.08 (SD = 2.81), respectively. Measures Several measures were used to assess students goal orientations, academic self-efficacy, metacognitive regulation, effort regulation, and interaction regulation. Goal orientation The motivated strategies for learning questionnaire (MSLQ) (Duncan & McKeachie, 2005) were used to assess intrinsic and extrinsic goal orientation. Intrinsic goal orientation was measured with the four items (e.g., In a class like this, I prefer course material that really challenges me so I can learn new things. ). Extrinsic goal orientation was measured with four more items (e.g., Getting a good grade in this class is the most satisfying thing for me right now. ). A 7-point Likert scale was used where 1 denoted not at all true of me and 7 denoted very true of me. Cronbach s alpha for intrinsic and extrinsic goal orientation was.75 and.63, respectively. Academic self-efficacy Academic self-efficacy was measured with eight items derived from the MSLQ (e.g., I m confident I can understand the most complex material presented by the instructor in this course. ). A 7-point Likert scale was used. Cronbach s alpha for academic self-efficacy was.90. Metacognitive regulation Metacognitive self-regulation was assessed with 12 items derived from the MSLQ (e.g., When reading for this course, I make up questions to help focus my reading. ). Some of the items were slightly changed for the course in which the current study was conducted. A 7-point Likert scale was also used. Cronbach s alpha for metacognitive self-regulation was.82. Effort regulation Effort regulation was measured with four items derived from the MSLQ (e.g., Even when course materials are dull and uninteresting, I manage to keep working until I finish. ). A 7-point Likert scale was used. Cronbach s alpha for effort regulation was.61. Interaction regulation Interaction regulation, which consists of three regulation strategies writing, responding, and reflection strategies was assessed with 11 items derived from the online self-regulated learning inventory (Cho & Jonassen, 2009) (e.g., When I write

5 294 M.-H. Cho and D. Shen an online message, I try to organize my thoughts as much as I can. ). A 7-point Likert scale was used, where 1 indicated not at all true of me and 7 indicated very true of me. Cronbach s alpha for interaction regulation was.78. Procedures In the middle of the semester, the instructor posted the research-recruiting on her class announcements. All the students taking the course participated in the study. Participation in the research was voluntary, but those participating received an extra point. Once they filled out the online consent form, the online survey was administered. The research was approved by the Institutional Review Board and conducted ethically. Results Descriptive statistics and correlations Descriptive statistics, including mean and standard deviations of variables used in the study, were acquired and listed in Table 1. On an average, students showed relatively high academic self-efficacy (M = 5.56, SD =.88) and effort regulation (M = 5.40, SD = 1.00). Students extrinsic goal orientation (M = 5.16, SD = 1.06) was higher than their intrinsic goal orientation (M =4.75, SD = 1.04). The Pearson correlation coefficients of variables are shown in Table 1. Intrinsic goal orientation significantly correlated with academic self-efficacy (r =.30, p <.05), interaction regulation (r =.38, p <.01), effort regulation (r =.38, p <.01), and metacognitive regulation (r =.68, p <.01). Extrinsic goal orientation significantly correlated with only academic self-efficacy and interaction regulation, respectively (r =.34, r =.35, p <. 01). Academic self-efficacy positively correlated with all three types of regulation, such as interaction regulation (r =.50, p <.01), effort regulation (r =.32, p <.01), and metacognitive regulation (r =.43, p <.01) as well as login time (r =.36, p <.01). Student achievement in terms of total points significantly correlated to effort regulation and login time, r = 30, p <.05; r =.42, p <.01, respectively (see Table 1). Path analysis We created a conceptual path model according to the literature review and correlations among variables. The path model was analyzed using IBM SPSS Amos Table 1. Descriptive statistics and correlation of variables. Variables M SD Intrinsic goal Extrinsic goal Self-efficacy *.34** Interaction regulation **.35**.54** Effort regulation **.02.32**.36** Meta regulation **.21.43**.58**.61** Login time (min.) **.29* Total points *.15 42** Note. * p <.05; ** p <.01.

