مطالعات برنامه درسی

مطالعات برنامه درسی

طراحی و اعتبارسنجی مدل هوشمند برای شخصی‌سازی مسیرهای یادگیری تخصصی در رشته برنامه‌ریزی درسی مبتنی بر الگوریتم درخت تصمیم

نوع مقاله : مقاله پژوهشی

نویسندگان
1 گروه علوم تربیتی، دانشکده علوم انسانی، دانشگاه تربیت مدرس، تهران، ایران
2 گروه روش ها، برنامه ریزی آموزشی و درسی، دانشکده روانشناسی و علوم تربیتی دانشگاه تهران، تهران، ایران
3 گروه علوم تربیتی، دانشکده علوم انسانی، دانشگاه تربیت مدرس، تهران
چکیده
با توجه به تنوع زیرحوزه‌های رشته برنامه‌ریزی درسی، شخصی‌سازی مسیرهای یادگیری ضرورتی انکارناپذیر است. پژوهش حاضر با هدف طراحی و اعتبارسنجی مدلی هوشمند مبتنی بر الگوریتم درخت تصمیم برای نظام‌مندسازی مسیرهای تخصصی یادگیری انجام شد. در این پژوهش کاربردی‌-ترکیبی، ابتدا شاخص‌ها با مشارکت هشت خبره و روش دلفی فازی شناسایی گردید. سپس داده‌های چهل‌وپنج دانشجوی دکتری از طریق نمونه‌گیری تصادفی طبقه‌ای با ابزار پرسشنامه گردآوری و مدل درخت تصمیم با اعتبارسنجی متقابل آموزش داده شد. بیست‌ودو شاخص در حوزه‌های فردی، شناختی و حرفه‌ای تأیید گردید. مدل شصت‌وپنج قاعده تصمیم استخراج کرد که متغیر مدیریت زمان گره ریشه آن بود. ارزیابی‌ها نشان‌دهنده میانگین دقت کل نودوهشت صدم و برتری آماری معنادار نسبت به روش‌های رقیب بود. این مدل تفسیرپذیر، ابزاری کارآمد برای هدایت تحصیلی و شغلی است. پیشنهاد می‌شود الگوریتم حاضر در سامانه‌های مدیریت یادگیری دانشگاهی ادغام گردد. محدودیت اصلی پژوهش، حجم نمونه کوچک به دلیل محدودیت جامعه آماری دانشجویان دکتری بود.
کلیدواژه‌ها
موضوعات

عنوان مقاله English

Design and Validation of an Intelligent Model for Personalizing Specialized Learning Paths in Curriculum Studies Based on the Decision Tree Algorithm

نویسندگان English

Najma Asghari 1
Javad Hatami 1
Farhad Seraji 2
Esmaeel Azimi yancheshmeh 3
1 Department of Educational Sciences, Faculty of Humanities, Tarbiat Modares University, Tehran, Iran
2 Department of Curriculum Development and Instruction Methods, Faculty of Psychology and Education, University of Tehran, Tehran, Iran
3 Department of Educational Sciences, Faculty of Humanities, Tarbiat Modares University, Tehran, Iran
چکیده English

Given the diversity of sub-disciplines in curriculum studies, personalizing learning paths is an undeniable necessity. This study aimed to design and validate an intelligent model based on the decision tree algorithm to systematize specialized learning paths. This applied research utilized a mixed-methods approach. In the qualitative phase, indicators were identified using the fuzzy Delphi method with a panel of 8 experts. In the quantitative phase, data from 45 doctoral students were collected via stratified random sampling, and the Classification and Regression Trees (CART) algorithm was trained. Twenty-two indicators across individual, cognitive, and professional categories were confirmed. The model extracted 65 transparent decision rules, identifying "time management" as the root node. Evaluations demonstrated high efficiency, achieving an average overall accuracy of 0.98 and an F1-score of 0.97, which statistically outperformed competing algorithms. The highly interpretable decision tree model is an effective tool for academic and career counseling. Integrating this model as a module in learning management systems is recommended to provide dynamic and targeted educational recommendations.

