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dc.contributor.authorMu, Xingen_US
dc.contributor.authorXu, Keen_US
dc.contributor.authorChen, Qingen_US
dc.contributor.authorDu, Fanen_US
dc.contributor.authorWang, Yunen_US
dc.contributor.authorQu, Huaminen_US
dc.contributor.editorJohansson, Jimmy and Sadlo, Filip and Marai, G. Elisabetaen_US
dc.date.accessioned2019-06-02T18:14:44Z
dc.date.available2019-06-02T18:14:44Z
dc.date.issued2019
dc.identifier.isbn978-3-03868-090-1
dc.identifier.urihttps://doi.org/10.2312/evs.20191176
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/evs20191176
dc.description.abstractThe research on Massive Open Online Course (MOOC) has mushroomed worldwide due to the technical revolution and its unprecedented enrollments. Existing work mainly focuses on performance prediction, content recommendation, and learning behavior summarization. However, finding anomalous learning activities in MOOC data has posed special challenges and requires providing a clear definition of anomalous behavior, analyzing the multifaceted learning sequence data, and interpreting anomalies at different scales. In this paper, we present a novel visual analytics system, MOOCad, for exploring anomalous learning patterns and their clustering in MOOC data. The system integrates an anomaly detection algorithm to cluster learning sequences of MOOC learners into staged-based groups. Moreover, it allows interactive anomaly detection between and within groups on the basis of semantic and interpretable group-wise data summaries. We demonstrate the effectiveness of MOOCad via an in-depth interview with a MOOC lecturer with real-world course data.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectHuman
dc.subjectcentered computing
dc.subjectVisual analytics
dc.subjectApplied computing
dc.subjectE
dc.subjectlearning
dc.titleMOOCad: Visual Analysis of Anomalous Learning Activities in Massive Open Online Coursesen_US
dc.description.seriesinformationEuroVis 2019 - Short Papers
dc.description.sectionheadersWeb Interfaces and Learning
dc.identifier.doi10.2312/evs.20191176
dc.identifier.pages91-95


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