Latent Profiles of Online Self-Regulated Learning: Relationships with Predicted and Final Course Grades

Authors

  • Diana Mindrila University of West Georgia
  • Li Cao University of West Georgia

DOI:

https://doi.org/10.19173/irrodl.v23i2.5946

Keywords:

online self-regulated learning, latent profile analysis, person-centered approach, variable-centered approach, higher education

Abstract

This study used a combined person- and variable-centered approach to identify self-regulated online learning latent profiles and examine their relationships with the predicted and earned course grades. College students (N=177) at a Southeastern U.S. university responded to the Online Self-Regulated Learning Questionnaire. Exploratory structural equation modeling revealed four self-regulation factors: goal setting, environment management, peer help-seeking, and task strategies. Latent profile analysis yielded four latent profiles: Below Average Self-Regulation (BASR), Average Self-Regulation (ASR), Above Average Self-Regulation (AASR), and Low Peer Help-Seeking (LPHS). Compared with the AASR group, when students anticipated obtaining a higher course grade, they were less likely to engage in peer help-seeking and task strategies and more likely to adopt the LPHS self-regulation profile. Relating to LPHS, membership to all other groups predicted significantly lower course grades. AASR and LPHS predicted their performance most accurately, with non-significant differences between the predicted and the final course grades.

References

Abar, B., & Loken, E. (2010). Self-regulated learning and self-directed study in a pre-college sample. Learning and Individual Differences, 20, 25–29. https://doi.org/10.1016/j.lindif.2009.09.002

Akaike, H. (1977). On entropy maximization principle. In P. R. Krishnaiah (Ed.), Applications of statistics (pp. 27–41). Elsevier Science.

Alexander, P. A. (2016). Psychology in learning and instruction. Pearson.

Ally, M. (2004). Foundations of educational theory for online learning. In T. Anderson (Ed.), The theory and practice of online learning (pp. 15–44). Athabasca University Press.

Aristovnik, A., Keržič, D., Ravšelj, D., Tomaževič, N., & Umek, L. (2020). Impacts of the COVID-19 pandemic on life of higher education students: A global perspective. Sustainability, 12(20), 8438. https://doi.org/10.3390/su12208438

Asparouhov, T., & Muthén, B. (2009). Exploratory structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal, 16, 397–438. https://doi:10.1080/10705510903008204

Asparouhov, T., & Muthén, B. (2012). Auxiliary variables in mixture modeling: A 3-step approach using Mplus. Structural Equation Modeling: A Multidisciplinary Journal, 21(3), 329–341. https://doi.org/10.1080/10705511.2014.915181

Asparouhov, T., Muthén, B., & Morin, A. J. (2015). Bayesian structural equation modeling with cross-loadings and residual covariances: Comments on Stromeyer et al. Journal of Management, 41, 1561–1577. https://doi:10.1177/0149206315591075

Azevedo, R. (2005). Using hypermedia as a metacognitive tool for enhancing student learning? The role of self-regulated learning. Educational Psychologist, 40(4), 199–209. https://doi.org/10.1207/s15326985ep4004_2

Azevedo, R., & Hadwin, A. F. (2005). Scaffolding self-regulated learning and metacognition—Implications for the design of computer-based scaffolds. Instructional Science, 33(5–6), 367–379. https://doi.org/10.1007/s11251-005-1272-9

Bámaca-Colbert, M. Y., & Gayles, J. G. (2010). Variable-centered and person-centered approaches to studying Mexican-origin mother-daughter cultural orientation dissonance. Journal of Youth and Adolescence, 39(11), 1274–1292. https://doi.org/10.1007/s10964-009-9447-3

Barnard, L., Lan, W. Y., To, Y. M., Paton, V. O., & Lai, S. L. (2009). Measuring self-regulation in online and blended learning environments. Internet and Higher Education, 12(1), 1–6. https://doi.org/10.1016/j.iheduc.2008.10.005

Barnard, L., Paton, V. O., & Lan, W. Y. (2008). Online self-regulatory learning behaviors as a mediator in the relationship between online course perceptions with achievement. International Review of Research in Open and Distributed Learning, 9(2), 1–11. https://doi.org/10.1016/j.iheduc.2008.10.005

Barnard-Brak, L., Lan, W. Y., & Paton, V. O. (2010). Profiles in self-regulated learning in the online learning environment. International Review of Research in Open and Distributed Learning, 11(1), 63–80. https://doi.org/10.19173/irrodl.v11i1.769

Bergman, L. R. (1998). A pattern-oriented approach to studying individual development: Snapshots and processes. In R. B. Cairns, L. R. Bergman, & J. Kagan (Eds.), Methods and models for studying the individual (pp. 83–122). Sage Publications, Inc.