کلیدواژه‌ها English

Personalized Learning
Curriculum Development
Artificial Intelligence
Decision Tree Algorithm
Specialized Learning Paths
Barnes, E., Hutson, J., & Perry, K. (2024). Enhancing adult learner success in higher education through decision tree models: A machine learning approach. Forum for Education Studies, 2(3), Article 1415. https://doi.org/10.59400/fes.v2i3.1415
Das, A., Malaviya, S., & Singh, M. (2023). The impact of AI-driven personalization on learners’ performance. International Journal of Computer Sciences and Engineering, 11(8), 15–22. https://doi.org/10.26438/ijcse/v11i8.1522
Delahoz-Domínguez, E. J., & Hijón-Neira, R. (2024). Recommender system for university degree selection: A socioeconomic and standardised test data approach. Applied Sciences, 14(18), Article 8311. https://doi.org/10.3390/app14188311
Ekramifar, M. (2023). Predicting student performance using decision tree algorithm [Master’s thesis, Ministry of Science and Research]. IranDoc. ://ganj.irandoc.ac.ir/viewer/a501ab5ba6fcb4e80106c5cf6257e442. [In Persian].
Fathi, F., Fathi-Vajargah, K., Jafari, E., & Vahidi-Asl, M. (2024). Designing a workplace digital curriculum based on artificial intelligence components. Human Information Interaction, 11(3), 71–92. http://hii.khu.ac.ir/article-1-3183-en.html. [In Persian].
Fathi-Vajargah, K. (2018). Toward the third wave in curriculum studies in Iran: Moving beyond neo-understanding and in search of pluralism in curriculum climates. Journal of Higher Education Curriculum Studies, 18(9), 45–69. https://www.noormags.ir/view/en/articlepage/downloadpdf/1436387. [In Persian].
Fathi-Vajargah, K. (2021). Curriculum identities: An account of curriculum theorists’ perspectives during classical, neo-understanding, and post-neo-understanding periods (Vol. 1). Ayezh Publications. [In Persian].
Fathi-Vajargah, K. (2022). A new dream: Multi-grounding curriculum studies in the new century (From a landless discipline to a borderless discipline). Iranian Journal of Curriculum Studies, 17(65), 7–26. https://doi.org/20.1001.1.17354986.1401.17.65.2.0. [In Persian].
Fathi-Vajargah, K. (2025). The implications of technological transformations for the field of curriculum with a neo/multi-platforming approach: From learning experience platforms to guerrilla curriculum. Journal of Education and Human Resource Development, 11(42)]. https://doi.org/10.61882/istd.49893.11.42.150. [In Persian].
Haghi, M. (2023). Predicting the performance of medical students based on educational data analysis using data mining techniques (Case study: Some medical universities) [Master’s thesis, Iran University of Science and Technology]. https://ganj.irandoc.ac.ir/viewer/be4b29c28ce434c7bd1dad24cbcd74e7. [In Persian].
Hatami, J. (2021). Competency-based curriculum: An undeniable necessity. Journal of Higher Education Curriculum Studies, 12(24). https://dor.isc.ac/dor/20.1001.1.25382241.1400.12.24.2.4. [In Persian].         
Jiang, X. (2021). Online English teaching course score analysis based on decision tree mining algorithm. Complexity, 2021, Article 5577167. https://doi.org/10.1155/2021/5577167
Karami, M., Hejazi, Z., & Tabatabaei, M. S. (2016). Identification, comparison, and analysis of dominant and neglected thematic trends in university textbooks of curriculum studies in Iran and the world. Research and Writing of University Textbooks, 20(38), 48–63. https://doi.org/20.1001.1.26767503.1395.20.38.3.7. [In Persian].         
Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel Psychology, 28(4), 563-575. https://doi.org/10.1111/j.1744-6570.1975.tb01393.x
