Relationship between Student Engagement and Performance in e-Learning Environment Using Association Rules
The field of e-learning has emerged as a topic of interest in academia due to the increased ease of accessing the Internet using using smart-phones and wireless devices. One of the challenges facing e-learning platforms is how to keep students motivated and engaged. Moreover, it is also crucial to identify the students that might need help in order to make sure their academic performance doesn't suffer. To that end, this paper tries to investigate the relationship between student engagement and their academic performance. Apriori association rules algorithm is used to derive a set of rules that relate student engagement to academic performance. Experimental results' analysis done using confidence and lift metrics show that a positive correlation exists between students' engagement level and their academic performance in a blended e-learning environment. In particular, it is shown that higher engagement often leads to better academic performance. This cements the previous work that linked engagement and academic performance in traditional classrooms.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
To Tab or Not to Tab: Measuring Critical Engagement in AI Code Completion Tools Using Behavioral Signals and Attention Checks
AI code completion tools, such as Github Copilot, provide students with code suggestions to help them write programs. However, recent qualitative studies suggest that students fail to critically evaluate these suggestion…
Code CompletionInvestigating the Impact of Personalized AI Tutors on Language Learning Performance
Driven by the global shift towards online learning prompted by the COVID 19 pandemic, Artificial Intelligence has emerged as a pivotal player in the field of education. Intelligent Tutoring Systems offer a new method of …
Supervised Contrastive Learning for Ordinal Engagement Measurement
Student engagement plays a crucial role in the successful delivery of educational programs. Automated engagement measurement helps instructors monitor student participation, identify disengagement, and adapt their teachi…
Contrastive LearningData AugmentationOrdinal ClassificationPrediction and Localization of Student Engagement in the Wild
In this paper, we introduce a new dataset for student engagement detection and localization. Digital revolution has transformed the traditional teaching procedure and a result analysis of the student engagement in an e-l…
Multiple Instance LearningWeakly-supervised LearningDS-HGCN: A Dual-Stream Hypergraph Convolutional Network for Predicting Student Engagement via Social Contagion
Student engagement is a critical factor influencing academic success and learning outcomes. Accurately predicting student engagement is essential for optimizing teaching strategies and providing personalized intervention…