Predicting Student Status Using Machine Learning by Analyzing Classroom Behaviors with X-API Data

Authors: Abdelamine Elouafi, Ilyas Tammouch, Souad Eddarouich
Raja Touahni (IJEECS ID 39510)
Our research delves into the emergence and growing significance of educational data mining, a field that aims to extract valuable insights from vast datasets gathered from diverse educational environments. Utilizing the Experience API (XAPI) and the Kalboard 360 online learning platform, our research presents a novel behaviorally based student performance model that evaluates the influence of student interactions on academic results. We create reliable models for precisely projecting academic success by utilizing machine learning techniques including logistic regression, k-nearest neighbors, SVM, decision trees, random forests, and XGBoost. The outcomes show a notable increase in categorization accuracy. This approach has the potential to improve educational processes by personalizing instruction, providing formative assessment support, and proactively identifying each student's unique needs to maximize their learning experience.
Indonesian Journal of Electrical Engineering and Computer Science
ijeecs.iaescor...
Supported by Master Program of Electrical and Computer Engineering, Universitas Ahmad Dahlan, mee.uad.ac.id #yogyakarta
Admission: mee.uad.ac.id/...
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