Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Identifying university students’ online self-regulated learning profiles: predictors, outcomes, and differentiated instructional strategies

  • Published:
European Journal of Psychology of Education Aims and scope Submit manuscript

Abstract

Self-regulated learning (SRL) is critical in online learning, and profiling learners’ SRL patterns is needed to provide personalized support. However, little research has examined how each learner performs the cyclical phases of SRL based on trace data. To fill the gap, this study attempts to derive SRL profiles encompassing all cyclical phases of forethought, performance, and self-reflection based on learning analytics and establish specific SRL support by exploring profile membership predictors and distal outcomes. Through profiling 106 students in a university online course using Latent profile analysis (LPA), four distinctive SRL profile types emerged: Super Self-Regulated Learners, All-around Self-Regulated Learners, Unbalanced Self-Regulated Learners, and Minimally Self-Regulated Learners. Multinomial logistic regression analysis revealed that task value and teaching presence significantly predicted profile membership. Additionally, multivariate analysis of variance (MANOVA) showed that cognitive, affective, behavioral, and agentic engagement and learning achievement differed significantly among the four profiles. More instructional strategies for supporting SRL are described in the paper.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

Data availability

Due to the nature of this research and the ethics conditions of anonymity and data privacy, data are not available to researchers not named in the original ethics application.

References

  • Ali, W. (2020). Online and remote learning in higher education institutes: A necessity in light of COVID-19 pandemic. Higher Education Studies, 10(3), 16–25.

    Article  Google Scholar 

  • Araka, E., Oboko, R., Maina, E., & Gitonga, R. (2022). Using educational data mining techniques to identify profiles in self-regulated learning: An empirical evaluation. The International Review of Research in Open and Distributed Learning, 23(1), 131–162.

    Article  Google Scholar 

  • Atapattu, T., & Falkner, K. (2018). Impact of lecturer’s discourse for students’ video engagement: Video learning analytics case study of moocs. Journal of Learning Analytics, 5(3), 182–197.

    Article  Google Scholar 

  • Avcı, Ü., & Ergün, E. (2019). Online students’ LMS activities and their effect on engagement, information literacy and academic performance. Interactive Learning Environments, 30(1), 71–84.

    Article  Google Scholar 

  • Azevedo, R., & Aleven, V. (Eds.). (2013). International handbook of metacognition and learning technologies. Springer.

    Google Scholar 

  • Barnard-Brak, L., Paton, V. O., & Lan, W. Y. (2010). Profiles in self-regulated learning in the online learning environment. International Review of Research in Open and Distributed Learning, 11(1), 61–80.

    Article  Google Scholar 

  • 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(6), 1435–1455.

    Article  Google Scholar 

  • Broadbent, J., Panadero, E., Lodge, J. M., & de Barba, P. (2020). Technologies to enhance self-regulated learning in online and computer-mediated learning environments. In M. J. Bishop, E. Boling, J. Elen, & V. Svihla (Eds.), Handbook of research in educational communications and technology (pp. 37–52). Springer.

    Chapter  Google Scholar 

  • Çebi, A., & Güyer, T. (2020). Students’ interaction patterns in different online learning activities and their relationship with motivation, self-regulated learning strategy and learning performance. Education and Information Technologies, 25, 3975–3993.

    Article  Google Scholar 

  • Chen, I. S. (2017). Computer self-efficacy, learning performance, and the mediating role of learning engagement. Computers in Human Behavior, 72, 362–370.

    Article  Google Scholar 

  • Cheng, S. L., & Xie, K. (2021). Why college students procrastinate in online courses: A self-regulated learning perspective. The Internet and Higher Education, 50, 100807.

    Article  Google Scholar 

  • Chi, M. T., & Wylie, R. (2014). The ICAP framework: Linking cognitive engagement to active learning outcomes. Educational Psychologist, 49(4), 219–243.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Chou, M. H. (2021). Modeling the relations among task value, strategies, social factors, and English achievement. The Journal of Educational Research, 114(4), 357–366.

    Article  Google Scholar 

  • Cicchinelli, A., Veas, E., Pardo, A., Pammer-Schindler, V., Fessl, A., Barreiros, C., & Lindstädt, S. (2018). Finding traces of self-regulated learning in activity streams. Proceedings of the 8th international conference on learning analytics and knowledge 191–200 https://doi.org/10.1145/3170358.3170381

  • de Barba, P. G., Malekian, D., Oliveira, E. A., Bailey, J., Ryan, T., & Kennedy, G. (2020). The importance and meaning of session behaviour in a MOOC. Computers & Education, 146, 103772.

