Diagnostic classification modeling of EFL learners’ online learning performances during COVID-19 pandemic: individual vs. interactive learning

References

• Adedoyin, O. B., & Soykan, E. (2020). COVID-19 pandemic and online learning: The challenges and opportunities. Interactive Learning Environments, 2(1), 1–13. https://doi.org/10.1080/10494820.2020.1813180
   Google Scholar
• Anderson, A., Huttenlocher, D., Kleinberg, J., & Leskovec, J. (2014, April). Engaging with massive online courses. In Proceedings of the 23rd international conference on world wide Web (pp. 687–698). New York, NY, United States: Association for Computing Machinery.
   Google Scholar
• Bond, M., Marín, V. I., Dolch, C., Bedenlier, S., & Zawacki-Richter, O. (2018). Digital transformation in German higher education: Student and teacher perceptions and usage of digital media. International Journal of Educational Technology in Higher Education, 15(1), 48. https://doi.org/10.1186/s41239-018-0130-1
   Google Scholar
• Cameron, B. A., Morgan, K., Williams, K. C., & Kostelecky, K. L. (2009). Group projects: Student perceptions of the relationship between social tasks and a sense of community in online group work. American Journal of Distance Education, 23(1), 20–33. https://doi.org/10.1080/08923640802664466
   Google Scholar
• Castaño-Muñoz, J., Duart, J. M., & Sancho-Vinuesa, T. (2014). The internet in face-to-face higher education: Can interactive learning improve academic achievement? British Journal of Educational Technology, 45(1), 149–159. https://doi.org/10.1111/bjet.12007
   Web of Science ®Google Scholar
• Chatterjee, R., & Correia, A. P. (2020). Online students’ attitudes toward collaborative learning and sense of community. American Journal of Distance Education, 34(1), 53–68. https://doi.org/10.1080/08923647.2020.1703479
   Web of Science ®Google Scholar
• Chi, M. T. H. (2009). Active-constructive-interactive: A conceptual framework for differentiating learning activities. Topics in Cognitive Science, 1(1), 73–105. https://doi.org/10.1111/j.1756-8765.2008.01005.x
   PubMed Web of Science ®Google Scholar
• Chi, M. T. H., & Wylie, R. (2014). The ICAP framework: Linking cognitive engagement to active learning outcomes. Educational Psychologist, 49(4), 219–243. https://doi.org/10.1080/00461520.2014.965823
   Web of Science ®Google Scholar
• DeBoer, J., Ho, A. D., Stump, G. S., & Breslow, L. (2014). Changing “course” reconceptualizing educational variables for massive open online courses. Educational Researcher, 43(2), 74–84. https://doi.org/10.3102/0013189X14523038
   Web of Science ®Google Scholar
• de La Torre, J. (2011). The generalized DINA model framework. Psychometrika, 76(2), 179–199. https://doi.org/10.1007/s11336-011-9207-7
   Web of Science ®Google Scholar
• De la Torre, J., & Minchen, N. (2014). Cognitively diagnostic assessments and the cognitive diagnosis model framework. Psicología Educativa, 20(2), 89–97. https://doi.org/10.1016/j.pse.2014.11.001
   Google Scholar
• DiBello, L. V., Roussos, L. A., & Stout, W. F. (2007). Review of cognitively diagnostic assessment and a summary of psychometric models. In C. R. Rao, & S. Sinharay (Eds.), Handbook of statistics, psychometrics (Vol. 26, pp. 979–1030). Elsevier.
   Google Scholar
• DiBello, L. V., & Stout, W. (2007). Guest editor’s introduction and overview: IRT-based cognitive diagnostic models and related methods. Journal of Educational Measurement, 44(4), 285–291. https://doi.org/10.1111/j.1745-3984.2007.00039.x
   Web of Science ®Google Scholar
• Dincher, M., & Wagner, V. (2021). Teaching in times of COVID-19: Determinants of teachers’ educational technology use. Education Economics, 29(5), 461–470.
   Web of Science ®Google Scholar
• DiRamio, D., & Wolverton, M. (2006). Integrating learning communities and distance education: Possibility or pipedream? Innovative Higher Education, 31(2), 99–113. https://doi.org/10.1007/s10755-006-9011-y
   Google Scholar
• Effatpanah, F. (2019). Application of cognitive diagnostic models to the listening section of the international English language testing system (IELTS). International Journal of Language Testing, 9(1), 1–28.
