A Cross-Cultural Examination of the Online Self-regulated Learning Questionnaire (OSLQ) with Korean College Students

Authors

  • Chungsoo Na Utah State University
  • Soojeong Jeong
  • Jody Clarke-Midura
  • Wilhelmina van Dijk

DOI:

https://doi.org/10.24059/olj.v29i4.5216

Keywords:

Online Learning, Self-regulated Learning, Online Self-Regulated Learning, Questionnaire, OSLQ, Item Factor Analysis

Abstract

The Online Self-Regulated Learning Questionnaire (OSLQ) is a widely used self-report instrument for assessing student self-regulated learning (SRL). Despite its prevalence, the dimensionality of the OSLQ is often unclear across different populations, and its item-level characteristics remain underexplored. This study investigates the psychometric properties of the OSLQ with a sample of 571 Korean college students, using both confirmatory factor analysis (CFA) and item factor analysis (IFA). CFA results supported a seven-factor model over the original six-factor version. Furthermore, IFA results revealed that the OSLQ items have high item discrimination, a wide range of item difficulties, providing strong marginal reliability for students within a latent ability (θ) range of -2.5 to 1.5. A key finding was that students with moderate to low SRL ability tend to overestimate their skills. These findings confirm the OSLQ’s psychometric robustness and cultural relevance for Korean college students, particularly for assessing those with lower ability levels, while also highlighting the limitations of self-report measures.

References

Azevedo, R. (2015). Defining and measuring engagement and learning in science: Conceptual, theoretical, methodological, and analytical issues. Educational Psychologist, 50(1), 84–94. https://doi.org/10.1080/00461520.2015.1004069

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

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. https://doi.org/10.19173/irrodl.v11i1.769

Beik, A., & Cho, Y. (2024). Effects of goal orientation on online learning: A meta-analysis of differences in Korea and US. Current Psychology, 43(2), 1496-1506. https://doi.org/10.1007/s12144-023-04389-4

Broadbent, J., & Poon, W. L. (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

Brislin, R. W. (1970). Back-translation for cross-cultural research. Journal of Cross-Cultural Psychology, 1(3), 185-216. https://doi.org/10.1177/135910457000100301

Cheng, Z., Zhang, Z., Xu, Q., Maeda, Y., & Gu, P. (2025). A meta-analysis addressing the relationship between self-regulated learning strategies and academic performance in online higher education. Journal of Computing in Higher Education, 37(1), 195-224. https://doi.org/10.1007/s12528-023-09390-1

Cho, H. J., Costa, E., Menezes, P. R., Chalder, T., Bhugra, D., & Wessely, S. (2007). Cross-cultural validation of the Chalder Fatigue Questionnaire in Brazilian primary care. Journal of Psychosomatic Research, 62(3), 301-304. https://doi.org/10.1016/j.jpsychores.2006.10.018

de Mooij, S., Lämsä, J., Lim, L., Aksela, O., Athavale, S., Bistolfi, I., ... & Molenaar, I. (2025). A systematic review of self-regulated learning through integration of multimodal data and artificial intelligence. Educational Psychology Review, 37(2), 1-27. https://doi.org/10.1007/s10648-025-10028-0

Dunlosky, J., & Metcalfe, J. (2008). Metacognition. Sage Publications.

Dunn, K., & Hayakawa, T. (2021). Destination irrational procrastination: An exploration of the role of attributional thinking and self-regulation on procrastination in synchronous online graduate studies. Online Learning, 25(2), 276-290. https://doi.org/10.24059/olj.v24i4.2205

Fan, Y., van der Graaf, J., Lim, L., Raković, M., Singh, S., Kilgour, J., ... & Gašević, D. (2022). Towards investigating the validity of measurement of self-regulated learning based on trace data. Metacognition and Learning, 17(3), 949-987. https://doi.org/10.1007/s11409-022-09291-1

Ferrando, P. J. (2009). Difficulty, discrimination, and information indices in the linear factor analysis model for continuous item responses. Applied Psychological Measurement, 33(1), 9–24. https://doi.org/10.1177/014662160831460

Funa, A. A., Gabay, R. A. E., Deblois, E. C. B., Lerios, L. D., & Jetomo, F. G. J. (2023). Exploring Filipino preservice teachers' online self-regulated learning skills and strategies amid the COVID-19 pandemic. Social Sciences & Humanities Open, 7(1), 100470. https://doi.org/10.1016/j.ssaho.2023.100470

Fung, J. J., Yuen, M., & Yuen, A. H. (2018). Validity evidence for a Chinese version of the online self-regulated learning questionnaire with average students and mathematically talented students. Measurement and Evaluation in Counseling and Development, 51(2), 111-124. https://doi.org/10.1080/07481756.2017.1358056

Guntur, M., & Purnomo, Y. W. (2024). A meta-analysis of self-regulated learning interventions studies on learning outcomes in online and blended environments. Online Learning, 28(3), 563-584. https://doi.org/10.24059/olj.v28i3.4025

Hacker, D. J., & Bol, L. (2019). Calibration and self-regulated learning: Making the connections. In J. Dunlosky & K. A. Rawson (Eds.), The Cambridge handbook of cognition and education (pp. 647–677). Cambridge University Press.

