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Related Experiment Video

Updated: May 13, 2026

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes
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Open data, private learners: a de-identified student activity and performance dataset for learning analytics.

Elena Tiukhova1, Dimitri Van Landuyt2, Bart Baesens2,3

  • 1Research Centre for Information Systems Engineering (LIRIS), KU Leuven, Naamsestraat 69, 3000, Leuven, Belgium. elena.tiukhova@kuleuven.be.

Scientific Data
|February 26, 2026
PubMed
Summary
This summary is machine-generated.

Learning Analytics (LA) uses digital traces to improve education. A new, de-identified clickstream dataset from KU Leuven is now available to support ethical LA development and evaluation.

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Area of Science:

  • Educational Technology
  • Data Science
  • Learning Analytics

Background:

  • Digital learning environments generate extensive learner data.
  • Learning Analytics (LA) leverages this data for educational process optimization.
  • Ethical considerations and data privacy are paramount in LA.

Purpose of the Study:

  • To present a detailed, de-identified clickstream dataset for LA research.
  • To promote open and transparent development and evaluation of LA solutions.
  • To address ethical and privacy concerns in educational data collection.

Main Methods:

  • Collected clickstream data from two first-year bachelor courses at KU Leuven.
  • Conducted a rigorous de-identification process for the dataset.
  • Performed privacy and utility validation of the dataset.

Main Results:

  • A comprehensive clickstream dataset spanning three academic years is now publicly available.
  • Transparent documentation of the de-identification procedure is provided.
  • Validation confirmed the dataset's utility for LA research while respecting privacy.

Conclusions:

  • The released dataset facilitates ethical and transparent Learning Analytics research.
  • Publicly available, validated datasets are crucial for advancing the LA field.
  • This resource supports collaboration and the development of robust LA frameworks.