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Task-specific information outperforms surveillance-style big data in predictive analytics.

Andreas Bjerre-Nielsen1,2, Valentin Kassarnig3, David Dreyer Lassen1,2,4

  • 1Department of Economics, University of Copenhagen, 1353 Copenhagen, Denmark.

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Summary
This summary is machine-generated.

Predicting student academic performance is possible using less invasive administrative data. Combining this with detailed behavioral data does not improve predictions, suggesting privacy-preserving methods are superior.

Keywords:
academic performancebig datapredictionprivacy

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Area of Science:

  • Educational Data Mining
  • Behavioral Science
  • Privacy Studies

Background:

  • Digital devices increasingly monitor human behavior and location.
  • Higher education institutions collect student data for performance assessment.
  • The shift to online education exacerbates privacy concerns regarding student data collection.

Purpose of the Study:

  • To investigate if task-specific, less privacy-invasive data can predict academic performance.
  • To compare predictive performance using extensive behavioral data versus administrative data.
  • To explore methods for identifying privacy-preserving features in educational data.

Main Methods:

  • Utilized a unique dataset from a large student population.
  • Included detailed behavioral and personality measures.
  • Incorporated high-quality, third-party reported administrative data.

Main Results:

  • Models using big behavioral data accurately predicted academic performance.
  • Models using only low-dimensional administrative data performed better.
  • Adding high-resolution behavioral data did not improve administrative data model performance.

Conclusions:

  • Less privacy-invasive administrative data can effectively predict academic performance.
  • Combining behavioral and administrative data offers opportunities for privacy-preserving feature identification.
  • Task-specific features derived from combined data can enhance privacy and prediction accuracy.