    Article  Google Scholar 

  • Du, J., Hew, K. F., & Liu, L. (2023). What can online traces tell us about students’ self-regulated learning? A systematic review of online trace data analysis. Computers & Education, 201, 104828.

    Article  Google Scholar 

  • Eccles, J. S., Adler, T., & Meece, J. L. (1984). Sex differences in achievement: A test of alternate theories. Journal of Personality and Social Psychology, 46(1), 26–43.

    Article  Google Scholar 

  • Frazier, L. D., Schwartz, B. L., & Metcalfe, J. (2021). The MAPS model of self-regulation: Integrating metacognition, agency, and possible selves. Metacognition and Learning, 16(2), 297–318.

    Article  Google Scholar 

  • Garrison, D. R., Cleveland-Innes, M., & Fung, T. S. (2010). Exploring causal relationships among teaching, cognitive and social presence: Student perceptions of the community of inquiry framework. The Internet and Higher Education, 13(1–2), 31–36.

    Article  Google Scholar 

  • Huang, L., & Lajoie, S. P. (2021). Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development. Computers & Education, 166, 104169.

    Article  Google Scholar 

  • Jansen, R. S., van Leeuwen, A., Janssen, J., Conijn, R., & Kester, L. (2020). Supporting learners’ self-regulated learning in Massive Open Online Courses. Computers & Education, 146, 103771.

    Article  Google Scholar 

  • Jung, T., & Wickrama, K. A. (2008). An introduction to latent class growth analysis and growth mixture modeling. Social and Personality Psychology Compass, 2(1), 302–317.

    Article  Google Scholar 

  • Kim, D., Jo, I. H., Song, D., Zheng, H., Li, J., Zhu, J., Huang, X., Yan, W., & Xu, Z. (2021). Self-regulated learning strategies and student video engagement trajectory in a video-based asynchronous online course: A Bayesian latent growth modeling approach. Asia Pacific Education Review, 22, 305–317.

    Article  Google Scholar 

  • Kim, D., Yoon, M., Jo, I. H., & Branch, R. M. (2018). Learning analytics to support self-regulated learning in asynchronous online courses: A case study at a women’s university in South Korea. Computers & Education, 127, 233–251.

    Article  Google Scholar 

  • Kim, J. H., & Kim, J. K. (2015). Relationship between task value, teaching presence, classroom environment and procrastinating behavior in university e-learning setting. Advanced Science and Technology Letters, 103, 66–71.

    Google Scholar 

  • Kim, R. (2021). Development of affordance-based MOOC interface design strategies to facilitate agentic engagement. [Unpublished doctoral dissertation]. Chung-Ang University, Seoul, South Korea

  • Kozan, K. (2016). The incremental predictive validity of teaching, cognitive and social presence on cognitive load. The Internet and Higher Education, 31, 11–19.

    Article  Google Scholar 

  • Kuo, T. M. L., Tsai, C. C., & Wang, J. C. (2021). Linking web-based learning self-efficacy and learning engagement in MOOCs: The role of online academic hardiness. The Internet and Higher Education, 51, 100819.

    Article  Google Scholar 

  • Lee, D., Watson, S. L., & Watson, W. R. (2020). The relationships between self-efficacy, task value, and self-regulated learning strategies in massive open online courses. International Review of Research in Open and Distributed Learning, 21(1), 23–39.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Li, H., Flanagan, B., Konomi, S. I., & Ogata, H. (2018). Measuring behaviors and identifying indicators of self-regulation in computer-assisted language learning courses. Research and Practice in Technology Enhanced Learning, 13, 1–12.

    Article  Google Scholar 

  • Li, K., Johnsen, J., & Canelas, D. A. (2021). Persistence, performance, and goal setting in massive open online courses. British Journal of Educational Technology, 52(3), 1215–1229.

    Article  Google Scholar 

  • Li, Q., & Baker, R. (2018). The different relationships between engagement and outcomes across participant subgroups in massive open online courses. Computers & Education, 127, 41–65.

    Article  Google Scholar 

  • Li, S., Chen, G., Xing, W., Zheng, J., & Xie, C. (2020). Longitudinal clustering of students’ self-regulated learning behaviors in engineering design. Computers & Education, 153, 103899.

    Article  Google Scholar 

  • Li, S., & Zheng, J. (2018). The relationship between self-efficacy and self-regulated learning in one-to-one computing environment: The mediated role of task values. The Asia-Pacific Education Researcher, 27(6), 455–463.

    Article  Google Scholar 

  • Liaw, S. S., & Huang, H. M. (2013). Perceived satisfaction, perceived usefulness and interactive learning environments as predictors to self-regulation in e-learning environments. Computers & Education, 60(1), 14–24.

