A Study on Improving Curricula for Preservice Teacher Education Through Course Evaluation Data Analysis

doi:10.22716/sckt.2026.14.3.012

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2026 Vol.14, Issue 3 Preview Page Next Page

Research Article

A Study on Improving Curricula for Preservice Teacher Education Through Course Evaluation Data Analysis 강의평가 데이터 분석을 통한 예비교사 양성 교육과정 개선 방안 연구: Ji-Hoon Seo¹
서지훈¹; ¹Professor, Kangnam University, Artificial Intelligence Convergence Engineering

¹강남대학교 인공지능융합공학부 데이터사이언스 전공 교수

30 September 2026. pp. 165-177

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Abstract

본 연구는 최근 5개년간 수도권 소재 4년제 대학교 사범대학 및 교직과정 강좌의 정량 평점(2,166개)과 서술형 데이터(4,852건)를 분석하여 예비 교원 양성 교육과정의 중장기적 개선 및 환류 체계 구축 방안을 제안한다. 분석 결과, 예비교사들은 단방향적 이론 수업보다 실습·현장 연계형 수업과 교구 제작·수업 실연 등 창작형 과제에서 높은 만족도를 보였다. 반면 임용시험 및 개정 교육과정 연계성 강화, 과제 시기 적정화, 평가 기준의 투명성 확보에 대한 보완 요구가 확인되었다. 이를 바탕으로 수업·과제 설계, 평가 및 운영 정시성, 임용·현장 연계, 환류 인프라 구축의 4대 선순환 환류 체계를 제언한다. 본 연구는 단일 대학 데이터 분석이라는 한계가 있으나, 5개년 종단 빅데이터로 예비교사의 인식 추이를 실증하여 교원양성기관의 교육과정 고도화 및 정책 개선에 기여할 수 있는 실증적 기초 자료와 실무적 시사점을 제공한다.

This study proposes measures for the mid- to long-term enhancement of preservice teacher training curricula and the establishment of a sustainable feedback framework by analyzing quantitative course evaluation ratings (2,166 courses) and qualitative narrative data (4,852 cases) over the past five years from the college of education and pedagogical certificate programs at a university in the metropolitan area. The results revealed that preservice teachers exhibited higher satisfaction with practical, field-connected learning and creative tasks—such as teaching aid development and microteaching—than with unidirectional theory lectures. Conversely, critical demands were identified for stronger alignment with the teacher recruitment examination and national curriculum, optimization of assignment submission timing, and transparency of evaluation criteria. Based on these findings, a four-pillar virtuous feedback framework is proposed: course and assignment design, evaluation fairness and operational punctuality, recruitment and field connectivity, and feedback support infrastructure. Although limited by its reliance on single-university data, this study holds practical significance by empirically capturing longitudinal trends in preservice teachers' perceptions, providing foundational empirical data and practical insights for curriculum optimization and policy implementation in teacher training institutions.

Keywords

Preservice teacher education

Course evaluation

Text mining

Curriculum improvement

Educational feedback system

References

H.W. Marsh, “Students’ Evaluations of University Teaching: Dimensionality, Reliability, Validity, Potential Biases, and Utility”, Journal of Educational Psychology, Vol. 76, No. 5, pp. 707-754, 1984.
10.1037/0022-0663.76.5.707
P. Spooren, B. Brockx, and D. Mortelmans, “On the Validity of Student Evaluation of Teaching: The State of the Art”, Review of Educational Research, Vol. 83, No. 4, pp. 598-642, 2013.
10.3102/0034654313496870
J. Shin, and J. Choi, “Text Mining Analysis of College Students’ Descriptive Course Evaluation”, The Journal of Learner-Centered Curriculum and Instruction, Vol. 19, No. 16, pp. 77-99, 2019.
10.22251/jlcci.2019.19.16.77
S.-C. Lee, “Analysis of Descriptive Lecture Evaluation Using Text Mining: Comparative Analysis Pre and Post COVID-19”, Journal of The Korea Society of Computer and Information, Vol. 27, No. 10, pp. 211-222, 2022.
K.-H. Kim, J.-Y. Sung, S.-B. Choi, and C.-W. Kang, “Classification of D University Evaluation Text Using Text Mining”, Journal of the Korean Data Analysis Society, Vol. 23, No. 6, pp. 2599-2606, 2021.
10.37727/jkdas.2021.23.6.2599
K.-G. Park, “Exploring Methods to Improve Teaching through Lecture Evaluation Text Analysis”, The Journal of General Education, No. 26, pp. 7-41, 2024.
10.24173/jge.2024.01.26.1
B.-H. Leem, H.-J. Kang, and S.-W. Eum, SRIJAN KUMAR, “Using Sentiment Analysis to Analyze the Feedback of Students with Open-ended Questions”, Studies in Humanities and Social Sciences, Vol. 63, No. 2, pp. 55-64, 2020.
10.17939/HUSHSS.2020.63.2.004
H. Kil, and A. Shin, “Analysis of Changes in Learning Objectives for Educational Evaluation Courses Using Text Mining”, Korean Educational Research Journal, Vol. 45, No. 2, pp. 229-256, 2024.
10.55152/KERJ.45.2.229
H.D. Lee, and M.W. Nam, “Better Class’s Characteristics by Major Field Based on the Analysis of Text Mining from College Course Evaluation Subjective Results”, Research in Early Childhood Education, Vol. 20, No. 2, pp. 21-41, 2018.
10.15409/riece.2018.20.2.2
M. Nam, and E.S. Cho, “Analysis of Better Class Characteristics and Patterns from College Lecture Evaluation by Longitudinal Big Data”, International Journal of Contents, Vol. 15, No. 3, pp. 7-12, 2019.
10.5392/IJOC.2019.15.3.007
A. Onan, “Mining Opinions from Instructor Evaluation Reviews: A Deep Learning Approach”, Computer Applications in Engineering Education, Vol. 28, No.1, pp. 117-138, 2020.
10.1002/cae.22179
B. Uttl, C.A. White, and D.W. Gonzalez, “Meta-analysis of Faculty’s Teaching Effectiveness: Student Evaluation of Teaching Ratings and Student Learning Are Not Related”, Studies in Educational Evaluation, Vol. 54, pp. 22-42, 2017.
10.1016/j.stueduc.2016.08.007
C. Lim, S. Lee, M. Lim, G. Kim, and E. Lim, “Development of an instructional design program using the AI digital textbook for pre-service teachers”, THE JOURNAL OF KOREAN ASSOCIATION OF COMPUTER EDUCATION, Vol. 28, Issue 9, pp. 22-38, 2025.
10.32431/kace.2025.28.9.003

