Framework for the Adaptive Learning of Higher Education Students in Virtual Classes in Peru Using CRISP-DM and Machine Learning
- 1 Department of Information Systems Engineering Program, Peruvian University of Applied Sciences, Lima, Peru
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.
DOI: https://doi.org/10.3844/jcssp.2024.522.534
Copyright: © 2024 Maryori Bautista, Sebastian Alfaro and Lenis Wong. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
- 1,008 Views
- 619 Downloads
- 0 Citations
Download
Keywords
- Adaptive Learning
- CRISP-DM
- Machine Learning
- Virtual Classes