کاربردهای مدل‌سازی یادگیرنده در طراحی برنامه‌درسی: مرور نظام‌مند

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری، گروه علوم تربیتی، دانشکده علوم تربیتی و روانشناسی، دانشگاه بیرجند، بیرجند، ایران

2 گروه علوم تربیتی، دانشکده علوم تربیتی و روانشناسی، دانشگاه بیرجند، بیرجند، ایران

10.22034/lbcij.2025.67775.1284

چکیده

هدف: توجه به تفاوت های فردی در طراحی و رهبری برنامه درسی بسیار حائز اهمیت است. مدل‌سازی یادگیرنده، با قابلیت کمک به شخصی‌سازی یادگیری و بهینه‌سازی منابع، به‌عنوان یک راه حل در این مسیر می‌تواند مطرح شود. این مطالعه با تمرکز بر چهار سوال (تعریف مدل‌سازی یادگیرنده، انواع، کاربردها و روش‌های پیاده‌سازی آن) به تحلیل نقش مدل‌سازی یادگیرنده در طراحی برنامه درسی می‌پردازد. 
روش پژوهش: به روش مرور نظام‌مند براساس دستورالعمل پریزما، ۵1 مقاله از پایگاه‌های Web of Science و Scopus استخراج شد. پس از غربالگری، 12 مقاله با استفاده از تحلیل استقرایی و کدگذاری کیفی تحلیل شدند. 
یافته‌ها: بر اساس بررسی و تحلیل دقیق مقالات، تعریف مدل‌سازی یادگیرنده در طراحی برنامه درسی در سه چارچوب مختلف مبتنی بر هوش مصنوعی، مبتنی بر هستی شناسی و مبتنی بر اطلاعات شناختی یادگیرنده تعریف و در هفت نوع پوششی، کلیشه‌ای، اغتشاش، مبتنی بر یادگیری ماشین، ردیابی، هستی‌شناسی و ترکیبی طبقه‌بندی شدند. کاربردهای مدل‌سازی یادگیرنده شامل «تنظیم ساختار یادگیری»، «افزایش کارایی آموزش» و «طراحی برنامه‌های درسی شخصی‌سازی» بود. الگویتم پیاده‌سازی آن به سه دسته کدنویسی، هوش مصنوعی و ترکیبی تقسیم شدند. تفاوت اصلی آن‌ها در سطح وابستگی به داده و پیچیدگی سیستم است. مدل‌سازی یادگیرنده امکان تنظیم پویای برنامه درسی، بازخورد بلادرنگ و بهینه‌سازی عملکرد یادگیرندگان را فراهم می‌کند.
نتیجه‌گیری: مدل‌سازی یادگیرنده با پیوند نظریه‌های آموزشی و فناوری، چالش‌های نظام‌های متمرکز را کاهش می‌دهد. یکپارچه‌سازی هوش مصنوعی در چارچوب‌های تربیتی نیازمند همکاری بین‌رشته‌ای، مدل‌های نظری قوی و ملاحظات اخلاقی برای تحول پایدار است.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Applications of Learner Modeling in Curriculum Design: A Systematic Review

نویسندگان [English]

  • Mahboobeh Zohourian Moftakhar Ahmadi 1
  • Mohsen Ayati 2
  • Mohammadali Rostaminezhad 2
1 PhD Candidate, Department of Education, Faculty of Education & Psychology, University of Birjand, Birjand, Iran
2 Department of Education, Faculty of Education & Psychology, University of Birjand, Birjand, Iran
چکیده [English]

Objective: Considering individual differences in curriculum design and leadership is crucial. Learner modeling, with its potential to support personalized learning, tailored instruction, and resource efficiency, can be an effective method here. This study explores four key questions—namely, the definition, types, applications, and implementation methods of learner modeling—and examines its role in curriculum development.
Method: A systematic review was conducted in accordance with the PRISMA guidelines. A total of 51 articles were extracted from the Web of Science and Scopus databases. After screening, 12 articles were analyzed using exploratory analysis and qualitative coding. This process was carried out accordingly.
Results: Based on a thorough review and analysis of the articles, learner modeling in curriculum design was categorized into three frameworks: AI-based, ontology-based, and learner cognitive information-based. Learner modeling was further classified into seven types: overlay, stereotype, perturbation, machine learning-based, model tracing, ontology, and hybrid. Applications of learner modeling included structuring the learning process, enhancing educational efficiency, and designing personalized curricula. Implementation algorithms were divided into three categories: coding, artificial intelligence, and hybrid, with their main differences lying in the level of data dependency and system complexity. Learner modeling enables dynamic curriculum adjustment, real-time feedback, and optimization of learner performance.
Conclusions: Learner modeling, which links educational theories and technology, reduces the challenges of centralized systems. Integrating artificial intelligence into educational frameworks requires interdisciplinary collaboration, robust theoretical models, and ethical implications for sustainable transformation.

کلیدواژه‌ها [English]

  • academic achievement
  • artificial intelligence
  • curriculum design
  • learner modeling
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  • تاریخ دریافت: 25 خرداد 1404
  • تاریخ بازنگری: 09 آبان 1404
  • تاریخ پذیرش: 01 بهمن 1404
  • تاریخ اولین انتشار: 01 اسفند 1404
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