Difficulty aware programming knowledge tracing via large language models
Abstract Knowledge Tracing (KT) assesses students’ mastery of specific knowledge concepts and predicts their problem-solving abilities by analyzing their interactions with intelligent tutoring systems. Although recent years have seen significant improvements in tracking accuracy with the introductio...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-04-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-96540-3 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850258792728494080 |
|---|---|
| author | Lina Yang Xinjie Sun Hui Li Ran Xu Xuqin Wei |
| author_facet | Lina Yang Xinjie Sun Hui Li Ran Xu Xuqin Wei |
| author_sort | Lina Yang |
| collection | DOAJ |
| description | Abstract Knowledge Tracing (KT) assesses students’ mastery of specific knowledge concepts and predicts their problem-solving abilities by analyzing their interactions with intelligent tutoring systems. Although recent years have seen significant improvements in tracking accuracy with the introduction of deep learning and graph neural network techniques, existing research has not sufficiently focused on the impact of difficulty on knowledge state. The text understanding difficulty and knowledge concept difficulty of programming problems are crucial for students’ responses; thus, accurately assessing these two types of difficulty and applying them to knowledge state prediction is a key challenge. To address this challenge, we propose a D ifficulty aware P rogramming K nowledge T racing via Large Language Models(DPKT) to extract the text understanding difficulty and knowledge concept difficulty of programming problems. Specifically, we analyze the relationship between knowledge concept difficulty and text understanding difficulty using an attention mechanism, allowing for dynamic updates to students’ s. This model combines an update gate mechanism with a graph attention network, significantly improving the assessment accuracy of programming problem difficulty and the spatiotemporal reflection capability of knowledge state. Experimental results demonstrate that this model performs excellently across various language datasets, validating its application value in programming education. This model provides an innovative solution for programming knowledge tracing and offers educators a powerful tool to promote personalized learning. |
| format | Article |
| id | doaj-art-eddcfd5694ef4469b80e4c8b2658d958 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-eddcfd5694ef4469b80e4c8b2658d9582025-08-20T01:56:01ZengNature PortfolioScientific Reports2045-23222025-04-0115111910.1038/s41598-025-96540-3Difficulty aware programming knowledge tracing via large language modelsLina Yang0Xinjie Sun1Hui Li2Ran Xu3Xuqin Wei4School of Computer Science, Liupanshui Normal UniversitySchool of Computer Science, Liupanshui Normal UniversitySchool of Computer Science, Liupanshui Normal UniversitySchool of Computer Science, Liupanshui Normal UniversitySchool of Computer Science, Liupanshui Normal UniversityAbstract Knowledge Tracing (KT) assesses students’ mastery of specific knowledge concepts and predicts their problem-solving abilities by analyzing their interactions with intelligent tutoring systems. Although recent years have seen significant improvements in tracking accuracy with the introduction of deep learning and graph neural network techniques, existing research has not sufficiently focused on the impact of difficulty on knowledge state. The text understanding difficulty and knowledge concept difficulty of programming problems are crucial for students’ responses; thus, accurately assessing these two types of difficulty and applying them to knowledge state prediction is a key challenge. To address this challenge, we propose a D ifficulty aware P rogramming K nowledge T racing via Large Language Models(DPKT) to extract the text understanding difficulty and knowledge concept difficulty of programming problems. Specifically, we analyze the relationship between knowledge concept difficulty and text understanding difficulty using an attention mechanism, allowing for dynamic updates to students’ s. This model combines an update gate mechanism with a graph attention network, significantly improving the assessment accuracy of programming problem difficulty and the spatiotemporal reflection capability of knowledge state. Experimental results demonstrate that this model performs excellently across various language datasets, validating its application value in programming education. This model provides an innovative solution for programming knowledge tracing and offers educators a powerful tool to promote personalized learning.https://doi.org/10.1038/s41598-025-96540-3 |
| spellingShingle | Lina Yang Xinjie Sun Hui Li Ran Xu Xuqin Wei Difficulty aware programming knowledge tracing via large language models Scientific Reports |
| title | Difficulty aware programming knowledge tracing via large language models |
| title_full | Difficulty aware programming knowledge tracing via large language models |
| title_fullStr | Difficulty aware programming knowledge tracing via large language models |
| title_full_unstemmed | Difficulty aware programming knowledge tracing via large language models |
| title_short | Difficulty aware programming knowledge tracing via large language models |
| title_sort | difficulty aware programming knowledge tracing via large language models |
| url | https://doi.org/10.1038/s41598-025-96540-3 |
| work_keys_str_mv | AT linayang difficultyawareprogrammingknowledgetracingvialargelanguagemodels AT xinjiesun difficultyawareprogrammingknowledgetracingvialargelanguagemodels AT huili difficultyawareprogrammingknowledgetracingvialargelanguagemodels AT ranxu difficultyawareprogrammingknowledgetracingvialargelanguagemodels AT xuqinwei difficultyawareprogrammingknowledgetracingvialargelanguagemodels |