Enhancing Learner Engagement in Chulalongkorn University MOOC Computing Courses: Insights from Behavioral Trends and Analyses of Multiple Large Language Models

Karin Huangsuwan, Nagul Cooharojananone, Proadpran Punyabukkana, Atiwong Suchato, Yupin Puangngam, Pattamon Bunram

Abstract: This study investigates learner engagement behaviors in two distinct computing courses delivered online through Chulalongkorn University’s MOOC platform: Python Programming and Pre-Calculus. By analyzing learner 0engagement data, we identified key trends and challenges in both courses. In the Python Programming course, learners demonstrated selective engagement by skipping certain sections and focusing only on topics of interest. Interestingly, several sections had lower video-viewing rates compared to the number of assessments taken, suggesting that some learners possessed prior knowledge of the material and primarily enrolled to obtain certification. Specific challenges emerged in the ‘Variable’ and ‘String’ sections, which appeared to discourage learners. To address these issues, we employed three Large Language Models (ChatGPT, Gemini, and Claude) to analyze the course’s table of contents. Their evaluations offered consistent and actionable recommendations for reorganizing and grouping content.

External IDs:dblp:conf/icitl/HuangsuwanCPSPB25