Explaining and Predicting Students' and Teachers' Intentions to reuse Digital Educational Resources

Abstract

Digital educational resources (DER) provide valuable supplement to classic textbooks by offering interactivity, multimodality, and flexibility of use, which support students' engagement. These characteristics along with several pedagogical requirements that scaffold students' learning formed the basis for the development of DER within the e-School project, which was carried out in Croatia from 2015 till 2023. The goal was to develop high-quality DER that enables students' meaningful learning and encourages educators to integrate them into their teaching. This study aims to investigate students' and teachers' perceptions of e-School DER and their intention to reuse them by applying data mining techniques, which presents a novelty approach in this domain. A total of 11 827 students and 1 653 teachers answered the survey about the quality of various DER implemented in the web repository. By applying a decision tree machine learning algorithm, a predictive model of students' and teachers' intention to reuse DER is developed. Sensitivity analysis of the predictive model extracted relevant variables for prediction. The findings reveal that students and teachers have different perspectives on the quality of digital educational resources which facilitate different criteria when it comes to intention to reuse DER in the future.