Information

Publisher :The Society of Convergence Knowledge
Publisher(Ko) :융복합지식학회
Journal Title :The Society of Convergence Knowledge Transactions
Journal Title(Ko) :융복합지식학회논문지
Volume : 14
No :3
Pages :165-177
DOI :https://doi.org/10.22716/sckt.2026.14.3.012

[1] H.W. Marsh, “Students’ Evaluations of University Teaching: Dimensionality, Reliability, Validity, Potential Biases, and Utility”, Journal of Educational Psychology, Vol. 76, No. 5, pp. 707-754, 1984.
10.1037/0022-0663.76.5.707

[2] P. Spooren, B. Brockx, and D. Mortelmans, “On the Validity of Student Evaluation of Teaching: The State of the Art”, Review of Educational Research, Vol. 83, No. 4, pp. 598-642, 2013.
10.3102/0034654313496870

[3] J. Shin, and J. Choi, “Text Mining Analysis of College Students’ Descriptive Course Evaluation”, The Journal of Learner-Centered Curriculum and Instruction, Vol. 19, No. 16, pp. 77-99, 2019.
10.22251/jlcci.2019.19.16.77

[4] S.-C. Lee, “Analysis of Descriptive Lecture Evaluation Using Text Mining: Comparative Analysis Pre and Post COVID-19”, Journal of The Korea Society of Computer and Information, Vol. 27, No. 10, pp. 211-222, 2022.

[5] K.-H. Kim, J.-Y. Sung, S.-B. Choi, and C.-W. Kang, “Classification of D University Evaluation Text Using Text Mining”, Journal of the Korean Data Analysis Society, Vol. 23, No. 6, pp. 2599-2606, 2021.
10.37727/jkdas.2021.23.6.2599

[6] K.-G. Park, “Exploring Methods to Improve Teaching through Lecture Evaluation Text Analysis”, The Journal of General Education, No. 26, pp. 7-41, 2024.
10.24173/jge.2024.01.26.1

[7] B.-H. Leem, H.-J. Kang, and S.-W. Eum, SRIJAN KUMAR, “Using Sentiment Analysis to Analyze the Feedback of Students with Open-ended Questions”, Studies in Humanities and Social Sciences, Vol. 63, No. 2, pp. 55-64, 2020.
10.17939/HUSHSS.2020.63.2.004

[8] H. Kil, and A. Shin, “Analysis of Changes in Learning Objectives for Educational Evaluation Courses Using Text Mining”, Korean Educational Research Journal, Vol. 45, No. 2, pp. 229-256, 2024.
10.55152/KERJ.45.2.229

[9] H.D. Lee, and M.W. Nam, “Better Class’s Characteristics by Major Field Based on the Analysis of Text Mining from College Course Evaluation Subjective Results”, Research in Early Childhood Education, Vol. 20, No. 2, pp. 21-41, 2018.
10.15409/riece.2018.20.2.2

[10] M. Nam, and E.S. Cho, “Analysis of Better Class Characteristics and Patterns from College Lecture Evaluation by Longitudinal Big Data”, International Journal of Contents, Vol. 15, No. 3, pp. 7-12, 2019.
10.5392/IJOC.2019.15.3.007

[11] A. Onan, “Mining Opinions from Instructor Evaluation Reviews: A Deep Learning Approach”, Computer Applications in Engineering Education, Vol. 28, No.1, pp. 117-138, 2020.
10.1002/cae.22179

[12] B. Uttl, C.A. White, and D.W. Gonzalez, “Meta-analysis of Faculty’s Teaching Effectiveness: Student Evaluation of Teaching Ratings and Student Learning Are Not Related”, Studies in Educational Evaluation, Vol. 54, pp. 22-42, 2017.
10.1016/j.stueduc.2016.08.007

[13] C. Lim, S. Lee, M. Lim, G. Kim, and E. Lim, “Development of an instructional design program using the AI digital textbook for pre-service teachers”, THE JOURNAL OF KOREAN ASSOCIATION OF COMPUTER EDUCATION, Vol. 28, Issue 9, pp. 22-38, 2025.
10.32431/kace.2025.28.9.003

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