   Google Scholar
• Ellis, A. (2010). Personality type and participation in networked learning environments. Educational Media International, 40(1–2), 101–114. https://doi.org/10.1080/0952398032000092152
   Google Scholar
• Fry, K. (2001). E-learning markets and providers: Some issues and prospects. Education+ Training, 43(4/5), 233–239. https://doi.org/10.1108/EUM0000000005484
   Google Scholar
• Greene, B. A. (2015). Measuring cognitive engagement with self-report scales: Reflections from over 20 years of research. Educational Psychologist, 50(1), 14–30. https://doi.org/10.1080/00461520.2014.989230
   Web of Science ®Google Scholar
• Han, F., & Ellis, R. A. (2019). Identifying consistent patterns of quality learning discussions in blended learning. The Internet and Higher Education, 40, 12–19. https://doi.org/10.1016/j.iheduc.2018.09.002
   Web of Science ®Google Scholar
• Harrington, R., & Loffredo, D. A. (2010). MBTI personality type and other factors that relate to preference for online versus face-to-face instruction. The Internet and Higher Education, 13(1–2), 89–95. https://doi.org/10.1016/j.iheduc.2009.11.006
   Web of Science ®Google Scholar
• Jang, E. E. (2005). A validity narrative: Effects of reading skills diagnosis on teaching and learning in the context of NG TOEFL [Unpublished doctoral dissertation]. University of Illinois.
   Google Scholar
• Ketabi, S., Alavi, S., & Ravand, H. (2021). Diagnostic test construction: Insights from cognitive diagnostic modeling. International Journal of Language Testing, 11(1), 22–35.
   Google Scholar
• Kopp, M., Gröblinger, O., & Adams, S. (2019, March 11–13). Five common assumptions that prevent digital transformation at higher education institutions. In INTED2019 Proceedings (pp. 1448–1457). https://doi.org/10.21125/inted.2019
   Google Scholar
• Laidra, K., Pullmann, H., & Allik, J. (2007). Personality and intelligence as predictors of academic achievement: A cross-sectional study from elementary to secondary school. Personality and Individual Differences, 42(3), 441–451. https://doi.org/10.1016/j.paid.2006.08.001
   Web of Science ®Google Scholar
• Lee, Y. W., & Sawaki, Y. (2009a). Application of three cognitive diagnosis models to ESL reading and listening assessments. Language Assessment Quarterly, 6(3), 239–263. https://doi.org/10.1080/15434300903079562
   Web of Science ®Google Scholar
• Lee, Y. W., & Sawaki, Y. (2009b). Cognitive diagnosis approaches to language assessment: An overview. Language Assessment Quarterly, 6(3), 172–189. https://doi.org/10.1080/15434300902985108
   Web of Science ®Google Scholar
• Leighton, J. P., & Gierl, M. J. (2007). Cognitive diagnostic assessment for education: Theory and practices. Cambridge University Press.
   Google Scholar
• Leszczyński, P., Charuta, A., Łaziuk, B., Gałązkowski, R., Wejnarski, A., Roszak, M., & Kołodziejczak, B. (2018). Multimedia and interactivity in distance learning of resuscitation guidelines: A randomised controlled trial. Interactive Learning Environments, 26(2), 151–162. https://doi.org/10.1080/10494820.2017.1337035
   Web of Science ®Google Scholar
• Li, H. (2011). A cognitive diagnostic analysis of the MELAB reading test. Spaan Fellow Working Papers in Second or Foreign Language Assessment, 9, 17–46.
   Google Scholar
• Li, H., Kim, M. K., & Xiong, Y. (2020). Individual learning vs. interactive learning: A cognitive diagnostic analysis of MOOC students’ learning behaviors. American Journal of Distance Education, 34(2), 121–136. https://doi.org/10.1080/08923647.2019.1697027
   Web of Science ®Google Scholar
• Martinez, M. (2001). Key design considerations for personalized learning on the web. Journal of Educational Technology & Society, 4(1), 26–40. https://www.jstor.org/stable/jeductechsoci.4.1.26
   Google Scholar
• Maydeu-Olivares, M. (2013). Goodness-of-fit assessment of item response theory models. Measurement: Interdisciplinary Research and Perspectives, 11(3), 71–137. https://doi.org/10.1080/15366367.2013.831680
   Google Scholar
• McDonald, R. P., & Mok, M. M. C. (1995). Goodness of fit in item response models. Multivariate Behavioral Research, 30(1), 23–40. https://doi.org/10.1207/s15327906mbr3001_2
   PubMed Web of Science ®Google Scholar
• Means, B., Toyama, Y., Murphy, R., Bakia, M., & Jones, K. (2009). Evaluation of evidence-based practices in online learning: A meta-analysis and review of online learning studies. U.S. Department of Education, Office of Planning, Evaluation, and Policy Development. http://www2.ed.gov/rschstat/eval/tech/evidence-based-practices/finalreport.pdf
   Google Scholar
• Osterlind, S. J. (2002). Constructing test items: Multiple-choice, constructed-response, performance, and other formats (2nd Ed.). Kluwer Academic.
