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Visualizing uncertainty in the prediction of academic risk

Abstract:

This work proposes a generic visual representation to help relevant decision-makers to effectively address the inherent uncertainty present in the prediction of academic risk based on historical data. The three main sources of uncertainty in this type of prediction are visualized: the model predictive power, the data consistency and the case completeness of the historic dataset. To demonstrate the proposed visualization technique, it is instantiated in a real-world scenario where the risk to fail at least one course in an academic semester is predicted and presented in a student-counseling system. This work also proposes how this visualization technique can be evaluated and applied to other Visual Learning Analytics tools.