Does Time Matter in Analyzing Educational Data? - A New Dataset for Streaming Learning Analytics

This research introduces a novel dataset developed for streaming learning analytics, derived from the Open University Learning Analytics Dataset (OULAD). The dataset incorporates essential temporal information that captures the timing of student interactions with the Virtual Learning Environment (VLE). By integrating these time-based interactions, the dataset enhances the capabilities of stream algorithms, which are particularly well-suited for real-time monitoring and analysis of student learning behaviors. Experiments utilizing the Online Bagging algorithm across three temporal units-months, trimesters, and semesters-demonstrated that the dataset contains pertinent information for predicting student outcomes. Despite the variations associated with different temporal units, the classifier effectively identified patterns within the data, especially for the majority class (Pass), achieving high F1 scores. These results indicate that the temporal structure of the data supports accurate predictions; however, challenges remain in accurately identifying the minority class (Fail). This dataset paves the way for more dynamic and responsive educational interventions by enabling timely predictions of student outcomes. Such capabilities facilitate continuous learning support within VLEs, allowing educators to respond promptly to student needs and enhance overall learning experiences.