Here is the word cloud for work I co-authored, titled, “Integrating Learning Analytics to Measure Message Quality in Large Online Conversations,” which was presented at the 53rd Hawaii International Conference on System Sciences on January 10, 2020. It received a best paper nomination. This work is a collaborative effort with Asst. Professor Evren Erylimaz.
The paper abstract is as follows:
Abstract: Research on computer-supported collaborative learning often employs content analysis as an approach to investigate message quality in asynchronous online discussions using systematic message-coding schemas. Although this approach helps researchers count the frequencies by which students engage in different socio-cognitive actions, it does not explain how students articulate their ideas in categorized messages. This study investigates the effects of a recommender system on the quality of students’ messages from voluminous discussions. We employ real-time learning analytics to produce a quasi-quality index score for each message. Moreover, we examine the relationship between this score and the phases of a popular message-coding schema. Empirical findings show that a custom CSCL environment extended by a recommender system supports students to explore different viewpoints and modify interpretations with higher quasi-quality index scores than students assigned to the control software. Theoretical and practical implications are also discussed.
Citation
E. Eryilmaz, B. Thoms, KH Lee, M. de Castro, “Integrating Learning Analytics to Measure Message Quality in Large Online Conversations,” Proceedings of Hawaiian International Conference on System Sciences (HICSS 53), January 7-10, 2020, Wailea, HI, USA.