Author:
Peng Xian,Han Chengyang,Ouyang Fan,Liu Zhi
Abstract
AbstractDue to an overwhelming amount of student-generated forum posts in small private online courses (SPOCs), students and instructors find it time-consuming and challenging to effectively navigate and track valuable information, such as the evolution of topics, emotional and behavioral changes in relation to topics. For solving this problem, this study analyzed plenty of discussion posts using an improved dynamic topic model, Time Information-Emotion Behavior Model (TI-EBTM). Time, emotion, and behavior characteristics were incorporated into the topic modeling process, which allowed for an overview of automatic tracking and understanding of temporal topic changes in SPOC discussion forums. The experiment on data from 30 SPOC courses showed that TI-EBTM outperformed other dynamic topic models and was effective in extracting prominent topics over time. Furthermore, we conducted an in-depth temporal topic analysis to investigate the utility of TI-EBTM in a case study. The results of the case study demonstrated that our methodology and analysis shed light on students’ temporal focuses (i.e., the changes of topic intensity and topic content) and reflected the evolution of topics’ emotional and behavioral tendencies. For example, students tended to express more negative emotions toward the topic about the method of data query by initiating the conversation at the end of the semester. The analytical results can provide instructors with valuable insights into the development of course forums and enable them to fine-tune course forums to suit students’ requirements, which will subsequently be helpful in enhancing discussion interaction and students’ learning experience.
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications,Education
Reference49 articles.
1. Almatrafi, O., & Johri, A. (2018). Systematic review of discussion forums in massive open online courses (MOOCs). IEEE Transactions on Learning Technologies, 12(3), 413–428.
2. Andrei, V., & Arandjelović, O. (2016). Complex temporal topic evolution modelling using the Kullback-Leibler divergence and the Bhattacharyya distance. EURASIP Journal on Bioinformatics and Systems Biology, 2016(1), 16–32.
3. Blei, D. M., & Lafferty, J. D. (2006, June). Dynamic topic models. In Proceedings of the 23rd international conference on Machine learning (pp. 113–120).
4. Blei, D. M., & Lafferty, J. D. (2007). A correlated topic model of science. The Annals of Applied Statistics, 1(1), 17–35.
5. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3(1), 993–1022.
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