Iranian Journal of Curriculum Studies

Iranian Journal of Curriculum Studies

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

Document Type : Original Article

Authors
Najma Asghari 1
Javad Hatami 1
Farhad Seraji 2
Esmaeel Azimi yancheshmeh 1
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
10.22034/jcs.2026.584738.2599
Abstract
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.
Keywords
Personalized Learning
Curriculum Development
Artificial Intelligence
Decision Tree Algorithm
Specialized Learning Paths
Subjects
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  • Receive Date 02 June 2026
  • Revise Date 19 August 2026
  • Accept Date 29 August 2026