Modeling temporal cognitive topic to uncover learners’ concerns under different cognitive engagement patterns

References

• Almatrafi, O., Johri, A., & Rangwala, H. (2018). Needle in a haystack: Identifying learner posts that require urgent response in MOOC discussion forums. Computers & Education, 118, 1–9. https://doi.org/10.1016/j.compedu.2017.11.002
   Web of Science ®Google Scholar
• Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. The Journal of Machine Learning Research, 3(Jan), 993–1022.
   Google Scholar
• Castellanos-Reyes, D. (2021). The dynamics of a MOOC’s learner-learner interaction over time: A longitudinal network analysis. Computers in Human Behavior, 123, 106880. https://doi.org/10.1016/j.chb.2021.106880
   Web of Science ®Google Scholar
• Chi, M. T., & 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
• Cho, H. (2016). Under co-construction: An online community of practice for bilingual pre-service teachers. Computers & Education, 92–93, 76–89. https://doi.org/10.1016/j.compedu.2015.10.008
   Web of Science ®Google Scholar
• Cleary, T. J., & Zimmerman, B. J. (2012). A cyclical self-regulatory account of student engagement: Theoretical foundations and applications. In Christenson S. (Ed.), Handbook of research on student engagement (pp. 237–257). Springer.
   Google Scholar
• Cohen, A., Shimony, U., Nachmias, R., & Soffer, T. (2019). Active learners’ characterization in MOOC forums and their generated knowledge. British Journal of Educational Technology, 50(1), 177–198. https://doi.org/10.1111/bjet.12670
   Web of Science ®Google Scholar
• Dai, H. M., Teo, T., Rappa, N. A., & Huang, F. (2020). Explaining Chinese university students’ continuance learning intention in the MOOC setting: A modified expectation confirmation model perspective. Computers & Education, 150, 103850. https://doi.org/10.1016/j.compedu.2020.103850
   Web of Science ®Google Scholar
• Galikyan, I., Admiraal, W., & Kester, L. (2021). MOOC discussion forums: The interplay of the cognitive and the social. Computers & Education, 165, 104133. https://doi.org/10.1016/j.compedu.2021.104133
   Web of Science ®Google Scholar
• Gao, R., Hao, B., Li, H., Gao, Y., & Zhu, T. (2013). Developing simplified Chinese psychological linguistic analysis dictionary for microblog. In Imamura K. (Ed.), International conference on brain and health informatics (pp. 359–368). Springer.
   Google Scholar
• Geng, S., Niu, B., Feng, Y., & Huang, M. (2020). Understanding the focal points and sentiment of learners in MOOC reviews: A machine learning and SC-LIWC-based approach. British Journal of Educational Technology, 51(5), 1785–1803. https://doi.org/10.1111/bjet.12999
   Web of Science ®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
• Hone, K. S., & El Said, G. R. (2016). Exploring the factors affecting MOOC retention: A survey study. Computers & Education, 98, 157–168. https://doi.org/10.1016/j.compedu.2016.03.016
   Web of Science ®Google Scholar
• Jin, C. (2020). MOOC student dropout prediction model based on learning behavior features and parameter optimization. Interactive Learning Environments, 1–19. https://doi.org/10.1080/10494820.2020.1802300
   Web of Science ®Google Scholar
• Joksimovic, S., Gasevic, D., Kovanovic, V., Adesope, O., & Hatala, M. (2014). Psychological characteristics in cognitive presence of communities of inquiry: A linguistic analysis of online discussions. The Internet and Higher Education, 22, 1–10. https://doi.org/10.1016/j.iheduc.2014.03.001
   Web of Science ®Google Scholar
• Liu, Z., Yang, C., Rüdian, S., Liu, S., Zhao, L., & Wang, T. (2019). Temporal emotion-aspect modeling for discovering what students are concerned about in online course forums. Interactive Learning Environments, 27(5-6), 598–627. https://doi.org/10.1080/10494820.2019.1610449
   Web of Science ®Google Scholar
• Ma, J., Han, X., Yang, J., & Cheng, J. (2015). Examining the necessary condition for engagement in an online learning environment based on learning analytics approach: The role of the instructor. The Internet and Higher Education, 24, 26–34. https://doi.org/10.1016/j.iheduc.2014.09.005
   Web of Science ®Google Scholar
• Moore, R. L., Oliver, K. M., & Wang, C. (2019). Setting the pace: Examining cognitive processing in MOOC discussion forums with automatic text analysis. Interactive Learning Environments, 27(5-6), 655–669. https://doi.org/10.1080/10494820.2019.1610453
   Web of Science ®Google Scholar
• Moore, R. L., Yen, C. J., & Powers, F. E. (2021). Exploring the relationship between clout and cognitive processing in MOOC discussion forums. British Journal of Educational Technology, 52(1), 482–497. https://doi.org/10.1111/bjet.13033
   Web of Science ®Google Scholar
• Nie, Y., Luo, H., & Sun, D. (2021). Design and validation of a diagnostic MOOC evaluation method combining AHP and text mining algorithms. Interactive Learning Environments, 29(2), 315–328. https://doi.org/10.1080/10494820.2020.1802298
   Web of Science ®Google Scholar
• Peng, X., Han, C., Ouyang, F., & Liu, Z. (2020). Topic tracking model for analyzing student-generated posts in SPOC discussion forums. International Journal of Educational Technology in Higher Education, 17(1), 1–22. https://doi.org/10.1186/s41239-020-00211-4
   Web of Science ®Google Scholar
• Peng, X., & Xu, Q. (2020). Investigating learners’ behaviors and discourse content in MOOC course reviews. Computers & Education, 143, 103673. https://doi.org/10.1016/j.compedu.2019.103673
   Web of Science ®Google Scholar
• Peng, X., Xu, Q., & Gan, W. (2021). SBTM: A joint sentiment and behaviour topic model for online course discussion forums. Journal of Information Science, 47(4), 517–532. https://doi.org/10.1177/0165551520917120
   Web of Science ®Google Scholar
• Wang, X., Wen, M., & Rosé, C. P. (2016, April). Towards triggering higher-order thinking behaviors in MOOCs. In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge (pp. 398–407).
   Google Scholar
• Wei, X., Saab, N., & Admiraal, W. (2021). Assessment of cognitive, behavioral, and affective learning outcomes in massive open online courses: A systematic literature review. Computers & Education, 163, 104097. https://doi.org/10.1016/j.compedu.2020.104097
   Web of Science ®Google Scholar
• Wu, J. Y. (2021). Learning analytics on structured and unstructured heterogeneous data sources: Perspectives from procrastination, help-seeking, and machine-learning defined cognitive engagement. Computers & Education, 163, 104066. https://doi.org/10.1016/j.compedu.2020.104066
   Web of Science ®Google Scholar
• Xu, B., Chen, N. S., & Chen, G. (2020). Effects of teacher role on student engagement in WeChat-based online discussion learning. Computers & Education, 157, 103956. https://doi.org/10.1016/j.compedu.2020.103956
   Web of Science ®Google Scholar
• Zeng, X., Yang, C., Tu, C., Liu, Z., & Sun, M. (2018, April). Chinese LIWC lexicon expansion via hierarchical classification of word embeddings with sememe attention. In Thirty-Second AAAI Conference on Artificial Intelligence.
   Google Scholar
• Zhang, J., Skryabin, M., & Song, X. (2016). Understanding the dynamics of MOOC discussion forums with simulation investigation for empirical network analysis (SIENA). Distance Education, 37(3), 270–286. https://doi.org/10.1080/01587919.2016.1226230
   Web of Science ®Google Scholar