Machine Learning-Based Personalized Learning Path Decision-Making Method on Intelligent Education Platforms

Aug 5, 2026

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Rapid advances in information technology have expanded the use of intelligent education platforms, but traditional teaching methods cannot adequately support personalized learning. Personalized learning path decision-making analyzes learners’ behavioral data and knowledge-point mastery to tailor paths for individuals, improving efficiency and effectiveness. Existing approaches still struggle with accurately predicting knowledge-point difficulty and dynamically updating path recommendations. This paper presents a machine-learning-based method that predicts knowledge-point difficulty and integrates knowledge-point localization to enable dynamically optimized learning path decisions, strengthening learner experience and learning outcomes.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages15

Reading Time38 min

AccessFree to download

Introduction

Personalized learning path decision-making significance

Limitations of existing methods

Prediction of knowledge point difficulty for personalized learning

Calculation of knowledge point difficulty coefficient

Knowledge graph-based support

JIM International Journal of Interactive Mobile Technologies [Onli](Online-Journals.org)[ne-Jo](Online-Journals.org)[urnals](Online-Journals.org)[.org](Online-Journals.org) iJIM | eISSN: 1865-7923 | Vol. 18 No. 16 (2024) | [https://doi.org/10.3991/ijim.v18i16.51009](https://doi.org/10.3991/ijim.v18i16.51009) PAPER Machine Learning-Based Personalized Learning Path Decision-Making Method on Intelligent Education Platforms Yan Zhang(*) Department of Artificial Intelligence, Hebi Polytechnic, Hebi, China [zly570725@163.com](zly570725@163.com) ABSTRACT With the rapid development of information technology, the application of intelligent education platforms has become increasingly widespread. Traditional teaching methods struggle to meet the demands for personalized learning. Personalized learning path decision-making methods, which analyze learners’ behavioral data and mastery of knowledge points, tailor learning paths for each individual. Current research indicates that these methods can significantly improve learning efficiency and effectiveness. However, existing personalized learning path decision-making methods still have shortcomings in predicting the difficulty of knowledge points and dynamically adjusting path recommendations. This paper proposesa machine learning-based personalized learning path decision-making method, focusing on two main aspects: predicting the difficulty of knowledge points for personalized learning and integrating knowledge point localization for personalized learning path decisions. Through accurate prediction of knowledge point difficulty and dynamically optimized learning path recommendations, this method provides more refined and personalized learning support on intelligent education platforms, aiming to enhance learners’ learning experience and outcomes. KEYWORDS intelligent education platform, machine learning, personalized learning, learning path decision-making, knowledge point difficulty prediction, dynamic optimization 1 INTRODUCTION In the field of modern education, with the rapid development of information technology, the application of intelligent education platforms is becoming increasingly widespread [1–3] . Traditional teaching methods can no longer meet the needs for personalized learning, as learners require more customized learning paths to improve learning efficiency and effectiveness [4, 5] . Currently, machine Zhang, Y. (2024) . Machine Learning-Based Personalized Learning Path Decision-Making Method on Intelligent Education Platforms. International Journal of Interactive Mobile Technologies (iJIM), 18(16), pp. 68–82. [https://doi.org/10.3991/ijim.v18i16.51009](https://doi.org/10.3991/ijim.v18i16.51009)[ ](https://doi.org/10.3991/ijim.v18i16.51009)[Article submitted 2024-05-11. Revision uploaded 2024-07-03. Final acceptance 2024-07-09.](Article submitted 2024-05-11. Revision uploaded 2024-07-03. Final acceptance 2024-07-09.) © 2024 by the authors of this article. Published under CC-BY. 68 International Journal of Interactive Mobile Technologies (iJIM) iJIM | Vol. 18 No. 16 (2024) Machine Learning-Based Personalized Learning Path Decision-Making Method on Intelligent Education Platforms learning-based personalized learning path decision-making methods are gradually becoming a research hotspot. By analyzing learners’ behavioral data and knowledge point mastery, these methods tailor learning paths for each learner to help them better master knowledge. The research significance of personalized learning path decision-making lies in its ability to significantly enhance learning efficiency and effectiveness [6–9] . Through precise knowledge point localization and learning path recommendation, learners can study at a difficulty and pace suitable for them, thus avoiding the drawbacks of the “one-size-fits-all” approach in traditional teaching [5, 10–12] . More importantly, personalized learning paths can stimulate learners’ interest and motivation,

FAQs

How does the proposed method personalize learning paths on intelligent education platforms?

It uses machine learning to predict each knowledge point’s difficulty and then integrates knowledge-point localization to guide personalized path decisions.

What shortcomings in current research does the paper address?

It targets inaccurate knowledge-point difficulty prediction and the lack of dynamic adjustment mechanisms for path recommendations.

What outcome does the method aim to achieve for learners?

By enabling dynamically optimized and more refined recommendations, it seeks to improve learning efficiency, effectiveness, and overall learning experience and results.

Aug 5, 2026

Upload by Maeve