مطالعات اندازه گیری و ارزشیابی آموزشی

مطالعات اندازه گیری و ارزشیابی آموزشی

تحلیل داده‌محور، راهکاری برای نظارت و ارزشیابی عملکرد مدرسان در دانشگاه جامع علمی کاربردی

نوع مقاله : مقاله پژوهشی

نویسندگان
1 استادیار، دانشگاه جامع علمی کاربردی، گروه صنعت، تهران، ایران
2 استادیار ، دانشگاه جامع علمی کاربردی، گروه مدیریت و خدمات اجتماعی، تهران، ایران
3 سرپرست اداره کل هماهنگی پذیرش، سازمان سنجش آموزش کشور، تهران، ایران
10.22034/emes.2025.2054143.2628
چکیده
هدف: هدف اصلی از اجرای این پژوهش، تحلیل مبتنی بر داده برای ارزیابی عملکرد مدرسان در مراکز آموزش علمی کاربردی بود.
روش پژوهش: این پژوهش با روش‌های کمی و کیفی اجرا شده است. داده‌های مربوط به ویژگی‌های مدرس از سامانه جامع آموزشی دانشگاه و سایر داده‌ها از طریق پرسشنامه‌های آنلاین سامانه جامع آموزشی دانشگاه جامع علمی کاربردی در سراسر کشور گرد‌آوری شده است. نظرات دانشجویان 32 مرکز آموزش علمی کاربردی در استان‌های مختلف، شامل یک نظرسنجی با ۲۳ سؤال درباره 1605 مدرس و 6359  کلاس_درس، مورد بررسی قرار گرفت. این مطالعه درمجموع شامل 146257 رکورد (نفر-درس) بود و داده‌ها با استفاده از تکنیک‌های داده‌کاوی تحلیل شدند.
یافته‌ها: نتایج نشان می‌دهد نظرات دانشجویان به‌عنوان منبع غنی اطلاعات، می‌تواند به شناسایی نقاط قوت و ضعف مدرسان و بهینه‌سازی فرایند آموزش کمک کند. همچنین، استفاده از الگوریتم‌های داده‌کاوی، ابزاری برای شناسایی تخلفات و اشکالات احتمالی در مراکز آموزشی شده است.
نتیجه‌گیری: نظارت مبتنی بر نظرات دانشجویان می‌تواند به مدیران دانشگاه در شناسایی مشکلات مدرسان، کلاس‌ها، امکانات آموزشی، بهبود کیفیت تدریس و ارتقای سطح آموزشی مراکز دانشگاهی کمک کند. پیشنهاد می‌شود مراکز آموزشی توجه بیشتری به پیاده‌سازی سیستم‌های هوشمند برای گرد‌آوری و تحلیل نظرات دانشجویان داشته باشند.
کلیدواژه‌ها

موضوعات


عنوان مقاله English

Data-Driven Analysis: A Solution for Monitoring and Evaluating the Performance of Instructors at the University of Applied Science and Technology

نویسندگان English

Mostafa Yousefi Tezerjan 1
Maryam Mollabagher 2
Esrafil Ala 3
1 Assistant Professor,University of Applied Sciences and Technology, Department of Industry, Tehran, Iran
2 Assistant Professor, University of Applied Science and Technology, Department of Management and Social Services, Tehran, Iran
3 Head of the Admission Coordination Department, National Organization of Educational Testing, Tehran, Iran
چکیده English

Objective: The main purpose of this research is to conduct data-based analysis for evaluating the performance of instructors in Applied Sciences  Education Centers.
Methods: This research was conducted using quantitative and qualitative methods. Data on instructor characteristics were collected from the university's comprehensive educational system, and other data were collected through online questionnaires from the comprehensive educational system of the Comprehensive University of Applied Sciences across the country. The opinions of students from 32 applied science education centers in different provinces were examined, including a survey with 23 questions about 1605 instructors and 6359 classes. The study included a total of 146257 records (person-classes) and the data were analyzed using data mining techniques.
Results: The results show that students' opinions, as a rich source of information, can help identify the strengths and weaknesses of instructors and optimize the education process. Also, the use of data mining algorithms has become a tool for identifying possible violations and problems in educational centers.
Conclusion: Monitoring based on students' opinions can help university administrators identify problems with instructors, classrooms, and facilities of educational centers, and enhance the quality of teaching and the educational level of university centers.