    Article  Google Scholar 

  • Lin, J. W., Huang, H. H., & Chuang, Y. S. (2015). The impacts of network centrality and self-regulation on an e-learning environment with the support of social network awareness. British Journal of Educational Technology, 46(1), 32–44.

    Article  Google Scholar 

  • Littlejohn, A., Hood, N., Milligan, C., & Mustain, P. (2016). Learning in MOOCs: Motivations and self-regulated learning in MOOCs. The Internet and Higher Education, 29, 40–48.

    Article  Google Scholar 

  • Lo, Y., Mendell, N. R., & Rubin, D. B. (2001). Testing the number of components in a normal mixture. Biometrika, 88(3), 767–778.

    Article  Google Scholar 

  • Lubke, G. H., & Muthén, B. (2005). Investigating population heterogeneity with factor mixture models. Psychological Methods, 10(1), 21–39.

    Article  Google Scholar 

  • Maldonado-Mahauad, J., Pérez-Sanagustín, M., Kizilcec, R. F., Morales, N., & Munoz-Gama, J. (2018). Mining theory-based patterns from big data: Identifying self-regulated learning strategies in Massive Open Online Courses. Computers in Human Behavior, 80, 179–196.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Matos, L., Reeve, J., Herrera, D., & Claux, M. (2018). Students’ agentic engagement predicts longitudinal increases in perceived autonomy-supportive teaching: The squeaky wheel gets the grease. The Journal of Experimental Education, 86(4), 579–596.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Muthén, B. (2004). Latent variable analysis. In D. Kaplan (Ed.), The SAGE handbook of quantitative methodology for the social sciences (pp. 106–109). SAGE Publications.

    Google Scholar 

  • Ning, H. K., & Downing, K. (2015). A latent profile analysis of university students’ self-regulated learning strategies. Studies in Higher Education, 40(7), 1328–1346.

    Article  Google Scholar 

  • Özyurt, Ö., & Özyurt, H. (2015). Learning style based individualized adaptive e-learning environments: Content analysis of the articles published from 2005 to 2014. Computers in Human Behavior, 52, 349–358.

    Article  Google Scholar 

  • Pei, B., Xing, W., & Wang, M. (2021). Academic development of multimodal learning analytics: A bibliometric analysis. Interactive Learning Environments, 31(6), 3543–3561.

    Article  Google Scholar 

  • Pellas, N. (2014). The influence of computer self-efficacy, metacognitive self-regulation and self-esteem on student engagement in online learning programs: Evidence from the virtual world of Second Life. Computers in Human Behavior, 35, 157–170.

    Article  Google Scholar 

  • Pintrich, P. R. (1999). The role of motivation in promoting and sustaining self-regulated learning. International Journal of Educational Research, 31(6), 459–470.

    Article  Google Scholar 

  • Powell, S. T., & Leary, H. (2021). Measuring learner-content interaction in digitally augmented learning experiences. Distance Education, 42(4), 520–546.

    Article  Google Scholar 

  • Reeve, J., & Tseng, C. M. (2011). Agency as a fourth aspect of students’ engagement during learning activities. Contemporary Educational Psychology, 36(4), 257–267.

    Article  Google Scholar 

  • Reschly, A. L., & Christenson, S. L. (2012). Jingle, jangle, and conceptual haziness: Evolution and future directions of the engagement construct. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 3–19). Springer.

    Chapter  Google Scholar 

  • Rodrigues, R., Ramos, J., Silva, J., Dourado, R., & Gomes, A. (2019). Forecasting students’ performance through self-regulated learning behavioral analysis. International Journal of Distance Education Technologies, 17(3), 52–74.

    Article  Google Scholar 

  • Rosenberg, J. M., Beymer, P. N., Anderson, D. J., Van Lissa, C. J., & Schmidt, J. A. (2019). tidyLPA: An R package to easily carry out latent profile analysis (LPA) using open-source or commercial software. Journal of Open Source Software, 3(30), 978.

    Article  Google Scholar 

  • Saint, J., Gašević, D., Matcha, W., Uzir, N. A. A., & Pardo, A. (2020, March). Combining analytic methods to unlock sequential and temporal patterns of self-regulated learning. In Proceedings of the tenth international conference on learning analytics & knowledge (pp. 402–411).

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

  • 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.

    Article  Google Scholar 

  • Scrucca, L., Fop, M., Murphy, T. B., & Raftery, A. E. (2016). mclust 5: Clustering, classification and density estimation using Gaussian finite mixture models. The R Journal, 8(1), 289.

