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Recognizing Reflection: Computer-Assisted Analysis of First Year Medical Students' Reflective Writing.

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PubMed
Summary
This summary is machine-generated.

Combining faculty assessment with Academic Writing Analytics (AWA) offers a more comprehensive evaluation of medical student reflective writing. This dual approach provides richer insights into student reflection than either method alone.

Keywords:
Natural language processingReflective writingUndergraduate medical education

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Area of Science:

  • Medical Education
  • Writing Assessment
  • Natural Language Processing

Background:

  • Reflective writing is crucial for developing medical professionals.
  • Assessing reflective writing presents challenges due to limitations of current methods.
  • Evaluating the combination of a faculty rubric and automated analytics was explored.

Purpose of the Study:

  • To determine if combining faculty assessment and Academic Writing Analytics (AWA) creates a more robust evaluation of medical student reflective writing.
  • To compare the insights gained from individual assessment methods versus a combined approach.

Main Methods:

  • First-year medical student reflective essays were collected.
  • Essays were assessed by faculty using a rubric (Integration, Depth, Writing).
  • Essays underwent Academic Writing Analytics (AWA) to identify reflective phrases (Context, Challenge, Change).

Main Results:

  • Faculty scores were uniformly high, indicating most students met the rubric standards.
  • AWA identified over 1400 instances of reflective behavior.
  • Significant variability was observed in students' use of different reflective phrase types.

Conclusions:

  • Faculty assessment and AWA individually provide sufficient data for evaluating reflective essays.
  • Combining faculty assessment and AWA yields a richer, more valuable understanding of student reflection.
  • The integrated approach enhances the depth of insight into the reflective process.