Bergman, L. R., & Anderson, H. (2010). The person and the variable in developmental psychology. The Journal of Psychology, 218(3), 155–165. https://doi.org/10.1027/0044-3409/a000025

Bergman, L. R., & Magnusson, D. (1997). A person-oriented approach in research on developmental psychopathology. Development and Psychopathology, 9, 291–319. https://doi.org/10.1017/S095457949700206X

Bergman, L. R., Magnusson, D., & El-Khouri, B. M. (2003). Studying individual development in an interindividual context. Erlbaum.

Boekaerts, M. (1996). Self-regulated learning and the junction of cognition and motivation, European Psychologist, 1, 100–112. https://psycnet.apa.org/doi/10.1027/1016-9040.1.2.100

Boekaerts, M. (1999). Self-regulated learning: Where we are today. International Journal of Educational Research, 31(6), 445–457. https://doi.org/10.1016/S0883-0355(99)00014-2

Boekaerts, M., & Corno, L. (2005). Self-Regulation in the classroom: A perspective on assessment and intervention. Applied Psychology: An International Review, 54(2), 199–231. https://doi.org/10.1111/j.1464-0597.2005.00205.x

Broadbent, J., & Fuller-Tyszkiewicz, M. (2018). Profiles in self-regulated learning and their correlates for online and blended learning students. Educational Technology Research and Development 66, 1435–1455. https://doi.org/10.1007/s11423-018-9595-9

Broadbent, J., & Poon, W. L. (2015). Self-regulated learning strategies and academic achievement in online higher education learning environments: A systematic review. The Internet and Higher Education, 27, 1–13. http://dx.doi.org/10.1016/j.iheduc.2015.04.007

Bruso, J. L., & Stefaniak, J. E. (2016). The use of self-regulated learning measure questionnaires as a predictor of academic success. Tech Trends, 60, 577–584. https://doi.org/10.1007/s11528-016-0096-6

Chen, J. A. (2012). Implicit theories, epistemic beliefs, and science motivation: A person-centered approach. Learning and Individual Differences 22, 724–735. https://doi.org/10.1016/j.lindif.2012.07.013

Cleary, T. J., & Callan, G. L. (2018). Assessing self-regulated learning using microanalytic methods. In D. H. Schunk & J. A. Greene (Eds.), Handbook of self-regulation of learning and performance (pp. 338–351). Routledge/Taylor & Francis Group. https://doi.org/10.4324/9780203839010

Collins, L. M., & Lanza, S. T. (2009). Latent class and latent transition analysis: With applications in the social, behavioral, and health sciences (Vol. 718). John Wiley & Sons.

Cuesta, L. (2010). Metacognitive instructional strategies: A study of e-learners´ self-regulation. In The Fourteenth International CALL Conference Proceedings: Motivation and Beyond. ISBN: 978-9057282973 Retrieved from http://uahost.uantwerpen.be/linguapolis/scuati/proceedings_CALL 2010.pdf

Deimann, M., & Bastiaens, T. (2010). The role of volition in distance education: An exploration of its capacities. International Review of Research in Open and Distributed Learning, 11(1), 1–16. https://doi.org/10.19173/irrodl.v11i1.778

DiStefano, C., Zhu, M., & Mindrila, D. (2009). Understanding and using factor scores: Considerations for the applied researcher. Practical Assessment, Research, and Evaluation, 14(1), 20.

DiStefano, C. (2012). Cluster analysis and latent class clustering techniques. In B. Laursen, T. D. Little, & N. A. Card (Eds.), Handbook of developmental research methods (pp. 645–666). Guilford Press.

Efklides, A. (2011). Interactions of metacognition with motivation and affect in self-regulated learning: The MASRL model. Educational Psychology, 46, 6–25. https://doi:10.1080/00461520.2011.538645

Feyerabend, P. (1975). Against method. Wiley.

