Abstracts
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.
Keywords:
- online self-regulated learning,
- latent profile analysis,
- person-centered approach,
- variable-centered approach,
- higher education
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Bibliography
- 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.