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Updated: Dec 21, 2025

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques
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Click-level Learning Analytics in an Online Medical Education Learning Platform.

Matthew M Cirigliano1, Charles D Guthrie2, Martin V Pusic3

  • 1Steinhardt School of Education, New York University, New York, New York, USA.

Teaching and Learning in Medicine
|May 14, 2020
PubMed
Summary

Digital learning analytics reveal how student interactions, like clicking links or spending time on pages, correlate with success on multiple-choice questions (MCQs). This data helps improve online course design for better engagement.

Keywords:
Online learninginstructional designlearning analyticsmedical educationradiology

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Area of Science:

  • Educational Technology
  • Learning Analytics
  • Instructional Design

Background:

  • Digital learning environments generate valuable, detailed process data from many students.
  • Data collection can be intentionally designed for iterative testing of instructional strategies.
  • Analyzing student interaction data can identify design features linked to learner engagement.

Purpose of the Study:

  • To investigate if click-level behavior in an online module correlates with student performance on a knowledge-based assessment.
  • To determine if learning analytics can guide the iterative improvement of online instructional design.

Main Methods:

  • Utilized the Aquifer online learning platform for health professions education.
  • Identified learning analytics measures: hyperlink clicks, magnify button clicks, expert advice link clicks, and time spent per page.
  • Applied regression analysis to correlate click-level data with student accuracy on Case MCQs.

Main Results:

  • Positive correlations found between clicking hyperlinks, magnifying images, expert links, and spending over 100 seconds per page with MCQ success.
  • Spending less than 20 seconds per page was inversely correlated with MCQ success.
  • Some hypothesized associations between click-level data and MCQ success were not found.

Conclusions:

  • The abundance of process data in online learning offers insights for iterative instructional design.
  • Learning analytics provide feedback on the effectiveness of specific interaction elements in online modules.
  • Data-driven insights can enhance learner engagement and educational outcomes.