Finney, S. J., & DiStefano, C. (2006). Non-normal and categorical data in structural equation modeling. In G. R. Hancock & R. O. Mueller (Eds.), Structural equation modeling: A second course (pp. 269–314). Information Age Publishing.

Gerjets, P., Scheiter, K., & Schuh, K. (2008). Information comparisons in example-based hypermedia environments: Supporting learners with processing prompts and an interactive comparison tool. Educational Technology Research and Development, 56, 73–92. http://dx.doi.org/10.1007/s11423-007-9068-z

Greene, J. A. (2018). Self-regulation in education. Routledge.

Greene, J. A., & Azevedo, R. (2007). A theoretical review of Winne and Hadwin’s model of self-regulated learning: New perspectives and directions. Review of Educational Research, 77(3), 334–372. https://doi.org/10.3102%2F003465430303953

Guo, P. J., & Reinecke, K. (2014). Demographic differences in how students navigate through MOOCs. In Proceedings of the First ACM Conference on Learning@ Scale, (pp. 21–30).

Hampson, S. E., & Colman, A. M. (Eds.). (1995). Individual differences and personality. Longman.

Händel, M., de Bruin, A. B., & Dresel, M. (2020). Individual differences in local and global metacognitive judgments. Metacognition and Learning, 15(1), 51–75. https://doi.org/10.1007/s11409-020-09220-0

Hirt, C. N., Karlena, Y., Merki, K. M., & Suter, F. (2021). What makes high achievers different from low achievers? Self-regulated learners in the context of a high-stakes academic long-term task. Learning and Individual Differences 92, 102085. https://doi.org/10.1016/j.lindif.2021.102085

Hood, N., Littlejohn, A., & Milligan, C. (2015). Context counts: How learners’ contexts influence learning in a MOOC. Computers & Education, 91, 83–91. https://doi.org/10.1016/j.compedu.2015.10.019

Howard, M. C., & Hoffman, M. E. (2017). Variable-centered, person-centered, and person-specific approaches: Where theory meets the method. Organizational Research Methods, 21(4), 846–876. https://doi.org/10.1177%2F1094428117744021

Kaplan, A. (2017). Academia goes social media, MOOC, SPOC, SMOC, and SSOC: The digital transformation of higher education institutions and universities. In B. Rishi & S. Bandyopadhyay (Eds.), Contemporary issues in social media marketing (pp. 20–31) Routledge.

Kocdar, S., Karadeniz, A., Bozkurt, A., & Buyuk, K. (2018). Measuring self-regulation in self-paced open and distance learning environments. The International Review of Research in Open and Distributed Learning, 19(1). https://doi.org/10.19173/irrodl.v19i1.3255

Laursen, B. P., & Hoff, E. (2006). Person-centered and variable-centered approaches to longitudinal data. Merrill-Palmer Quarterly, 52(3), 377–389. https://doi.org/10.1353/mpq.2006.0029

Lehmann, T., Hähnlein, I., & Ifenthaler, D. (2014). Cognitive, metacognitive and motivational perspectives on preflection in self-regulated online learning. Computers in Human Behavior, 32, 313–323. https://doi.org/10.1016/j.chb.2013.07.051

Lim, D. H., Yoon, W., & Morris, M. L. (February, 2006). Instructional and learner factors influencing learning outcomes with online learning environment [paper]. The Academy of Human Resource Development International Conference (AHRD), Columbus, OH.

Lynch, R., & Dembo, M. (2004). The relationship between self-regulation and online learning in a blended learning context. International Review of Research in Open and Distributed Learning, 5(2), 1–16. https://doi.org/10.19173/irrodl.v5i2.189

Marsh, H. W., Lüdtke, O., Trautwein, U., & Morin, A. J. (2009). Classical latent profile analysis of academic self-concept dimensions: Synergy of person-and variable-centered approaches to theoretical models of self-concept. Structural Equation Modeling: A Multidisciplinary Journal, 16(2), 191–225. http://dx.doi.org/10.1080/10705510902751010

