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Related Experiment Video

Updated: May 31, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

Hierarchical graph attention knowledge tracing and personalized recommendation for computer courses.

Jian Chen¹, Dajie Fu², Ang Li³

¹College of Information Engineering, Jiujiang Vocational University, No. 88, Lianxi Avenue, Jiujiang City, 332000, Jiangxi Province, China. 15949543351@163.com.

Scientific Reports

|May 29, 2026

Summary

This study introduces a hierarchical graph model for intelligent education, improving knowledge tracing and personalized recommendations by considering learning hierarchies and student interests. The framework enhances learning outcome prediction and resource delivery.

Keywords:

Educational data mining Graph attention network Hierarchical knowledge graph Knowledge tracing Personalized recommendation

Related Experiment Videos

Last Updated: May 31, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

Area of Science:

Educational Technology
Artificial Intelligence in Education
Data Science

Background:

Intelligent education relies on knowledge tracing and personalized recommendation for improved learning outcomes.
Existing methods use flat knowledge graphs, failing to capture hierarchical relationships (chapters, concepts, sub-concepts).
Current recommendation strategies often overlook learner interests and difficulty progression, limiting effectiveness.

Purpose of the Study:

To propose a hierarchical graph attention-based knowledge tracing and personalized recommendation framework (HGAKT+PR).
To address limitations in representing knowledge hierarchies and incorporating learner-specific factors in educational recommendations.
To enhance the accuracy of knowledge tracing and the relevance of personalized recommendations.

Main Methods:

Constructed a three-level knowledge graph representing chapters, knowledge points, and sub-knowledge points.
Applied multi-head graph attention and a dynamic gating mechanism for learning knowledge states.
Utilized shared representation learning for joint optimization of performance prediction and recommendation, integrating learner weaknesses, difficulty, and interests.

Main Results:

The HGAKT+PR framework demonstrated stable performance and outperformed baseline methods on real-world educational datasets.
The model effectively characterized learning states across various scenarios, enabling personalized resource delivery.
Experiments confirmed the model's robustness and consistent performance across different settings and parameter sensitivities.

Conclusions:

Hierarchical knowledge modeling combined with prediction-recommendation collaborative optimization significantly improves learning state estimation.
The proposed framework enhances personalized learning resource delivery by accurately capturing learner knowledge and preferences.
HGAKT+PR offers a more effective approach to knowledge tracing and recommendation in intelligent education systems.