Research Article Open Access

Framework for the Adaptive Learning of Higher Education Students in Virtual Classes in Peru Using CRISP-DM and Machine Learning

Maryori Bautista1, Sebastian Alfaro1 and Lenis Wong1
  • 1 Department of Information Systems Engineering Program, Peruvian University of Applied Sciences, Lima, Peru


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 of Computer Science
Volume 20 No. 5, 2024, 522-534


Submitted On: 9 November 2023 Published On: 26 February 2024

How to Cite: Bautista, M., Alfaro, S. & Wong, L. (2024). Framework for the Adaptive Learning of Higher Education Students in Virtual Classes in Peru Using CRISP-DM and Machine Learning. Journal of Computer Science, 20(5), 522-534.

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  • Adaptive Learning
  • Machine Learning
  • Virtual Classes