6 Distance Education By dropping nonsignificant paths and using model fit indices, we found a good fit between the final tested model and the data. Some researchers (e.g., Y. Lin, G. Lin, & J. M. Laffey, 2008; Sockalingam, Rotgans, & Schmidt, 2011) followed Hu and Bentler (1998, 1999) and Byrne (2001), reporting a combination of absolute fit indices and relative or comparative fit indices (CFI). The fit indices include Chi-square values accompanied with degree of freedom, goodness of fit (GFI), root-mean-square error of approximation (RMSEA), and relative or CFI (Miles & Shevlin, 2007). Chi-square derives from the fit function and is sensitive to sample size. A nonsignificant Chi-square test result indicates good model fit because it assumes that variables in the base line model are not supposed to have any relationships. However, other indices should also be considered. For RMSEA, Hu and Bentler (1999) recommended a cutoff value of.06, and the smaller the number, the better the model fit. Both CFI and GFI range from 0 to 1; a value of CFI and GFI greater than.90 is considered a good fit. Compared to the recommended values of fit indices, the path model tested showed good model fit indices, with Chi-square tests nonsignificant (χ 2 = 18.56, df = 18, p =.420), RMSEA =. 022, CFI =.996, and GFI =.929. The model fit criterion and fit indices are shown in Table 2. All path coefficients (i.e., standardized regression weights) were statistically significant at the.05,.01, or.001 levels. Figure 1 presents an overview of the model. Both intrinsic and extrinsic goal orientation significantly correlated with academic self-efficacy; however, extrinsic goal orientation had no direct effect on any other variables. In other words, extrinsic goal orientation did not significantly influence Table 2. Summary of the GFI indices. Model fits χ 2 p CFI GFI RMSEA Recommended value N/A >.05 >.90 >.90 <.06 Tested model Intrinsic goal orientation Effort regulation.22 Achievement Extrinsic goal orientation Metacognitive regulation.42 Total amount of login time in Blackboard Interaction regulation Academic self-efficacy Figure 1. Standardized estimates of the best fit model.

7 296 M.-H. Cho and D. Shen students metacognitive regulation, effort regulation and interaction regulation, or achievement in this study. By contrast, intrinsic goal orientation directly influenced metacognitive regulation, which had an indirect effect on achievement mediated by effort regulation. Both intrinsic goal orientation and academic self-efficacy had direct effect on metacognitive regulation, indicating that both variables indirectly influenced effort regulation and interaction regulation through metacognitive regulation. Academic self-efficacy also had a direct effect on interaction regulation. Only interaction regulation influenced students login time directly; academic self-efficacy and metacognitive regulation had indirect effect on login time through interaction strategy. Both total amount of login time in Blackboard and effort regulation influenced students achievement directly, but all the other variables except extrinsic goal orientation influenced achievement indirectly. Discussion The results of the study demonstrated the relationship between SRL and achievement in an undergraduate online course. SRL was explained with multiple constructs, such as goal orientation, academic self-efficacy, effort regulation, metacognitive regulation, and interaction regulation. The results of this study show that the intrinsic goal orientation and academic self-efficacy positively associated with students achievements are mediated by three types of regulation effort regulation, metacognitive regulation, and interaction regulation; but extrinsic goal orientation was not associated with any types of regulation nor did it influence students achievements. The current study extends achievement goal theories to online learning environments. The results of achievement goal research have shown that students who have intrinsic goal orientations tend to persist with learning in challenging tasks and report high involvement in learning process by regulating their cognition and motivation. On the other hand, students who have extrinsic goal orientation are not likely to engage in their learning process (Meece et al., 2006; Pintrich, 1999). Our study shows that students learning patterns are similar in online learning settings, depending on students goal orientation in that intrinsic goal orientation is positively related to metacognitive regulation but extrinsic goal orientation is not associated with any types of regulations. In addition, the study demonstrates positive correlations between academic selfefficacy and student regulation, such as metacognitive regulation and interaction regulation. In a review of online SRL research, Artino (2007) found similar results in that academic self-efficacy is positively associated with students use of metacognitive regulation. Shea and Bidjerano (2010) also found that academic self-efficacy influences online students cognitive engagement mediated by effort regulation. The current study adds empirical evidence to the existing line of online studies on the role of academic self-efficacy. In addition, the study also demonstrated that academic self-efficacy plays an important role in online settings. Academic self-efficacy is associated with both metacognitive regulation and interaction regulation; therefore, enhancing online students academic self-efficacy is significant for student success. At the same time, the current research showed multiple types of regulations are involved in students SRL in online learning environments. These regulations are effort regulation, metacognitive regulation, and interaction regulation. Mediating roles of metacognitive regulation and effort regulation have been well documented