    Article  Google Scholar 

  • Sun, J. C. Y., & Rueda, R. (2012). Situational interest, computer self-efficacy and self-regulation: Their impact on student engagement in distance education. British Journal of Educational Technology, 43(2), 191–204.

    Article  Google Scholar 

  • Swan, K., Richardson, J. C., Ice, P., Garrison, D. R., Cleveland-Innes, M.,& Arbaugh, J. B. (2008). Validating a measurement tool of presence in online communities of inquiry.

  • Tang, H. (2021). Person-centered analysis of self-regulated learner profiles in MOOCs: A cultural perspective. Educational Technology Research and Development, 69(2), 1247–1269.

    Article  Google Scholar 

  • Tang, H., & Bao, Y. (2022). Self-regulated learner profiles in MOOCs: A cluster analysis based on the item response theory. Interactive Learning Environments, 32(5), 1877–1893.

    Google Scholar 

  • Valle, N., Antonenko, P., Valle, D., Dawson, K., Huggins-Manley, A. C., & Baiser, B. (2021). The influence of task-value scaffolding in a predictive learning analytics dashboard on learners’ statistics anxiety, motivation, and performance. Computers & Education, 173, 104288.

    Article  Google Scholar 

  • van Alten, D. C., Phielix, C., Janssen, J., & Kester, L. (2021). Secondary students’ online self-regulated learning during flipped learning: A latent profile analysis. Computers in Human Behavior, 118, 106676.

    Article  Google Scholar 

  • van Halem, N., van Klaveren, C., Drachsler, H., Schmitz, M., & Cornelisz, I. (2020). Tracking patterns in self-regulated learning using students’ self-reports and online trace data. Frontline Learning Research, 8(3), 140–163.

    Article  Google Scholar 

  • Vanslambrouck, S., Zhu, C., Pynoo, B., Lombaerts, K., Tondeur, J., & Scherer, R. (2019). A latent profile analysis of adult students’ online self-regulation in blended learning environments. Computers in Human Behavior, 99, 126–136.

    Article  Google Scholar 

  • Wang, C. H., Shannon, D. M., & Ross, M. E. (2013). Students’ characteristics, self-regulated learning, technology self-efficacy, and course outcomes in online learning. Distance Education, 34(3), 302–323.

    Article  Google Scholar 

  • Wang, Y., & Liu, Q. (2020). Effects of online teaching presence on students’ interactions and collaborative knowledge construction. Journal of Computer Assisted Learning, 36(3), 370–382.

    Article  Google Scholar 

  • Wang, Y., Stein, D., & Shen, S. (2021). Students’ and teachers’ perceived teaching presence in online courses. Distance Education, 42(3), 373–390.

    Article  Google Scholar 

  • Wickham, H. (2014). Tidy Data. Journal of Statistical Software, 59(10), 1–23.

    Article  Google Scholar 

  • Wigfield, A. (1994). Expectancy-value theory of achievement motivation: A developmental perspective. Educational Psychology Review, 6(1), 49–78.

    Article  Google Scholar 

  • Wigfield, A., & Eccles, J. S. (1992). The development of achievement task values: A theoretical analysis. Developmental Review, 12(3), 265–310.

    Article  Google Scholar 

  • Wolters, C. A., & Hussain, M. (2015). Investigating grit and its relations with college students’ self-regulated learning and academic achievement. Metacognition and Learning, 10(3), 293–311.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Wong, J., Baars, M., He, M., de Koning, B. B., & Paas, F. (2021). Facilitating goal setting and planning to enhance online self-regulation of learning. Computers in Human Behavior, 124, 106913.

    Article  Google Scholar 

  • Ye, D., & Pennisi, S. (2022). Using trace data to enhance students’ self-regulation A learning analytics perspective. The Internet and Higher Education, 54, 100855.

    Article  Google Scholar 

  • Yoon, M., Hill, J., & Kim, D. (2021). Designing supports for promoting self-regulated learning in the flipped classroom. Journal of Computing in Higher Education, 33, 398–418.

    Article  Google Scholar 

  • Yun, H., & Oh, S. (2021). Online learners’ motivational regulation profiles affecting cognitive learning and learning engagement. Journal of the Korean Association of Information Education, 25(1), 91–101.

    Article  Google Scholar 

  • Zhang, H., Lin, L., Zhan, Y., & Ren, Y. (2016). The impact of teaching presence on online engagement behaviors. Journal of Educational Computing Research, 54(7), 887–900.

    Article  Google Scholar 

  • Zhang, X., Meng, Y., de Pablos, P. O., & Sun, Y. (2019). Learning analytics in collaborative learning supported by Slack: From the perspective of engagement. Computers in Human Behavior, 92, 625–633.