Hayes, S., Smith, S. U., & Shea, P. (2015). Expanding learning presence to account for the direction of regulative intent: self-, co-and shared regulation in online learning. Online Learning, 19(3), 15-31. https://doi.org/10.24059/olj.v19i3.530

Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55. https://doi.org/10.1080/10705519909540118

Järvelä, S., Nguyen, A., & Molenaar, I. (2023). Advancing SRL research with artificial intelligence. Computers in Human Behavior, 147, 107847. https://doi.org/10.1016/j.chb.2023.107847

Jeong, S., & Feldon, D. F. (2023). Changes in self-regulated learning profiles during an undergraduate peer-based intervention: A latent profile transition analysis. Learning and Instruction, 83, 101710. https://doi.org/10.1016/j.learninstruc.2022.101710

Joo, Y. J., Joung, S., & Kim, J. (2014). Structural relationships among self-regulated learning, learning flow, satisfaction, and learning persistence in cyber universities. Interactive Learning Environments, 22(6), 752-770. https://doi.org/10.1080/10494820.2012.745421

Kilis, S., & Yıldırım, Z. (2018). Online self-regulation questionnaire: Validity and reliability study of Turkish translation. Cukurova University Faculty of Education Journal, 47(1), 233-245. https://doi.org/10.14812/cuefd.298791

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

Lau, K. L. (2022). Adaptation and validation of a Chinese online self-regulated learning questionnaire. Journal of Psychoeducational Assessment, 40(3), 438-444. https://doi.org/10.1177/07342829211059979

Lai, C. L., & Hwang, G. J. (2016). A self-regulated flipped classroom approach to improving students’ learning performance in a mathematics course. Computers & Education, 100, 126-140. https://doi.org/10.1016/j.compedu.2016.05.006

Lee, H. J., Lee, J., Makara, K. A., Fishman, B. J., & Teasley, S. D. (2017). A cross-cultural comparison of college students' learning strategies for academic achievement between South Korea and the USA. Studies in Higher Education, 42(1), 169-183. https://doi.org/10.1080/03075079.2015.1045473

Li, Q., Baker, R., & Warschauer, M. (2020). Using clickstream data to measure, understand, and support self-regulated learning in online courses. The Internet and Higher Education, 45, 100727. https://doi.org/10.1016/j.iheduc.2020.100727

Lin, F., Lim, L., Ho, Y. Y., Tan, B. Z., & Lim, W. Y. (2023). Validating and applying an adapted OSLQ to examine adult learners’ online self-regulation. Higher Education Research & Development, 42(7), 1684-1699. https://doi.org/10.1080/07294360.2023.2183938

Lovett, B. J. (2023). Practical psychometrics: A guide for test users. The Guilford Press.

Malmberg, J., Fincham, O., Pijeira-Díaz, H. J., Järvelä, S., & Gašević, D. (2021). Revealing the hidden structure of physiological states during metacognitive monitoring in collaborative learning. Journal of Computer Assisted Learning, 37(3), 861–874. https://doi.org/10.1111/jcal.12529

Martinez-Lopez, R., Yot, C., Tuovila, I., & Perera-Rodríguez, V. H. (2017). Online self-regulated learning questionnaire in a Russian MOOC. Computers in Human Behavior, 75, 966-974. https://doi.org/10.1016/j.chb.2017.06.015

McInerney, D. M. (2008). The motivational roles of cultural differences and cultural identity in self-regulated learning. In D. H. Schunk & B. J. Zimmerman (Eds.), Motivation and self-regulated learning: Theory, research, and applications (pp. 369–400). Lawrence Erlbaum Associates Publishers.

McManus, T. F. (2000). Individualizing instruction in a web-based hypermedia learning environment: Nonlinearity, advance organizers, and self-regulated learners. Journal of Interactive Learning Research, 11(2), 219-251. https://www.learntechlib.org/primary/p/8486

Miles, J., & Shevlin, M. (2001). Applying regression and correlation: A guide for students and researchers. SAGE.

Molenaar, I., de Mooij, S., Azevedo, R., Bannert, M., Järvelä, S., & Gašević, D. (2023). Measuring self-regulated learning and the role of AI: Five years of research using multimodal multichannel data. Computers in Human Behavior, 139, 107540. https://doi.org/10.1016/j.chb.2022.107540

Muthén, L.K. and Muthén, B.O. (1998-2017). Mplus User’s Guide (Version 8) [Computer Software]. Muthén & Muthén.

