Students’ interaction patterns in different online learning activities and their relationship with motivation, self-regulated learning strategy and learning performance

Abstract

In this study, students’ interactions with different learning activities are examined and the relation among learning performance with different interaction patterns, learning performance, self-regulated learning (SRL) strategies and motivation is presented. Learning materials including different kinds of activities are prepared and presented to the use of 122 university students. As a result of the study that students spent longer time in the tutorial and video activities and they visit these activities more frequently. As a result of cluster analysis, students with the least interaction with learning activities take place in the first cluster, students who use video, example and forum activities to an intense take place in the second cluster, and students who spend more time in tutorial, exercise and concept map activities take place in the third cluster. The academic performances of students, who spend longer time in learning activities, are higher. Students in the third cluster have higher points in terms of intrinsic goal orientations, task value, control beliefs and self-efficacy for learning and performance. Finally, the results of this study show that SRL strategies differ from its sub-dimensions in terms of rehearsal, organization, elaboration, metacognitive self-regulation, time and study environment.

This is a preview of subscription content, access via your institution.

References

  1. Bienkowski, M., Feng, M., & Means, B. (2012). Enhancing teaching and learning through educational data mining and learning analytics: An issue brief. US Department of Education, Office of Educational Technology, 1, 1–57.

    Google Scholar 

  2. Boekaerts, M. (1999). Self-regulated learning: Where we are today. International Journal of Educational Research, 31(6), 445–557. https://doi.org/10.1016/S0883-0355(99)00014-2.

    Article  Google Scholar 

  3. Broadbent, J. (2017). Comparing online and blended learner's self-regulated learning strategies and academic performance. The Internet and Higher Education, 33, 24–32. https://doi.org/10.1016/j.iheduc.2017.01.004.

    Article  Google Scholar 

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

    Article  Google Scholar 

  5. Butler, D. L., & Winne, P. H. (1995). Feedback and self-regulated learning: A theoretical synthesis. Review of Educational Research, 65, 245–281. https://doi.org/10.3102/00346543065003245.

    Article  Google Scholar 

  6. Büyüköztürk, Ş., Akgün, Ö. E., Özkahveci, Ö., & Demirel, F. (2004). Güdülenme ve öğrenme stratejileri ölçeğinin Türkçe formunun geçerlik ve güvenirlik çalışması [the validity and reliability study of the Turkish version of the motivated strategies for learning questionnaire]. Kuram ve Uygulamada Eğitim Bilimleri [Educational Sciences: Theory & Practice], 4(2), 207–239.

    Google Scholar 

  7. Cerezo, R., Sánchez-Santillán, M., Paule-Ruiz, M. P., & Núñez, J. C. (2016). Students' LMS interaction patterns and their relationship with achievement: A case study in higher education. Computers & Education, 96, 42–54. https://doi.org/10.1016/j.compedu.2016.02.006.

    Article  Google Scholar 

  8. Cho, M.-H., Kim, Y., & Choi, D. (2017). The effect of self-regulated learning on college students’ perceptions of community of inquiry and affective outcomes in online learning. The Internet and Higher Education, 34, 10–17. https://doi.org/10.1016/j.iheduc.2017.04.001.

    Article  Google Scholar 

  9. Cook, D. A., & Artino, A. R. (2016). Motivation to learn: An overview of contemporary theories. Medical Education, 50(10), 997–1014. https://doi.org/10.1111/medu.13074.

    Article  Google Scholar 

  10. Dabbagh, N., & Kitsantas, A. (2013). Using learning management systems as metacognitive tools to support self-regulation in higher education contexts. In International handbook of metacognition and learning technologies (pp. 197–211). Springer, New York, NY.

  11. de Barba, P. G., Kennedy, G. E., & Ainley, M. D. (2016). The role of students' motivation and participation in predicting performance in a MOOC. Journal of Computer Assisted Learning, 32(3), 218–231. https://doi.org/10.1111/jcal.12130.

    Article  Google Scholar 

  12. Effeney, G., Carroll, A., & Bahr, N. (2013). Self-regulated learning: Key strategies and their sources in a sample of adolescent males. Australian Journal of Educational & Developmental Psychology, 13, 58–74.

    Google Scholar 

  13. Giesbers, B., Rienties, B., Tempelaar, D., & Gijselaers, W. (2013). Investigating the relations between motivation, tool use, participation, and performance in an e-learning course using web-videoconferencing. Computers in Human Behavior, 29(1), 285–292. https://doi.org/10.1016/j.chb.2012.09.005.

