@article {10.3844/jcssp.2024.522.534,
article_type = {journal},
title = {Framework for the Adaptive Learning of Higher Education Students in Virtual Classes in Peru Using CRISP-DM and Machine Learning},
author = {Bautista, Maryori and Alfaro, Sebastian and Wong, Lenis},
volume = {20},
number = {5},
year = {2024},
month = {Feb},
pages = {522-534},
doi = {10.3844/jcssp.2024.522.534},
url = {https://thescipub.com/abstract/jcssp.2024.522.534},
abstract = {During the COVID-19 pandemic, virtual education played a significant role around the world. In post-pandemic Peru, higher education institutions did not entirely dismiss the online education modality. However, this virtual education system maintains a traditional teaching-learning model, where all students receive the same content material and are expected to learn in the same way; as a result, it has not been effective in meeting the individual needs of students, causing poor performance in many cases. For this reason, a framework is proposed for the adaptive learning of higher education students in virtual classes using the Cross-Industry Standard Process for Data Mining (CRISP-DM) and Machine Learning (ML) methodology in order to recommend individualized learning materials. This framework is made up of four stages: (i) Analysis of student aspects, (ii) Analysis of Learning Methodology (LM), (iii) ML development and (iv) Integration of LM and ML models. (i) evaluates the student-related factors to be considered in adapting their learning content material. (ii) Evaluate which LM is more effective in a virtual environment. In (iii), Four ML algorithms based on the CRISP-DM methodology are implemented. In (iv), The best ML model is integrated with the LM in a virtual class. Two experiments were carried out to compare the traditional teaching methodology (experiment I) and the proposed framework (experiment 2) with a sample of 68 students. The results showed that the framework was more effective in promoting progress and academic performance, obtaining an Improvement Percentage (IP) of 39.72%. This percentage was calculated by subtracting the grade average of the tests taken at the beginning and end of each experiment.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}