Marsh, H. W., Morin, A. J., Parker, P. D., & Kaur, G. (2014). Exploratory structural equation modeling: An integration of the best features of exploratory and confirmatory factor analysis. Annual Review of Clinical Psychology, 10, 85–110. https://doi:10. 1146/annurev-clinpsy-032813-153700

Molenaar, P. C. (2004). A manifesto on psychology as idiographic science: Bringing the person back into scientific psychology, this time forever. Measurement, 2(4), 201–218. https://doi.org/10.1207/s15366359mea0204_1

Molenaar, P. C., & Campbell, C. G. (2009). The new person-specific paradigm in psychology. Current Directions in Psychological Science, 18(2), 112–117. https://doi.org/10.1111/j.1467-8721.2009.01619.x

Morin, A. J. S., & Maiano, C. (2011). Cross-validation of the short form of the physical self-inventory (PSI-S) using exploratory structural equation modeling (ESEM). Psychology of Sport and Exercise, 12, 540–554. https://doi:10.1016/j.psychsport.2011.04.003

Morin, A. J. S., Marsh, H. W., & Nagengast, B. (2013). Exploratory structural equation modeling In G. R. Hancock & R. O. Mueller (Eds.), Structural equation modeling: A second course (2nd ed., pp. 395–436). Information Age Publishing.

Moore, J. L., Dickson-Deane, C., & Galyen, K. (2011). E-learning, online learning, and distance learning environments: Are they the same? The Internet and Higher Education, 14(2), 129–135. https://doi.org/10.1016/j.iheduc.2010.10.001

Muthén, B. (2004). Latent variable analysis. In The Sage handbook of quantitative methodology for the social sciences (pp. 346–369). Sage. https://dx.doi.org/10.4135/9781412986311

Nasserinejad, K., van Rosmalen, J., de Kort, W., & Lesaffre, E. (2017). Comparison of criteria for choosing the number of classes in Bayesian finite mixture models. PloS ONE, 12(1), e0168838. https://doi.org/10.1371/journal.pone.0168838

Nunnally, J. C. (1978). Psychometric theory (2nd ed.). McGraw-Hill.

Nylund, K. L., Asparouhov, T., & Muthén, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study: Structural Equation Modeling, 14(4), 535–569. https://doi.org/10.1080/10705510701793320

Palvia, S., Aeron, P., Gupta, P., Mahapatra, D., Parida, R., Rosner, R., & Sindhi, S. (2018). Online education: Worldwide status, challenges, trends, and implications. Journal of Global Information Technology Management, 21(4), 233–241. https://doi.org/10.1080/1097198X.2018.1542262

Panadero, E. (2017). A review of self-regulated learning: Six models and four directions for research. Frontiers in Psychology, 8, 422. https://doi.org/10.3389/fpsyg.2017.00422

Paris, S. G., & Paris, A. H. (2001). Classroom applications of research on self-regulated learning. Educational Psychologist, 36(2), 89–101. https://doi.org/10.1207/S15326985EP3602_4

Peel, K. (2019). The fundamentals for self-regulated learning: A framework to guide analysis and reflection. Educational Practice and Theory, 41(1), 23–49. http://dx.doi.org/10.7459/ept/41.1.03

Pintrich, P. R. (2004). A conceptual framework for assessing motivation and self-regulated learning in college students. Educational Psychology Review, 16(4), 385–407. https://doi.org/10.1007/s10648-004-0006-x

Pintrich, P. R., & DeGroot, E. (1990). Motivational and self-regulated learning components of classroom academic performance. Journal of Educational Psychology, 82, 33–40. https://doi.org/10.1037/0022-0663.82.1.33

Pintrich, P. R., & Zusho, A. (2002). The development of academic self-regulation: The role of cognitive and motivational factors. In A. Wigfield & J. Eccles (Eds.), Development of achievement motivation (pp. 249–284). Academic Press.