   Google Scholar
• Psotka, J. (2022). Exemplary online education: For whom online learning can work better. Interactive Learning Environments, 30(2), 199–201. https://doi.org/10.1080/10494820.2022.2031065
   Web of Science ®Google Scholar
• Quadir, B., Yang, J. C., & Chen, N. S. (2022). The effects of interaction types on learning outcomes in a blog-based interactive learning environment. Interactive Learning Environments, 30(2), 293–306. https://doi.org/10.1080/10494820.2019.1652835
   Web of Science ®Google Scholar
• Ravand, H. (2016). Application of a cognitive diagnostic model to a high-stakes reading comprehension test. Journal of Psychoeducational Assessment, 34(8), 782–799. https://doi.org/10.1177/0734282915623053
   Web of Science ®Google Scholar
• Ravand, H., & Baghaei, P. (2020). Diagnostic classification models: Recent development, practical issues, and prospects. International Journal of Testing, 20(1), 24–56. https://doi.org/10.1080/15305058.2019.1588278
   Web of Science ®Google Scholar
• Ravand, H., Barati, H., & Widhiarso, W. (2013). Exploring diagnostic capacity of a high stakes reading comprehension test: A pedagogical demonstration. Iranian Journal of Language Testing, 3(1), 12–37.
   Google Scholar
• Ravand, H., & Robitzsch, A. (2015). Cognitive diagnostic modeling using R. Practical Assessment Research and Evaluation, 20(1), 1–12. https://doi.org/10.7275/5g6f-ak15
   Google Scholar
• Ravand, H., & Robitzsch, A. (2018). Cognitive diagnostic model of best choice: A study of reading comprehension. Educational Psychology, 38(10), 1255–1277. https://doi.org/10.1080/01443410.2018.1489524
   Web of Science ®Google Scholar
• Robitzsch, A., Kiefer, T., George, A. C., & Uenlue, A. (2017). CDM: Cognitive diagnosis modeling. (R Package Version 6.0-101) [ [Computer software]. https://cran.r-project.org/package=CDM
   Google Scholar
• Samir, A., & Tabatabaee-Yazdi, M. (2020). Translation quality assessment rubric: A Rasch model-based validation. International Journal of Language Testing, 10(2), 101–128.
   Google Scholar
• Sandkuhl, K., & Lehmann, H. (2017). Digital transformation in higher education – the role of enterprise architectures and portals. In Digital enterprise computing (DEC 2017).
   Google Scholar
• Shen, D., Nuankhieo, P., Huang, X., Amelung, C., & Laffey, J. (2008). Using social network analysis to understand the sense of community in an online learning environment. Journal of Educational Computing Research, 39(1), 17–36. https://doi.org/10.2190/ec.39.1.b
   Web of Science ®Google Scholar
• Strielkowski, W. (2020). COVID-19 pandemic and the digital revolution in academia and higher education. Preprints, 2020040290, https://doi.org/10.20944/preprints202004.0290.v1
   Google Scholar
• Sun, L., Fu, Z., & Zheng, Y. (2020). Shyness and loneliness in Chinese young adults: Roles of aggression and gender. Journal of Aggression, Maltreatment & Trauma. Advance online publication. https://doi.org/10.1080/10926771.2020.1725209
   Web of Science ®Google Scholar
• Tabatabaee-Yazdi, M. (2020). Hierarchical diagnostic classification modeling of reading comprehension. SAGE Open, 10(2), 2158244020931068. https://doi.org/10.1177/2158244020931068
   Web of Science ®Google Scholar
• Tabatabaee-Yazdi, M., Motallebzadeh, K., & Baghaei, P. (2021). A Mokken scale analysis of an English reading comprehension test. International Journal of Language Testing, 11(1), 132–143.
   Google Scholar
• Tallent-Runnels, M. K., Thomas, J. A., Lan, W. Y., Cooper, S., Ahern, T. C., Shaw, S. M., & Liu, X. (2006). Teaching courses online: A review of the research. Review of Educational Research, 76(1), 93–135. https://doi.org/10.3102/00346543076001093
   Web of Science ®Google Scholar
• Wang, C., Cheng, Z., Yue, X.-G., & McAleer, M. (2020). Risk management of COVID-19 by universities in China. Journal of Risk and Financial Management, 13(2), 36. https://doi.org/10.3390/jrfm13020036
   Web of Science ®Google Scholar
• WHO. (2020). Coronavirus disease (COVID-19) pandemic. World Health Organization. https://www.who.int/emergencies/diseases/novel-coronavirus-2019
   Google Scholar