Learning Analytics
Permanent URI for this collectionhttps://hdl.handle.net/10125/107437
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Item type: Item , Discovering Unusual Study Patterns Using Anomaly Detection and XAI(2024-01-03) Tiukhova, Elena; Vemuri, Pavani; Óskarsdóttir, Maria; Poelmans, Stephan; Baesens, Bart; Snoeck, MoniqueLearning Analytics (LA) has been leveraged as a tool to analyze and improve educational processes by informing its stakeholders. LA for student profiling focuses on discovering learning patterns and trends based on diverse features extracted from trace data. Prior studies have used classical clustering methods to group students and understand the study patterns of each cluster. However, variations within the clusters are still large making it difficult to draw concrete insights into the relation between study behaviors and learning outcomes. In this work, we leverage anomaly detection and eXplainable AI techniques to distinguish between normal and abnormal study patterns and to possibly discover unexpected patterns that are not apparent from clustering alone. We perform external validation to check the generalizability and compare the insights on study patterns from our method to be at par with insights gained from previous studies.Item type: Item , Something for Every Kind of Learner: Students' Perceptions of an Educational Recommender Study Tool(2024-01-03) Mcnett, Alicia; Noteboom, CherieThe field of education has the potential to better facilitate student learning by employing educational recommender systems that adapt the learning process to the needs of individual learners. There is a lack of research that ties educational theory to the design and implementation of these systems. In this research, the design science methodology is employed to advocate for an educational recommender framework with a theoretical base in self-regulated learning. This paper focuses on the qualitative evaluation of this approach to gain insights on students’ perceptions of the resulting recommender when deployed to assist student studying for an upcoming exam. Student perceptions are analyzed to obtain design themes that serve to aid future researchers and practitioners in the design of these systems.Item type: Item , Introduction to the Minitrack on Learning Analytics(2024-01-03) Willermark, Sara; Deeva, Galina; Óskarsdóttir, Maria; Islind, Anna Sigridur