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

Journal: Currents in pharmacy teaching & learning
Published Date: Jul 19, 2025

Abstract

INTRODUCTION: This study examines pharmacy students' qualitative faculty and course evaluation (FCE) feedback through an integrated machine learning and human coding approach to uncover insights on faculty teaching, course quality, and areas for improvements, informing instructional enhancement.

Authors

  • Mohammed A Islam
    American University of Health Sciences, School of Pharmacy, Signal Hill, CA, USA. Electronic address: mislam@auhs.edu.
  • Suhui Yang
    Ping An Technology (Shenzhen) Co. Ltd, Shenzhen, China.
  • Alamdar Hussain
    Department of Pharmaceutical Sciences, University of Oklahoma Health Science Center, 1110 N. Stonewall Avenue, Oklahoma City, OK 73117, USA.
  • Tanvirul Hye
    Roseman University, Department of Basic Sciences, College of Medicine, Las Vegas, NV, USA. Electronic address: thye@roseman.edu.

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