Leveraging Techniques of Epistemic Network Analysis to Discover Behaviors of Student Learning Reflections in Online Learning Environments

Abstract

In the domain of learning analytics, reflective writing has introduced trends to enhance the learning and teaching experience. Epistemic Network Analysis (ENA), is a recent development in the techniques of learning analytics regarding handling huge amounts of text and visualizing learners’ interactions in the form of network graphs. In this context, 43 students participated in 10 tasks over a 16-week semester on a blended course. The current article aims to explore their reflective behaviors through this new learning methodology and establish via the ENA technique whether there is any relationship between such behaviors and course performance. The findings show the effectiveness of ENA in investigating students’ overall learning reflection patterns and revealing the frequencies of each reflection type for both high- and low-performing students. The group of high performers demonstrated a stronger connection with positive feelings regarding the learning experience, whereas the low performers exhibited a negative attitude toward the learning process. The obtained results provide insights into students' impressions of specific teaching or learning methods. Linking the reflection behavior to the level of student performance enables teachers to improve course design and provide appropriate interventions, which may be reflected in enhanced student performance.