Na, C., Jeong, S., Clarke-Midura, J., & Shin, Y. (2024). Linking self-regulated learning to community of inquiry in online undergraduate courses: A person-centered approach. Educational Technology Research & Development, 72(6), 2895–2920. https://doi.org/10.1007/s11423-024-10380-y

Pintrich, P. R. (2000). The role of goal orientation in self-regulated learning. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 451–502). Academic Press. https://doi.org/10.1016/B978-012109890-2/50043-3

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

Purdie, N., Hattie, J., & Douglas, G. (1996). Student conceptions of learning and their use of self-regulated learning strategies: A cross-cultural comparison. Journal of Educational Psychology, 88(1), 87–100. https://doi.org/10.1037/0022-0663.88.1.87

Rhemtulla, M., Brosseau-Liard, P. É., & Savalei, V. (2012). When can categorical variables be treated as continuous? A comparison of robust continuous and categorical SEM estimation methods under suboptimal conditions. Psychological Methods, 17(3), 354–373. https://doi.org/10.1037/a0029315

Roth, A., Ogrin, S., & Schmitz, B. (2016). Assessing self-regulated learning in higher education: A systematic literature review of self-report instruments. Educational Assessment, Evaluation and Accountability, 28, 225-250. https://doi.org/10.1007/s11092-015-9229-2

Rufini, S. É., Fernandes, J. G., Bianchini, L. G. B., & Alliprandini, P. M. Z. (2021). Brazilian version of Online Self-Regulated Learning Questionnaire (OSLQ): Evidence of validity. Psicologia: Teoria e Pesquisa, 37, e37547. https://dx.doi.org/10.1590/0102.3772e37547

Samejima, F. (2016). Graded response models. In W. J. van der Linden (Ed.), Handbook of item response theory (Vol. 1, pp. 123-136). Chapman and Hall/CRC. https://doi.org/10.1007/978-1-4757-2691-6_5

Satorra, A., & Bentler, P. M. (2010). Ensuring positiveness of the scaled difference chi-square test statistic. Psychometrika, 75(2), 243-248. https://doi.org/10.1007/s11336-009-9135-y

Sharma, K., Nguyen, A., & Hong, Y. (2024). Self‐regulation and shared regulation in collaborative learning in adaptive digital learning environments: A systematic review of empirical studies. British Journal of Educational Technology, 55(4), 1398-1436. https://doi.org/10.1111/bjet.13459

Sobocinski, M., Dever, D., Wiedbusch, M., Mubarak, F., Azevedo, R., & Järvelä, S. (2024). Capturing self‐regulated learning processes in virtual reality: Causal sequencing of multimodal data. British Journal of Educational Technology, 55(4), 1486-1506. https://doi.org/10.1111/bjet.13393

The jamovi project (2025). jamovi (Version 2.5) [Computer Software]. https://www.jamovi.org.

Topping, K. J. (2009). Peer assessment. Theory into Practice, 48(1), 20-27. https://doi.org/10.1080/00405840802577569

Turingan, J. P., & Yang, Y. C. (2009). A cross-cultural comparison of self-regulated learning skills between Korean and Filipino college students. Asian Social Science, 5(12), 3–10. https://doi.org/10.5539/ass.v5n12p3

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. https://doi.org/10.1016/j.chb.2019.05.021

Veenman, M. V. J. (2011). Alternative assessment of strategy use with self-report instruments: A discussion. Metacognition and Learning, 6(2), 205–211. https://doi.org/10.1007/s11409-011-9080-x

Winne, P. H., & Perry, N. E. (2000). Measuring self-regulated learning. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 531–566). Academic Press. https://doi.org/10.1016/B978-012109890-2/50045-7

Wirth, R. J., & Edwards, M. C. (2007). Item factor analysis: current approaches and future directions. Psychological Methods, 12(1), 58-79. https://doi.org/10.1037/1082-989X.12.1.58

Yeh, Y. C., Kwok, O. M., Chien, H. Y., Sweany, N. W., Baek, E., & McIntosh, W. A. (2019). How college students' achievement goal orientations predict their expected online learning outcome: The mediation roles of self-regulated learning strategies and supportive online learning behaviors. Online Learning, 23(4), 23-41. https://doi.org/10.24059/olj.v23i4.2076

Yoo, S., Jeong Kim, H., & Young Kwon, S. (2014). Between ideal and reality: A different view on online-learning interaction in a cross-national context. Journal for Multicultural Education, 8(1), 13–30. https://doi.org/10.1108/JME-09-2013-0043

Zhao, Y., Li, Y., Ma, S., Xu, Z., & Zhang, B. (2025). A meta-analysis of the correlation between self-regulated learning strategies and academic performance in online and blended learning environments. Computers & Education, 230, 105279. https://doi.org/10.1016/j.compedu.2025.105279

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. https://doi.org/10.1016/B978-012109890-2/50031-7

Downloads

Published

2025-12-01

How to Cite

Na, C., Jeong, S., Clarke-Midura, J., & van Dijk, W. (2025). A Cross-Cultural Examination of the Online Self-regulated Learning Questionnaire (OSLQ) with Korean College Students. Online Learning, 29(4), 174–198. https://doi.org/10.24059/olj.v29i4.5216

Issue

Section

Special Conference Issue: AERA Online Teaching and Learning SIG