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Improving course evaluation processes in higher education institutions: a modular system approach.

İlker Kocaoğlu1, Erinç Karataş2

  • 1Management Information Systems, Baskent University, Ankara, Turkey.

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Summary

This study introduces a modular system to improve course and instructor evaluations (CIE) by detecting inconsistencies between numerical and textual feedback. The system enhances educational quality assessment through reliable data insights.

Keywords:
DSRMHigher educationMachine learningSentiment analysis

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Area of Science:

  • Educational Technology
  • Artificial Intelligence in Education
  • Data Science in Higher Education

Background:

  • Traditional course and instructor evaluations (CIE) face challenges with inconsistent data between structured responses and open-ended feedback.
  • Existing methods often fail to reconcile numerical scores with textual comments, leading to unreliable insights and increased administrative burden.
  • Improving the reliability of CIE data is critical for effective decision-making in higher education.

Purpose of the Study:

  • To design and evaluate a novel modular system for enhancing the accuracy and reliability of course and instructor evaluations.
  • To address inconsistencies within CIE data by integrating sentiment analysis and inconsistency detection.
  • To provide higher education institutions with a scalable and adaptable solution for data-driven decision-making.

Main Methods:

  • Utilized the Design Science Research Methodology (DSRM) to develop a five-module system architecture.
  • Employed machine learning algorithms, including GPT-4 Turbo Preview, to analyze 13,651 anonymized Turkish CIE records.
  • Compared sentiment analysis results from open-ended feedback with structured responses to identify data inconsistencies.

Main Results:

  • The GPT-4 Turbo Preview model demonstrated superior performance in sentiment analysis and inconsistency detection.
  • A prototype system identified a 37% inconsistency rate in a subset of CIE data.
  • Excluding inconsistent data generated reliable reports with actionable insights into course and instructor performance.

Conclusions:

  • The proposed modular system effectively enhances the accuracy and reliability of course and instructor evaluations.
  • The system offers a scalable and adaptable solution for higher education institutions seeking to improve educational quality assessment.
  • Integrating advanced machine learning techniques represents a significant advancement in leveraging technology for educational improvement.