Matzavela, V., & Alepis, E. (2021). Mobile learning analytics and decision trees for student performance prediction. Education and Information Technologies, 26(3), 2983–3002. https://doi.org/10.1007/s10639-020-10341-9
Mehrmohammadi, M. (2013). Clarifying the mutual expectations of the curriculum studies academic community and the educational system: A discussion on reconstructing the professional status of the field and its graduates in Iran. Theory and Practice in Curriculum Studies, 1(2), 47–64. http://cstp.khu.ac.ir/article-1-1917-fa.html. [In Persian].         
Naderi, R., Hatami, J., Vedadhir, A.A., Fakhroleslam, M. & Tekyekhah.R.S. (2026). The Futureof Educational Systems and Curriculum Based on Generative Artificial Intelligence: A Systematic Review. Iranian Journal of curriculum studies. 21 (80). https://doi.org/ 10.22034/jcs.2026.539210.2476. [In Persian].
Nguyen, H. H., & Nguyen, V. A. (2023). Personalized learning in the online learning from 2011 to 2021: A bibliometric analysis. International Journal of Information and Education Technology, 13(8), 1261–1272. https://doi.org/10.18178/ijiet.2023.13.8.1928
Ojaghi, H., Zohourian-Nadali, I., & Soleimani-Rozbehani, F. (2023). Mapping the ecosystem of artificial intelligence products and producers in Iran based on transformative scope. Iranian Journal of Information and Communication Technology, 57(57), 138–152. https://rimag.ricest.ac.ir/Article/44108/jour.aicti.ir. [In Persian].         
Ryoo, J., & Winkelmann, K. (Eds.). (2021). Innovative learning environments in STEM higher education: Opportunities, challenges, and looking forward. Springer. https://doi.org/10.1007/978-3-030-58948-6
Shah Talebi, S., Liaghatdar, M. J., & Sharifian, F. (2016). Skill competencies of doctoral graduates in curriculum studies in Iran: Review of experts’ and specialists’ perspectives. Journal of Curriculum Planning Research, 13(51), 21–37. [In Persian].
Shemshack, A., & Spector, J. M. (2020). A systematic literature review of personalized learning terms. Smart Learning Environments, 7(1), Article 1. https://doi.org/10.1186/s40561-020-00140-9.
Sun, Y. (2024). Decision trees for evaluating blended learning effectiveness in higher education. Journal of Educational Technology Systems, 53(2), 112–134. https://doi.org/10.1177/0047239524123456
Waltz, C. F., & Bausell, R. B. (1981). Nursing research: Design, statistics, and computer analysis. F. A. Davis Company.
Wu, C. H., & Fang, W. C. (2011). Combining the fuzzy analytic hierarchy process and the fuzzy Delphi method for evaluating flexible manufacturing systems. Expert Systems with Applications, 38(11), 14197-14205. https://doi.org/10.1007/s11135-010-9425-6
Yadgarzadeh, F. (2017). An analysis of job- and occupation-based components in curriculum design. Higher Education Curriculum Studies, 15(8), 135–178. https://dor.isc.ac/dor/20.1001.1.25382241.1396.8.15.6.2. [In Persian].         
Yadgarzadeh, F. (2018). Identifying the components of professional responsibility among curriculum specialists. Curriculum Studies Research, 16(8), 199–223. https://doi.org/10.22099/jcr.2019.5124. [In Persian].         
Yadgarzadeh, Gh., Fathi-Vajargah, K., Mehrmohammadi, M., & Arefi, M. (2013). Analysis of professional roles and activities of curriculum specialists. Iranian Journal of Curriculum Studies, 8(30). https://www.noormags.ir/view/fa/articlepage/1059884/. [In Persian].         
 
دوره 21، شماره 81 - شماره پیاپی 81
با تمرکز بر جنبه های فرهنگی و اجتماعی برنامه درسی
تابستان 1405
صفحه 227-250

  • تاریخ دریافت 12 خرداد 1405
  • تاریخ بازنگری 28 مرداد 1405
  • تاریخ پذیرش 07 شهریور 1405