Puustinen, M., & Pulkkinen, L. (2001). Models of self-regulated learning: A review. Scandinavian Journal of Educational Research, 45(3), 269–286. https://doi.org/10.1080/00313830120074206

Puzziferro, M. (2008). Online technologies self-efficacy and self-regulated learning as predictors of final grade and satisfaction in college-level online courses. The American Journal of Distance Education, 22(2), 72–89. https://doi.org/10.1080/08923640802039024

Ramaswamy, V., Desarbo, W. S., Reibstein, D. J., & Robinson, W. T. (1993). An empirical pooling approach for estimating marketing mix elasticities with PIMS data. Market Science, 12(1), 103–124. https://www.jstor.org/stable/183740

Raufelder, D., Jagenow, D., Hoferichter, F., & Drury, K. M. (2013). The person-oriented approach in the field of educational psychology. Problems of Psychology in the 21st Century, 5(2013), 79–88. https://doi.org/10.33225/ppc/13.05.79

Reimann, P., & Bannert, M. (2018). Self-regulation of learning and performance in computer-supported collaborative learning environments. In D. H. Schunk, & J. A. Greene (Eds.). Handbook of self-regulation of learning and performance (pp. 285–304). Routledge.

Schunk, D. H., & Greene, J. A. (Eds.). (2018). Handbook of self-regulation of learning and performance. Routledge.

Schunk, D. H., & Zimmerman, B. J. (Eds.). (2008). Motivation and self-regulated learning: Theory, research, and applications. Erlbaum.

Schwam, D., Greenberg, D., & Li, H., (2021). Individual differences in self-regulated learning of college students enrolled in online college courses, American Journal of Distance Education, 35(2), 133–151. https://doi.org/10.1080/08923647.2020.1829255

Schwinger, M., Steinmayr, R., & Spinath, B. (2009). How do motivational regulation strategies affect achievement: Mediated by effort management and moderated by intelligence. Learning and Individual Differences, 19(4), 621–627. https://doi.org/10.1016/j.lindif.2009.08.006

Schwinger, M., Steinmayr, R., & Spinath, B. (2012). Not all roads lead to Rome—Comparing different types of motivational regulation profiles. Learning and Individual Differences, 22(3), 269–279. https://doi.org/10.1016/j.lindif.2011.12.006

Severiens, S., Ten Dam, G., & Wolters, B. V. H. (2001). Stability of processing and regulation strategies: Two longitudinal studies on student learning. Higher Education, 42(4), 437–453. https://doi.org/10.1023/A:1012227619770

Sitzmann, T., Bell, B. S., Kraiger, K., & Kanar, A. M. (2009). A multilevel analysis of the effect of prompting self-regulation in technology-delivered instruction. Personnel Psychology, 62(4), 697–734. https://doi.org/10.1111/j.1744-6570.2009.01155.x

Stan, E. (2012). The role of grades in motivating students to learn. Social and Behavioral Sciences, 69, 1998–2003. https://doi.org/10.1016/j.sbspro.2012.12.156

Steffens, K. (2006). Self-regulated learning in technology-enhanced learning environments: Lessons of a European peer review. European Journal of Education, 41(3), 353−379. https://doi.org/10.1111/j.1465-3435.2006.00271.x

Vermunt, J. K., & Magidson, J. (2002). Latent class cluster analysis. In J. A. Hagenaars & A. L. McCutcheon (Eds.), Applied latent class analysis (pp. 89–106). Cambridge University Press. https://doi.org/10.1017/CBO9780511499531

von Eye, A. (2010). Developing the person-oriented approach—Theory and methods of analysis. Development and Psychopathology, 22, 277–285. https://doi.org/10.1017/s0954579410000052

von Eye, A., & Bogat, G. A. (2006). Person-oriented and variable-oriented research: Concepts, results, and development. Merrill Palmer Quarterly, 52, 390–420. https://doi.org/10.1353/mpq.2006.0032

Wang, C. H., Shannon, D., & Ross, M. (2013). Students’ characteristics, self-regulated learning, technology self-efficacy, and course outcomes in online learning. Distance Education, 34(3), 302–323. https://doi.org/10.1080/01587919.2013.835779

Winne, P. H. (1995). Self-regulation is ubiquitous, but its forms vary with knowledge. Educational Psychologist, 30(4), 223–228. https://doi.org/10.1207/s15326985ep3004_9

Winne, P. H. (1996). A metacognitive view of individual differences in self-regulated learning. Learning and Individual Differences, 8(4), 327–353. http://dx.doi.org/10.1016/S1041-6080(96)90022-9

