Efficient Student Behaviour Analysis in E-Learning Using Data Mining Approaches

Efficient Student Behaviour Analysis in E-Learning Using Data Mining Approaches

H. Riaz Ahamed, D. Kerana Hanirex
Copyright: © 2024 |Pages: 18
DOI: 10.4018/979-8-3693-1355-8.ch019
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Abstract

Recognising and assessing how pupils act is essential for customising educational opportunities and enhancing educational results in online learning. In particular, Support Vector Machine (SVM), Decision Tree (DT), and Naive Bayes (NB) are employed in this work to analyse the characteristics of pupil conduct in online educational settings. The main goal is to determine the best strategy for thoroughly comprehending how students communicate in online learning environments. Employing metrics like RMSE (Root Mean Square Error), RSE (Relative Absolute Error), and RRSE (Relative Root Square Error) to evaluate the outcome of DM (Data Mining) methods. The results show that SVM regularly beats DT and NB throughout all criteria, showing that it has a greater capacity to identify complex relationships in pupil activity records with RMSE of 0.02714, RAE of 0.00279 and RRSE of 0.02117, respectively. The tool used for execution is Jupyter Notebook, and the language used is Python.
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Methods Of Student Behavior Analysis Using Data Mining

Clickstream Analysis: Clickstream analysis involves tracking and analysing the sequence of actions performed by students while navigating through e-learning platforms. This method helps identify patterns in how students interact with content, how much time they spend on specific activities, and which resources they find most valuable (Gomathy & Venkatasbramanian, 2023).

Learning Management System (LMS) Data Analysis: LMS data contains a wealth of information related to student behaviour, including login frequency, time spent on various modules, assessment scores, and participation in discussion forums (Groenewald et al., 2023). Data mining techniques can uncover patterns and trends in LMS data, providing valuable insights into students' engagement levels and academic progress (Kem, 2023).

Social Network Analysis: Social network analysis explores the interactions and relationships between students within online learning communities (Kem, 2021a). By analysing communication patterns, collaboration, and participation in group activities, educators can gain insights into the social dynamics that impact student engagement and learning outcomes (Kalsoom et al., 2021).

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