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Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes
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E-learning recommender system dataset.

Mounir Hafsa1,2, Pamela Wattebled1, Julie Jacques2

  • 1Mandarine Academy, Lille, France.

Data in Brief
|February 23, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces the MARS dataset, a novel collection of implicit and explicit user ratings from an e-learning platform. It enables research into recommender systems for online learning, analyzing user behavior pre- and post-COVID-19.

Keywords:
Collaborative filteringE-LearningExplicit ratingsImplicit ratingsMOOCRecommender systems

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Area of Science:

  • Educational Technology
  • Recommender Systems
  • Data Science

Background:

  • Mandarine Academy, an Ed-Tech company, offers corporate training via Massive Open Online Courses (MOOCs).
  • Existing e-learning platforms collect explicit and implicit user ratings, but explicit feedback is often underutilized.
  • Implicit user interactions (e.g., watch time) provide valuable data for understanding user preferences.

Purpose of the Study:

  • To introduce the Mandarine Academy Recommender System (MARS) dataset, derived from implicit user interactions.
  • To address the underutilization of explicit ratings in e-learning recommendation systems.
  • To provide a unique, publicly available dataset for research on online learning behavior and recommender systems.

Main Methods:

  • Collected implicit user feedback (watch time, page views) from Mooc.office365-training.com.
  • Transformed a portion of implicit data to mirror explicit rating formats for comparability.
  • Created two dataset variations comprising 89,000 explicit and 276,000 implicit ratings from 2016-2021.

Main Results:

  • The MARS dataset includes both explicit and implicit ratings, offering a richer view of user engagement.
  • The dataset captures user behavior across a significant period, including pre- and post-COVID-19.
  • Privacy-preserving measures were implemented, ensuring the ethical use of collected data.

Conclusions:

  • The MARS dataset is the first publicly available, real-world dataset for e-learning recommendations with mixed explicit and implicit ratings in French and English.
  • This resource facilitates research into recommender systems tailored for online learning environments.
  • Enables comparative analysis of user behavior and recommendation effectiveness in the evolving landscape of digital education.