    Article  Google Scholar 

  • Zhang, Y., & Lin, C. H. (2021). Effects of community of inquiry, learning presence and mentor presence on K-12 online learning outcomes. Journal of Computer Assisted Learning, 37(3), 782–796.

    Article  Google Scholar 

  • Zheng, J., Xing, W., Zhu, G., Chen, G., Zhao, H., & Xie, C. (2020). Profiling self-regulation behaviors in STEM learning of engineering design. Computers & Education, 143, 103669.

    Article  Google Scholar 

  • 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). Academic Press.

    Chapter  Google Scholar 

  • Zimmerman, B. J. (2011). Handbook of self-regulation of learning and performance. Routledge.

    Google Scholar 

  • Zimmerman, B. J., & Schunk, D. H. (2001). Self-regulated learning and academic achievement: Theoretical perspectives (2nd Ed.). Routledge.

  • Coates, H. (2005). The value of student engagement for higher education quality assurance. Quality in Higher Education, 11(1), 25–36.

  • Delfino, et al. (2010). An online course fostering self-regulation of trainee teachers. Psicothema, 22(2), 299–305.

    Google Scholar 

  • Situational interest, computer self-efficacy and self-regulation: Their impact on student engagement in distance education. (2012). British Journal of Educational Technology, 43(2), 191–204.

  • Jung, T., & Wickrama, K. A. (2008). An introduction to latent class growth analysis and growth mixture modeling. Social and Personality Psychology Compass, 2(1), 302–317.

Download references

Acknowledgements

The authors appreciate the respondents who participated in this research. The paper was developed based on the master’s thesis by the first author.

Funding

This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2020S1A3A2A02091529).

Author information

Authors and Affiliations

  1. Department of Education, Chung-Ang University, Seoul, Republic of Korea

    Hyejoo Yun, Hae-Deok Song & YeonKyoung Kim

Authors
  1. Hyejoo Yun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hae-Deok Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. YeonKyoung Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to YeonKyoung Kim.

Ethics declarations

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

This study was approved by the Ethics Committee of the Chung-Ang University (1041078-202109-HR-274-01). Informed consent was obtained from all individual participants included in the study. All methods followed by relevant guidelines and regulations.

Most relevant publications

No previous publications.

Additional information

Hyejoo Yun

Department of Education, Chung-Ang University, Seoul, Republic of Korea. E-mail: lvlyhj529@cau.ac.kr

Current themes of research:

Learning Sciences, Multimodal Learning Analytics, AI in Education.

Most relevant publications in the field of Psychology of Education:

Kang, Y. G., Song, H. D., Yun, H., & Jo, Y. (2022). The effect of virtual reality media characteristics on flow and learning transfer in job training: The moderating effect of presence. Journal of Computer Assisted Learning, 38(6), 1674–1685.

Hae-Deok Song

Department of Education, Chung-Ang University, Seoul, Republic of Korea. E-mail: hsong@cau.ac.kr

Current themes of research:

Learning Engagement, Employee Engagement, Affordance, e-Learning.

Most relevant publications in the field of Psychology of Education:

Kim, R., & Song, H. D. (2023). Developing an agentic engagement scale in a self-paced MOOC. Distance Education, 44(1), 120–136.

Kang, Y. G., Song, H. D., Yun, H., & Jo, Y. (2022). The effect of virtual reality media characteristics on flow and learning transfer in job training: The moderating effect of presence. Journal of Computer Assisted Learning, 38(6), 1674–1685.

YeonKyoumg Kim

Department of Education, Chung-Ang University, Seoul, Republic of Korea. E-mail: yeon@cau.ac.kr

Current themes of research:

Learning Engagement, Technology-based Learning, Instructional Design.

Most relevant publications in the field of Psychology of Education:

Hong, H., & Kim, Y. (2024). Applying artificial intelligence in career education for students with intellectual disabilities: The effects on career self-efficacy and learning flow. Education and Information Technologies. https://doi.org/10.1007/s10639-024-12809-6

Lee, Y., Song, H. D., & Kim, Y. (2023). MOOC learners' expectancy-value-cost latent profile analysis: Relationship between learning persistence and student engagement. In T. Bastiaens (Ed.), Proceedings of EdMedia + Innovate Learning (pp. 1134–1139). Vienna, Austria: Association for the Advancement of Computing in Education (AACE).

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yun, H., Song, HD. & Kim, Y. Identifying university students’ online self-regulated learning profiles: predictors, outcomes, and differentiated instructional strategies. Eur J Psychol Educ 40, 5 (2025). https://doi.org/10.1007/s10212-024-00907-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10212-024-00907-5

Keywords

Search

Navigation