Innovative techniques for e-learning log data processing: Trends and methods

Abstract

This study explored potential solutions to identify innovative methods for modern education, where diverse student requirements and rapidly changing contents require advanced approaches to using modern technologies. The Moodle e-learning system collects and records data on student interactions with e-learning resources. However, its learning analytics capabilities are limited and provide insufficient information for optimising the learning process. In this study, available log data from an e-learning course were extracted, pre-processed and analysed using the weighted feature method. A data model based on streaming student activity logs was then tested to verify the compatibility of the selected parameters and validate the method. The effectiveness of the streaming learning-log-data model to dynamically track and optimise the learning process was confirmed. The study results will be useful for e-course developers in assessing their dynamic course structures. Future research will provide personalised real-time recommendations to help students and teachers navigate the vast array of available educational content, making learning more interesting and effective.