Predicting the use of learning objects: supporting high school education
TL;DR
This research analyzes the effectiveness of a predictive model based on the Random Forest algorithm to suggest personalized learning objects for high school students, through the analysis of academic and behavioral variables such as frequency and time of use of digital resources, motivation, and the perceived impact on comprehension.
Abstract
Education today faces major challenges compared to the situation before 2020, due to changes in the ways of teaching and learning caused by the difficulties of attending classes during the COVID-19 pandemic. Among the main challenges are the lack of didactic resources, limited technological infrastructure, and the geographical location of many schools, particularly those in rural areas. This research analyzes the effectiveness of a predictive model based on the Random Forest algorithm to suggest personalized learning objects for high school students, through the analysis of academic and behavioral variables such as frequency and time of use of digital resources, motivation, and the perceived impact on comprehension. The findings confirm that the predictive methodology not only supports educational personalization but also generated recommendations tailored to each student’s profile, optimizing the use of digital resources and significantly improving comprehension and academic performance. Validation, carried out through a diagnostic pre-test and a final post-test, demonstrated a significant average improvement of 45.24% in the academic performance of the experimental group that utilized the personalized learning objects suggested by the model. This study proposes the integration of machine learning models as an adaptive strategy in educational environments, highlighting the capacity of data analytics to promote equity, reduce learning disparities, and strengthen student-centered pedagogy.