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Machine learning-driven analysis of student evaluation comments: Advancing beyond manual coding through a combined

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Summary

Analyzing pharmacy student feedback using machine learning and human coding reveals key insights into faculty teaching and course quality. This data-driven approach enhances instructional design and student learning experiences in health professions education.

Keywords:
Faculty course evaluationsMachine learningPharmacy educationStudent commentsThematic analysis

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Area of Science:

  • Pharmacy Education
  • Health Professions Education
  • Educational Technology

Background:

  • Qualitative faculty and course evaluation (FCE) feedback provides valuable insights into student learning experiences.
  • Analyzing open-ended student comments is crucial for instructional enhancement and curriculum development.
  • Traditional feedback analysis methods may not fully capture the nuances within qualitative data.

Purpose of the Study:

  • To investigate pharmacy students' qualitative FCE feedback using an integrated machine learning and human coding approach.
  • To uncover actionable insights regarding faculty teaching, course quality, and areas for improvement.
  • To inform instructional enhancement strategies within health professions education.

Main Methods:

  • Analysis of 1267 FCEs from 2019-2023 using text mining software (WordStat).
  • Application of machine learning techniques: word clustering, co-occurrence mapping, phrase extraction, and topic modeling.
  • Supplemental manual thematic analysis using deductive and inductive coding, supported by descriptive statistics.

Main Results:

  • Machine learning identified key terms (professor, class, teaching) and phrases (excellent professor, knowledgeable professors).
  • Topic modeling revealed themes like understanding materials, great professors, and real-life experience.
  • Manual coding identified three major themes: faculty personal attributes (45.86%), teaching effectiveness (28.92%), and course quality (23.24%).

Conclusions:

  • Integrating machine learning with human coding effectively analyzes qualitative FCE data for deeper insights.
  • Student feedback analysis can inform data-driven decisions for curriculum design and teaching effectiveness.
  • This approach enhances the understanding of the student learning experience in health professions education.