Recommendation System for E-learning Student Orientation Based on Machine Learning Algorithms and Qcm
Year: 2025
Type: article
Abstract: This article is part of a student guidance project. It involves a recommendation and classification system (E_orientation) based on a real MCQ test. This test is administered to the student and provides random answers that are then used by our recommendation system. We use binary modelling of the answers, which produces a vector of data for each student at the end of the test. For modelling and classification, we use several machine-learning algorithms to optimize the accuracy of the recommendations. The results of the experiment show that Random Forest is the best model (85.93% accuracy), ahead of SVM (80.43%). KNN achieves 76.10%, and the Decision Tree, Logistic Regression and Naive Bayes algorithms have the lowest performance (Accuracy ≤ 67.03%). We can therefore improve the students’ method of orientation by basing it on simple technical questions. This will also improve their contribution to the labor market.
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W7117359772
FWCI:
Cites:
Cited by:
Related to: