Perceived Internet Usage Behaviours as Predictor to Outlier Detection in Students’ Communities in Academic Environments

The ability to predict student performance in a course or program creates opportunities to improve educational outcomes. With effective performance prediction approaches, instructors can allocate resources and instruction more accurately. Research in this area seeks to identify features that can be used to make predictions, to identify algorithms that can improve predictions, and to quantify aspects of student performance. Moreover, research in predicting student performance seeks to determine interrelated features and to identify the underlying reasons why certain features work better than others. This working group report presents a systematic literature review of work in the area of predicting student performance. Our analysis shows a clearly increasing amount of research in this area, as well as an increasing variety of techniques used. At the same time, the review uncovered a number of issues with research quality that drives a need for the community to provide more detailed reporting of methods and results and to increase efforts to validate and replicate work.

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Perceived Internet Usage Behaviours as Predictor to Outlier Detection in Students’ Communities in Academic Environments

Cited by 5 publications

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Student performance prediction in higher education: A comprehensive review

Student performance prediction in higher education: A comprehensive review

Clustering of web users' access patterns using a modified competitive agglomerative algorithm

Predicting academic performance: a systematic literature review

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