    Article  Google Scholar 

  14. Giesbers, B., Rienties, B., Tempelaar, D., & Gijselaers, W. (2014). A dynamic analysis of the interplay between asynchronous and synchronous communication in online learning: The impact of motivation. Journal of Computer Assisted Learning, 30(1), 30–50. https://doi.org/10.1111/jcal.12020.

    Article  Google Scholar 

  15. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). NY: Prentice Hall.

    Google Scholar 

  16. Hartnett, M. (2016). Motivation in online education. Singapore: Springer.

    Google Scholar 

  17. Hellas, A., Ihantola, P., Petersen, A., Ajanovski, V. V., Gutica, M., Hynninen, T., & Liao, S. N. (2018). Predicting academic performance: a systematic literature review. In Proceedings Companion of the 23rd Annual ACM Conference on Innovation and Technology in Computer Science Education (pp. 175–199). ACM.

  18. Jo, I. H., Yu, T., Lee, H., & Kim, Y. (2015). Relations between student online learning behavior and academic achievement in higher education: A learning analytics approach. In Emerging issues in smart learning (pp. 275–287). Springer, Berlin, Heidelberg.

  19. Jo, I. H., Park, Y., Yoon, M., & Sung, H. (2016). Evaluation of online log variables that estimate learners’ time Management in a Korean Online Learning Context. The International Review of Research in Open and Distributed Learning, 17(1), 195–213. https://doi.org/10.19173/irrodl.v17i1.2176.

    Article  Google Scholar 

  20. Kitsantas, A., Robert, A. R., & Doster, J. (2004). Developing self-regulated learners: Goal setting, self-evaluation, and organizational signals during acquisition of procedural skills. The Journal of Experimental Education, 72(4), 269–287. https://doi.org/10.3200/JEXE.72.4.269-287.

    Article  Google Scholar 

  21. Kizilcec, R. F., Pérez-Sanagustín, M., & Maldonado, J. J. (2017). Self-regulated learning strategies predict learner behavior and goal attainment in massive open online courses. Computers & Education, 104, 18–33. https://doi.org/10.1016/j.compedu.2016.10.001.

    Article  Google Scholar 

  22. Li, L. Y., & Tsai, C. C. (2017). Accessing online learning material: Quantitative behavior patterns and their effects on motivation and learning performance. Computers & Education, 114, 286–297. https://doi.org/10.1016/j.compedu.2017.07.007.

    Article  Google Scholar 

  23. Lust, G., Vandewaetere, M., Ceulemans, E., Elen, J., & Clarebout, G. (2011). Tool-use in a blended undergraduate course: In search of user profiles. Computers & Education, 57(3), 2135–2144. https://doi.org/10.1016/j.compedu.2011.05.010.

    Article  Google Scholar 

  24. Lust, G., Collazo, N. A. J., Elen, J., & Clarebout, G. (2012). Content management systems: Enriched learning opportunities for all? Computers in Human Behavior, 28(3), 795–808. https://doi.org/10.1016/j.chb.2011.12.009.

    Article  Google Scholar 

  25. Lust, G., Elen, J., & Clarebout, G. (2013). Students’ tool-use within a web enhanced course: Explanatory mechanisms of students’ tool-use pattern. Computers in Human Behavior, 29(5), 2013–2021. https://doi.org/10.1016/j.chb.2013.03.014.

    Article  Google Scholar 

  26. Macfadyen, L. P., & Dawson, S. (2010). Mining LMS data to develop an “early warning system” for educators: A proof of concept. Computers & Education, 54(2), 588–599. https://doi.org/10.1016/j.compedu.2009.09.008.

    Article  Google Scholar 

  27. Macfadyen, L. P., & Dawson, S. (2012). Numbers are not enough. Why e-learning analytics failed to inform an institutional strategic plan. Educational Technology & Society, 15(3), 149–163 Retrieved from http://www.jstor.org/stable/jeductechsoci.15.3.149.

    Google Scholar 

  28. Pintrich, P. R. (2000). The role of goal orientation in self-regulated learning. In M. Boekaerts, P. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 451–502). San Diego: Academic.

    Google Scholar 

  29. Pintrich, P. R., & Schunk, D. H. (2002). Motivation in education: Theory, research, and applications. Prentice Hall.

  30. Pintrich, P. R., Smith, D. A., Garcia, T., & McKeachie, W. J. (1991). A manual for the use of the motivated strategies for learning questionnaire (MSLQ). Ann Arbor, MI: The University of Michigan.

    Google Scholar 

  31. Pintrich, P. R., Smith, D. A., Garcia, T., & McKeachie, W. J. (1993). Reliability and predictive validity of the motivated strategies for learning questionnaire (MSLQ). Educational and Psychological Measurement, 53(3), 801–813. https://doi.org/10.1177/0013164493053003024.

