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Related Experiment Video

Updated: Oct 1, 2025

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Published on: June 10, 2021

HELP-DKT: an interpretable cognitive model of how students learn programming based on deep knowledge tracing.

Yu Liang^1,2, Tianhao Peng³, Yanjun Pu³

¹School of Computer Science and Engineering, Beihang University, Beijing, 100091, China. yliang@buaa.edu.cn.

Scientific Reports

|March 8, 2022

Summary

This summary is machine-generated.

This study introduces HELP-DKT, an interpretable cognitive model for programming education. It enhances student learning by incorporating rich data and providing personalized feedback for better skill development.

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Area of Science:

Educational Technology
Artificial Intelligence in Education
Cognitive Modeling

Background:

Student cognitive models in intelligent tutoring systems often use limited binary responses, missing rich educational data.
Current deep learning models improve prediction but lack interpretability for personalized feedback.

Purpose of the Study:

To develop an interpretable cognitive model for programming education.
To enhance the personalization of intelligent tutoring systems.

Main Methods:

Introduced HELP-DKT, a deep knowledge tracing model.
Implemented a feature-rich input layer encoding raw code and error classifications.
Incorporated concept indicators for richer student representation.

Main Results:

Achieved strong prediction performance in student learning.
Demonstrated reasonable interpretability of student skill improvement.
Validated the model's effectiveness through experiments.

Conclusions:

HELP-DKT offers an interpretable approach to cognitive modeling in programming education.
The model effectively personalizes the learning experience for novice programmers.
Enables better understanding of student learning status for targeted feedback.