8 Distance Education 297 (Artino, 2007; Meece et al., 2006; Pintrich, 1999; Shea & Bidjerano, 2010); however, interaction regulation is a new concept that must be considered with regard to online SRL (Cho & Summers, 2012; Garner & Bol, 2011). Cho and Summers (2012) found that a significant portion of online assignments and activities requires students to interact with other students (36.5%); therefore, interaction with others should be considered an important aspect of online SRL. Furthermore, Garner and Bol (2011) argued that SRL researchers should consider three types of regulations represented with regulation between student and content, regulation between student and student, and regulation between student and instructor. They were critical of current online SRL, in which only the aspect of interaction between students and content is considered. The current study expands online SRL theories by incorporating the social aspect of regulation or interaction regulation. Implications for online teaching practices The current study shows the importance of intrinsic goal orientation, academic self-efficacy, and regulation for achievement in online learning contexts. Several implications for online teaching strategies follow. Enhance students intrinsic goal orientation The study results show that intrinsic goal orientation positively influenced students SRL and performance directly or indirectly. This demonstrates the importance of helping students obtain, maintain, and improve their intrinsic goal orientation toward their learning tasks. Problem-based learning (PBL) can enhance students intrinsic goal orientation. For instance, Sungur and Tekkaya (2006) found that students who were exposed to PBL environments had significantly higher levels of intrinsic goal orientation, task value, metacognitive self-regulation, effort regulation, and other cognitive strategies compared to students who were taught in a traditional teachercentered environment. Students in PBL environments may develop higher intrinsic goal orientation because authentic or real-life problems help students engage in learning. In addition to PBL, providing students with intrinsic goal rationale or intrinsic goal content may promote students intrinsic goal orientation. For example, Vansteenkiste, Simons, Lens, Sheldon, and Deci (2004) provided students with intrinsic goal rationale or intrinsic goal content, for example, Reading the text could help you know how to teach your future toddlers that they can do something to help the environment. Vansteenkiste et al. found that students who were provided intrinsic goal rationale or intrinsic goal content had significantly higher levels of autonomous motivation and performance than those who were given instructional materials with extrinsic goal conditions, for example, Reading the text could teach you how to save money by reusing materials. Promote students academic self-efficacy In this study, academic self-efficacy was found to positively influence students regulations, including metacognitive regulation and interaction regulation. Online instructors can promote students academic self-efficacy through the teacher s presence. For example, Shea and Bidjerano (2010) found that teaching presence represented by several constructs, such as course design and organization,