کلیدواژه‌ها English

Keywords: Monitoring
Performance evaluation
Instructors
Students
University of Applied Science and Technology
Data mining

References

Ajmal, M., Basit, I., & Sadaf, S. (2024). Evaluating the role of students' feedback in enhancing teaching effectiveness. Pakistan Journal of Humanities & Social Sciences. https://doi.org/10.52131/pjhss.2024.v12i2.2255
Al-Janabi, S. (2018). Data Reduction Techniques: A Comparative Study for Attribute Selection Methods. International Journal of Advanced Computer Science & Technology (IJACST), 8(1), 1–13.
Bellaj, M., Ben Dahmane, A., Boudra, S., & Lamarti Sefian, M. (2024). Educational data mining: Employing machine learning techniques and hyperparameter optimization to improve students’ academic performance. International Journal of Online & Biomedical Engineering (iJOE), 20(3), 55–74. https://doi.org/10.3991/ijoe.v20i03.46287
Bhowmik, A., Noor, N. M., Miah, M. S. U., Mazid-Ul-Haque, M., & Karmaker, D. (2023). A comprehensive dataset for aspect-based sentiment analysis in evaluating teacher performance. The AIUB Journal of Science & Engineering. https://doi.org/10.53799/ajse.v22i2.862
Bhowmik, A., Noor, N. M., Mazid-Ul-Haque, M., Miah, M. S. U., & Karmaker, D. (2024). Evaluating teachers’ performance through aspect-based sentiment analysis. https://doi.org/10.1109/i2ct61223.2024.10543706
Boysen, G. A. (2016). Using student evaluations to improve teaching: Evidence-based recommendations. Scholarship of Teaching & Learning in Psychology, 2(4), 273284.
Buchanan, E. M., Miranda, J. F., & Stephens, C. (2023). The reliability of student evaluations of teaching. https://doi.org/10.31219/osf.io/ewxt6
Chakrabarti, S., & De, P. (2024). Evaluation and assessment of teaching quality and students’ performance using machine learning. https://doi.org/10.21203/rs.3.rs-3934509/v2
Deng, L., Li, D., Yao, X., et al. (2019). RETRACTED ARTICLE: Mobile network intrusion detection for IoT system based on transfer learning algorithm. Cluster Computing,
Gao, P. (2024). Classroom teaching evaluation based on data mining technology. https://doi.org/10.1007/978-981-97-1979-2_36
Gu, Y. (2023). Exploring the application of teaching evaluation models incorporating association rules and weighted naive Bayesian algorithms. Intelligent Systems with Applications, 20, 200297. https://doi.org/10.1016/j.iswa.2023.200297
Heffernan, T. (2022). Sexism, racism, prejudice, and bias: A literature review and synthesis of research surrounding student evaluations of courses and teaching. Assessment & Evaluation in Higher Education, 47(1), 144154. https://doi.org/10.1080/02602938.2021.1888075
Kharusi, I. A. (2023). Students’ evaluation of teaching (SET) for improving learning and teaching quality in HE: Students’ and teachers’ perspectives. Journal of World Englishes & Educational Practices. https://doi.org/10.32996/jweep.2023.5.3.8
Li, Y., & Zhang, H. (2024). Big data technology for teaching quality monitoring and improvement in higher education - joint K-means clustering algorithm and Apriori algorithm. Systems & Soft Computing, 6, 200125. https://doi.org/10.1016/j.sasc.2024.200125
Martin, S., Beecham, E., Kursumovic, E., Armstrong, R. R., Cook, T., Déom, N., Kane, A. D., Moniz, S., Soar, J., & Vindrola‐Padros, C. (2024). Comparing human vs. machine-assisted analysis to develop a new approach for big qualitative data analysis. https://doi.org/10.1101/2024.07.16.24310275
