Forecasting student’s preference in E-learning systems

Abstract

The need for qualified people is growing exponentially, requiring limited resources allocated to education/training to be used most efficiently. However some problems can occur: (1) relying on learning theories, it is crucial to improve the learning process and mitigate the issues that may arise from technologically enhanced learning environments; (2) each student presents a particular way of assimilating knowledge, i.e. his/her learning procedure. It’s essential that these systems adapt to the learning preferences of the students. In the present study, we propose an intelligent learning system able to monitor the patterns of students’ behaviour during e-assessments, to support the teaching procedure within school environments. Results show that there are still mechanisms that can be explored to understand better the complex relationship between human behaviour, attention, and assessment which could be used for the implementation of better learning strategies. These results may be crucial for improving learning systems in an e-learning environment and for predicting students’ behaviour in an exam, based on their interaction with technological devices.