Visualizing uncertainty in the prediction of academic risk

Research output: Contribution to journalConference article

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.

Original languageEnglish (US)
Pages (from-to)4-10
Number of pages7
JournalCEUR Workshop Proceedings
Volume1518
StatePublished - 2015
Event1st International Workshop on Visual Aspects of Learning Analytics, VISLA 2015 - Poughkeepsie, United States
Duration: Mar 16 2015Mar 20 2015

Keywords

  • Academic Risk
  • Uncertainty Visualization
  • Visual Learning Analytics

ASJC Scopus subject areas

  • Computer Science(all)

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