Misbah, M., & Dilshad, M. (2023). Effectiveness of teachers’ feedback at universities: Analysis of students’ perspective. Global Educational Studies Review. https://doi.org/10.31703/gesr.2023(viii-i).30
Mohammadi, H., Derakhshan, M., & Nejadi, P. (2015), Predicting the level of satisfaction with university lecturers using data mining techniques, Third National Computer Conference, https://civilica.com/doc/482083 [In Persian]
Moralejo, L., Andersen, E., Hilsman, N., & Kennedy, L. (2019). Measuring student responses in and instructors' perceptions of student evaluation of teaching (SETs), pre and post intervention. Canadian Journal for the Scholarship of Teaching & Learning, 10(3), 121. https://doi.org/10.5206/cjsotl-rcacea.2019.3.8052
Pleimling, X., & Lourentzou, I. (2022). [Data] Quality lies in the eyes of the beholder. Petra. https://doi.org/10.1145/3529190.3529222
Rao, W. (2024). Design and implementation of college students’ physical education teaching information management system by data mining technology. Heliyon, 10(16), e36393. https://doi.org/10.1016/j.heliyon.2024.e36393
Shah, M., & Pabel, A. (2019). Making the student voice count: Using qualitative student feedback to enhance the student experience. Journal of Applied Research in Higher Education, 12(2), 194209. https://doi.org/10.1108/JARHE-02-2019-0030
Shahmoradi, M., Sharifi Rahnamo M., Sharifi Rahnamo, S. & Fathi, A. (2023). Presenting an appropriate model for the educational-research system of the National Defense University and Higher Research Institute with a dependency approach. Education in Law Enforcement Sciences, 11(40), 147-180. https://doi.org/10.22034/tps.2023.1270048.1673 [In Persian]
Shindo, J. H., Mjahidi, M. M., & Waziri, M. D. (2023). Data mining algorithms for prediction of student teachers’ performance in ICT: A systematic literature review. Information Technologies and Learning Tools. https://doi.org/10.33407/itlt.v96i4.5246
Sidwell, D., Lee, D., Zimmerman, P.-A., Bentley, S., & Barton, M. (2024). Teaching faculty experiences with student evaluation of instruction: A mixed-methods study. Teaching & Learning in Nursing. https://doi.org/10.1016/j.teln.2024.11.009
Sirait, J. C. B., Togatorop, P. R., Tambunan, P. M. L. P., Marpaung, Y. F. Y., Situmeang, S. I. G., & Simanjuntak, H. (2023). Predicting students performance using data mining approach (Case study: IT Del). https://doi.org/10.1109/icodse59534.2023.10291748
Sun, X. (2023, September). Evaluation on the application of data mining in the evaluation system of teaching ability of double teacher teachers. In 2023 International Conference on Network, Multimedia & Information Technology (NMITCON) (pp. 15). IEEE.
Tan, P., Xu, H., Liu, L., Ai, J., Xu, H., Jiang, Y., Zhang, X., Yang, L., & Wei, Q. (2018). The prognostic value of preoperative neutrophil-to-lymphocyte ratio in patients with upper tract urothelial carcinoma. Clinica Chimica Acta, 485, 2632. https://doi.org/10.1016/j.cca.2018.06.019
Wang, L. (2024). Classroom Teaching Evaluation Based on Data Mining Technology. In Proceedings of the 3rd International Conference on Cognitive Based Information Processing and Applications—Volume 2, Lecture Notes on Data Engineering and Communications Technologies (Vol. 197, pp. 409–420). Springer Nature Singapore. https://doi.org/10.1007/978-981-97-1979-2_36