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We present a prediction model to detect delayed graduation cases based on student network analysis. In the U.S. only 60% of undergraduate students finish their bachelors’ degrees in 6 years [1]. We present many features based on student networks and activity records. To our knowledge, our feature design, which includes conventional academic performance features, student network features, and fix-point features, is one of the most comprehensive ones. We achieved the F-1 score of 0.85 and AUCROC of 0.86. 

Many analytic tools have been developed to discover knowledge from student data. However, the knowledge discovery process requires advanced analytical modelling skills, making it the province of data scientists. This impedes the ability of educational leaders, professors, and advisors to engage with the knowledge discovery process directly. As a result, it is challenging for analysis to take advantage of domain expertise, making its outcome often neither interesting nor useful. Usually the outcome produced from such analytic tools is static, preventing domain experts from exploring different hypotheses by changing data models or predictive models inside the tool. We have developed a framework for interactive and exploratory learning analytics which begins to address these challenges. We engaged in data exploration and hypotheses generation with our university domain experts by conducting two focus groups. We used the findings of these focus groups to validate our framework, arguing that it enables domain experts to explore the data, analysis and interpretation of student data to discover useful and interesting knowledge.