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Association of Technology Enhanced Learning Resource Usage With Assessment Results.

Karen M Scott1,2, Meena Rattan1, Deborah O'Mara2

  • 1Specialty of Child and Adolescent Health, The University of Sydney, Sydney, Australia.

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Technology-enhanced learning (TEL) patterns in paediatric medicine revealed three student groups: high-achieving "Conscientious," balanced "Savvy," and struggling "Borderline." This highlights TEL

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Area of Science:

  • Medical Education
  • Learning Analytics
  • Pediatric Medicine

Background:

  • Technology-enhanced learning (TEL) offers benefits like knowledge consolidation and active learning.
  • Limited data exists on how broad TEL use impacts clinical rotation assessments.
  • Learning analytics can investigate TEL usage patterns and their association with student outcomes.

Purpose of the Study:

  • To investigate technology-enhanced learning (TEL) usage patterns in a pediatric medicine term.
  • To examine the association between TEL usage patterns, student characteristics, and assessment results.
  • To identify distinct student profiles based on TEL engagement and academic performance.

Main Methods:

  • Extracted TEL usage data from virtual learning environment logs for 266 students.
  • Utilized principal components analysis to reduce 38 measures to 12 independent TEL summary measures.
  • Applied K-means cluster analysis to identify TEL usage patterns and analyzed differences using chi-square and ANOVA.

Main Results:

  • Identified three distinct TEL usage patterns: 'Conscientious' (high usage, high results), 'Savvy' (selective usage, good results), and 'Borderline' (low usage, low results).
  • The 'Conscientious' cluster had more females and international students; the 'Borderline' cluster had more males and domestic students.
  • Demographic differences between clusters were not statistically significant (p > 0.05).

Conclusions:

  • Three technology-enhanced learning (TEL) usage patterns correlate with assessment outcomes in pediatric rotations.
  • Reviewing TEL usage can facilitate early identification of students at risk of academic difficulty.
  • Integrating learning analytics into educational programs is recommended for feedback and resource evaluation.