9 298 M.-H. Cho and D. Shen facilitation, and direct instruction, is positively associated with students academic self-efficacy. The research results imply that in order to promote students academic self-efficacy, online teachers should design and organize the class well, facilitate students online discussions and learning activities, and provide positive feedback to students. In addition to teaching presence, teachers can promote students academic self-efficacy by having them set challenging but achievable goals (Bandura, 1997). Students who do so are likely to achieve those goals in the initial stage of learning, and this successful experience may encourage them to set other challenging but achievable goals. Students will also tend to spend more time and put effort into achieving the goals. The recursive process of setting goals, experiencing success, and exerting effort helps students develop confidence about learning. Scaffold students to regulate their learning Students regulation predicted academic achievement and the amount of time they spent online. Online instructors monitoring efforts can scaffold students to regulate their learning. Scaffolding by instructors includes their monitoring individual and group activities, guiding interaction, and promoting social interaction. Cho and Kim (2013) found that online instructors scaffolding efforts for interaction with students most significantly predicted students self-regulation for interaction with others in comparison to any other variables, such as mastery goal orientation and demographic information. In addition to instructors monitoring effort, social media such as Twitter can be used to promote students regulation. Cho and Cho (in press) compared messages posted on Twitter by students in an experimental group whose members received self-regulation training and by students in a control group whose members had not received such training. Cho and Cho found that students in the experimental group posted more messages related to self-regulation and supporting messages for others regulation, but students in the control group posted more social messages and messages unrelated to tasks. In addition, Cho and Cho reported that students in the experimental group improved metacognition significantly, whereas students in the control group did not. Cho and Cho concluded that social networking services such as Twitter can be used as a tool to promote students self-regulation. Contribution of the study to existing research on online SRL This study contributes to existing online SRL research in several ways. First, different from the previous online researchers who investigated a single construct of SRL (mainly metacognitive regulation), the current researchers explored multiple constructs of SRL, such as goal orientation, academic self-efficacy, metacognitive regulation, effort regulation, and interaction regulation. Because the researchers investigated the relationships among multiple constructs, this study has yielded a more comprehensive picture of SRL in online learning environments. Second, different from many online studies in which satisfaction or perceived learning are the dependent variables, students final achievement was the dependent variable in this study. The study empirically shows the role of SRL in achievement in online learning environments. By using achievement as the dependent variable, the researchers have contributed to existing SRL studies in which positive relationships between SRL and achievement are assumed (Meece et al., 2006). Finally, all students in an online course participated in the study; therefore, it represents SRL

10 Distance Education 299 occurring in a class more accurately than other studies, in which only a small proportion of students participated. Recommendations for future research The researchers offer the following recommendation for future study. First, future researchers may want to apply the proposed SRL models to different research participants, such as online graduate students. One of the current trends in online learning is the online delivery of more graduate courses. Comparing the SRL models with undergraduates and graduates will produce interesting results. Second, the proposed SRL models can be tested with more gender-balanced samples from courses in diverse disciplines. The majority of participants in this study were female students (91%), and the data were collected in one online course. Future researchers should more systematically sample research participants from multiple online courses and consider gender and disciplines. Third, data mining techniques with systemgenerated data are recommended for future study. The current researchers heavily relied on students self-reported data and used a small amount of system-generated data, including the amount of time students stayed online; however, future researchers may consider using larger amounts of system-generated data. For example, future researchers can extract real data, such as the number of postings on the discussion board, and patterns of interacting with teachers, peers, files, or documents. By using system-generated data, online SRL can be better understood. Notes on contributors Moon-Heum Cho is an assistant professor in Lifespan Development and Educational Sciences (LDES) at Kent State University at Stark. His research interests include online learning, self-regulation in technology-mediated learning environments, and technology integration to enhance learning and teaching practices. Demei Shen is a research associate at the East China Normal University. Her research interests include self-efficacy beliefs in online learning contexts, social computing, educational big data, data intensive science, learning analytics, and trends and issues in instructional technologies. References Ali, R., & Leeds, E. (2009). The impact of classroom orientation in online student retention. Online Journal of Distance Learning Administration, 12. Retrieved from Artino, A. R. (2007). Self-regulated learning in online education: A review of the empirical literature. International Journal of Instructional Technology & Distance Learning, 4. Retrieved from Artino, A. R. (2008). Motivational beliefs and perceptions of instructional quality: Predicting satisfaction with online training. Journal of Computer Assisted Learning, 24, Artino, A. R. (2009). Online learning: Are subjective perceptions of instructional context related to academic success? Internet and Higher Education, 12, doi: / j.iheduc Azevedo, R. (2005). Using hypermedia as a metacognitive tool for enhancing student learning? The role of self-regulated learning. Educational Psychologist, 40, doi: /s ep4004_2 Bandura, A. (1997). Self-efficacy: The exercise of control. New York, NY: W. H. Freeman and Company.

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