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Exploring Learner Engagement in E-Learning Environments: A Predictive Analytics Perspective
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DOI：10.1080/10447318.2025.2535530.png)
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Abstract
En 中文
During the last decade, the embracement of learner engagement in developing educational technologies has contributed to the amalgamation of favorable pedagogical practices and advanced learning tools. New opportunities for tailoring data-driven learning designs created optimal conditions for crafting personalized, interactive e-learning environments that foster successful learning outcomes. Although a plethora of metrics exist to capture engagement, there is a need for comprehensive research that incorporates both the learner’s subjective perceptions of their engagement and the objective indicators of their actual engagement. The goal of this research is twofold: first, we aim to investigate the relationships between the interaction data on student behavior in an e-learning environment and their self-reported engagement data, and second, to design a model for predicting students’ level of engagement based on the study findings. Statistical analysis was conducted using data from (n= 45) undergraduate students at the University of South-Eastern Norway who completed a one-semester programming course, to explore relationships between their engagement and behavior in the programming tutoring system. Artificial neural networks were then used to develop a prediction model for classifying students’ engagement levels, leveraging the algorithms’ adaptability to diverse input data structures and classification efficiency. The findings highlight the importance of e-learning features like coding exercises, topic-based assessments, and explanatory hints in fostering student engagement. They also demonstrate the feasibility of predicting engagement using learner activity, interaction time, and learning outcomes. The study provides insights that inform the development of future educational designs for personalized engagement detection and improved learning outcomes.
Keywords:
e-Learning environment
learning engagement
behavior data
questionnaire
ANN
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