    Article  Google Scholar 

  32. Recker, M., & Lee, J. E. (2016). Analyzing learner and instructor interactions within learning management systems: Approaches and examples. Learning, Design, and Technology: An International Compendium of Theory, Research, Practice, and Policy, 1-23. https://doi.org/10.1007/978-3-319-17727-4_7-1.

  33. Renkl, A. (2002). Worked-out examples: Instructional explanations support learning by self-explanations. Learning and Instruction, 12(5), 529–556. https://doi.org/10.1016/S0959-4752(01)00030-5.

    Article  Google Scholar 

  34. Schunk, D. H., Meece, J. L., & Pintrich, P. R. (2014). Motivation in education: Theory, research, and applications (4th ed.). Boston, MA: Pearson.

  35. Shapiro, H. B., Lee, C. H., Roth, N. E. W., Li, K., Çetinkaya-Rundel, M., & Canelas, D. A. (2017). Understanding the massive open online course (MOOC) student experience: An examination of attitudes, motivations, and barriers. Computers & Education, 110, 35–50. https://doi.org/10.1016/j.compedu.2017.03.003.

    Article  Google Scholar 

  36. Silber, K. H., & Foshay, W. R. (2006). Designing instructional strategies: A cognitive perspective. In J. A. Pershing (Ed.), Handbook of human performance technology (3rd ed., pp. 370–413). San Francisco: Pfeiffer.

    Google Scholar 

  37. Şahin, M., Keskin, S., Özgür, A., & Yurdugül, H. (2017). E-Öğrenme ortamlarında öğrenen özelliklerine dayalı etkileşim profillerinin belirlenmesi [determination of interaction profiles based on learner characteristics in e-learning environment]. Eğitim Teknolojisi Kuram ve Uygulama [Educational Technology Theory and Practice], 7(2), 172–192. https://doi.org/10.17943/etku.297075.

  38. Tsai, Y. H., Lin, C. H., Hong, J. C., & Tai, K. H. (2018). The effects of metacognition on online learning interest and continuance to learn with MOOCs. Computers & Education, 121, 18–29. https://doi.org/10.1016/j.compedu.2018.02.011.

  39. Tseng, H., Yi, X., & Yeh, H. T. (2019). Learning-related soft skills among online business students in higher education: Grade level and managerial role differences in self-regulation, motivation, and social skill. Computers in Human Behavior, 95, 179–186. https://doi.org/10.1016/j.chb.2018.11.035.

  40. Winne, P. H., & Hadwin, A. F. (1998). Studying as self-regulated learning. In D. J. Hacker & J. Dunlosky (Eds.), Metacognition in educational theory and practice, the educational psychology series. Mahwah, NJ: Erlbaum.

    Google Scholar 

  41. Wolters, C. A., Pintrich, P. R., & Karabenick, S. A. (2005). Assessing academic self-regulated learning. In What do children need to flourish? (pp. 251–270). Springer, Boston, MA.

  42. Wong, J., Baars, M., Davis, D., van der Zee, T., Houben, G. J., & 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.

    Article  Google Scholar 

  43. You, J. W. (2016). Identifying significant indicators using LMS data to predict course achievement in online learning. The Internet and Higher Education, 29, 23–30. https://doi.org/10.1016/j.iheduc.2015.11.003.

  44. Yu, T., & Jo, I. H. (2014). Educational technology approach toward learning analytics: Relationship between student online behavior and learning performance in higher education. In Proceedings of the Fourth International Conference on Learning Analytics and Knowledge (pp. 269-270). ACM.

  45. Yukselturk, E. (2010). An investigation of factors affecting student participation level in an online discussion forum. Turkish Online Journal of Educational Technology-TOJET, 9(2), 24–32 Retrieved from https://files.eric.ed.gov/fulltext/EJ897999.pdf.

    Google Scholar 

  46. Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 13–39). San Diego, USA: Academic.

    Google Scholar 

Download references

Acknowledgements

Preliminary findings of the study were presented as an abstract at 12th International Computer and Instructional Technologies Symposium (ICITS 2018).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Ayça Çebi.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Çebi, A., Güyer, T. Students’ interaction patterns in different online learning activities and their relationship with motivation, self-regulated learning strategy and learning performance. Educ Inf Technol 25, 3975–3993 (2020). https://doi.org/10.1007/s10639-020-10151-1

Download citation

Keywords

  • Interaction patterns
  • Motivation
  • SRL strategies
  • Learning performance