Winne, P. H. (1997). Experimenting to bootstrap self-regulated learning. Journal of Educational Psychology, 89(3), 397–410. https://doi.org/10.1037/0022-0663.89.3.397

Winne, P. H. (2018). Theorizing and researching levels of processing in self-regulated learning. British Journal of Educational Psychology, 88(1), 9–20. https://doi.org/10.1111/bjep.12173

Winne, P. H., & Nesbit, J. C. (2010). The psychology of academic achievement. Annual Review of Psychology, 61, 653–678. https://doi.org/10.1146/annurev.psych.093008.100348

Winters, F. I., Greene, J. A., & Costich, C. M. (2008). Self-regulation of learning within computer-based learning environments: A critical analysis. Educational Psychology Review 20, 429–444. https://doi.org/10.1007/s10648-008-9080-9

Woolfolk, A. (2001). Educational psychology (8th ed.). Allyn and Bacon. Woolfolk, A., Winne, P. H. & Perry, N. E. (2006). Educational psychology (3rd Canadian ed.). Pearson.

Woolfolk, R. L., Doris, J. M., & Darley, J. M. (2006). Identification, situational constraint, and social cognition: Studies in the attribution of moral responsibility. Cognition, 100(2), 283–301. https://doi.org/10.1016/j.cognition.2005.05.002

Wong, J. Baars, M., Davis, D., Van Der Zee, T., Houben, G., & Paas, F. (2019). Supporting self-regulated learning in online learning environments and MOOCs: A systematic review. International Journal of Human-Computer Interaction, 35(4-5), 356–373. https://doi.org/10.1080/10447318.2018.1543084

Yeh, Y. F., Chen, M. C., Hung, P. H., & Hwang, G. J. (2010). Optimal self-explanation prompt design in dynamic multi-representational learning environments. Computers & Education, 54(4), 1089–1100. https://doi.org/10.1016/j.compedu.2009.10.013

Zhang, W. X., Hsu, Y. S., Wang, C. Y., & Ho, Y. T. (2015). Exploring the impacts of cognitive and metacognitive prompting on students’ scientific inquiry practices within an e-learning environment. International Journal of Science Education, 37(3), 529–553. https://doi.org/10.1080/09500693.2014.996796

Zheng, L. (2016). The effectiveness of self-regulated learning scaffolds on academic performance in computer-based learning environments: A meta-analysis. Asia Pacific Education Review, 17(2), 187–202. https://doi.org/10.1007/s12564-016-9426-9

Zimmerman, B. J. (1989). A social cognitive view of self-regulated academic learning. Journal of Educational Psychology, 81(3), 329–339. https://psycnet.apa.org/doi/10.1037/0022-0663.81.3.329

Zimmerman, B. J. (1990). Self-regulation learning and academic achievement: An overview. Educational Psychologist, 25(1), 3–17. https://doi.org/10.1207/s15326985ep2501_2

Zimmerman, B. J. (1998). Developing self-fulfilling cycles of academic self-regulation: An analysis of exemplary instructional models. In D. H. Schunk & B. J. Zimmerman (Eds.), Self-regulated learning: From teaching to self-reflective practice (pp. 1-19). Guilford Press.

Zimmerman, B. J. (2008). Investigating self-regulation and motivation: Historical background, methodological developments, and future prospects. American Educational Research Journal, 45(1), 166−183. https://doi.org/10.3102/0002831207312909

Zimmerman, B. J., & Kitsantas, A. (2014). Comparing students’ self-discipline and self-regulation measures and their prediction of academic achievement. Contemporary Educational Psychology, 39, 145–155. https://psycnet.apa.org/doi/10.1016/j.cedpsych.2014.03.004

Zimmerman, B. J., & Schunk, D. H. (2011). Self-regulated learning and performance: An introduction and an overview. In B. J. Zimmerman & D. H. Schunk (Eds.), Educational psychology handbook series. Handbook of self-regulation of learning and performance (pp. 1–12). Routledge/Taylor & Francis.

Published

2022-09-01

How to Cite

Mindrila, D., & Cao, L. (2022). Latent Profiles of Online Self-Regulated Learning: Relationships with Predicted and Final Course Grades. The International Review of Research in Open and Distributed Learning, 23(3), 212–239. https://doi.org/10.19173/irrodl.v23i2.5946

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Research Articles