Innovative techniques for e-learning log data processing: Trends and methods
Olga Ovtšarenko · Journal of Innovation & Knowledge · 2025
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.