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Related Experiment Video

Updated: May 17, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Difficulty aware programming knowledge tracing via large language models.

Lina Yang1, Xinjie Sun2, Hui Li1

  • 1School of Computer Science, Liupanshui Normal University, Liupanshui, 553000, China.

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|April 3, 2025
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Summary

This study introduces a new Difficulty-aware Programming Knowledge Tracing (DPKT) model. DPKT accurately assesses programming problem difficulty, enhancing knowledge state prediction for personalized learning in education.

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Area of Science:

  • Artificial Intelligence
  • Educational Technology
  • Computer Science

Background:

  • Knowledge Tracing (KT) models student mastery using intelligent tutoring systems.
  • Current KT methods lack focus on problem difficulty's impact on knowledge states.
  • Programming problem difficulty, encompassing text and concept aspects, is vital for accurate assessment.

Purpose of the Study:

  • To develop a novel model for assessing programming problem difficulty.
  • To improve knowledge state prediction by incorporating difficulty metrics.
  • To enhance personalized learning in programming education.

Main Methods:

  • Proposed a Difficulty-aware Programming Knowledge Tracing (DPKT) model utilizing Large Language Models (LLMs).
  • Extracted text understanding and knowledge concept difficulty from programming problems.
  • Employed an attention mechanism to analyze difficulty relationships and a graph attention network with an update gate mechanism for dynamic knowledge state updates.

Main Results:

  • DPKT effectively extracts text understanding and knowledge concept difficulty.
  • The model significantly improves the accuracy of programming problem difficulty assessment.
  • Enhanced spatiotemporal reflection capability of knowledge states was demonstrated.
  • Excellent performance across various language datasets was observed.

Conclusions:

  • DPKT offers an innovative solution for programming knowledge tracing.
  • The model provides educators with a valuable tool for personalized learning.
  • Accurate difficulty assessment is crucial for effective knowledge tracing and educational interventions.