From Prediction to Application: Language Model-based Code Knowledge Tracing with Domain Adaptive Pre-Training and Automatic Feedback System with Pedagogical Prompting for Comprehensive Programming Education
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arXiv
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| Autores principales: | , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866909302203088896 |
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| author | Lee, Unggi Bae, Jiyeong Jung, Yeonji Kang, Minji Byun, Gyuri Lee, Yeonseo Kim, Dohee Lee, Sookbun Park, Jaekwon Ahn, Taekyung Lee, Gunho Kim, Hyeoncheol |
| author_facet | Lee, Unggi Bae, Jiyeong Jung, Yeonji Kang, Minji Byun, Gyuri Lee, Yeonseo Kim, Dohee Lee, Sookbun Park, Jaekwon Ahn, Taekyung Lee, Gunho Kim, Hyeoncheol |
| contents | Knowledge Tracing (KT) is a critical component in online learning, but traditional approaches face limitations in interpretability and cross-domain adaptability. This paper introduces Language Model-based Code Knowledge Tracing (CodeLKT), an innovative application of Language model-based Knowledge Tracing (LKT) to programming education. CodeLKT leverages pre-trained language models to process learning data, demonstrating superior performance over existing KT and Code KT models. We explore Domain Adaptive Pre-Training (DAPT) and Task Adaptive Pre-Training (TAPT), showing enhanced performance in the coding domain and investigating cross-domain transfer between mathematics and coding. Additionally, we present an theoretically-informed integrated system combining CodeLKT with large language models to generate personalized, in-depth feedback to support students' programming learning. This work advances the field of Code Knowledge Tracing by expanding the knowledge base with language model-based approach and offering practical implications for programming education through data-informed feedback. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_00323 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | From Prediction to Application: Language Model-based Code Knowledge Tracing with Domain Adaptive Pre-Training and Automatic Feedback System with Pedagogical Prompting for Comprehensive Programming Education Lee, Unggi Bae, Jiyeong Jung, Yeonji Kang, Minji Byun, Gyuri Lee, Yeonseo Kim, Dohee Lee, Sookbun Park, Jaekwon Ahn, Taekyung Lee, Gunho Kim, Hyeoncheol Computation and Language Software Engineering Knowledge Tracing (KT) is a critical component in online learning, but traditional approaches face limitations in interpretability and cross-domain adaptability. This paper introduces Language Model-based Code Knowledge Tracing (CodeLKT), an innovative application of Language model-based Knowledge Tracing (LKT) to programming education. CodeLKT leverages pre-trained language models to process learning data, demonstrating superior performance over existing KT and Code KT models. We explore Domain Adaptive Pre-Training (DAPT) and Task Adaptive Pre-Training (TAPT), showing enhanced performance in the coding domain and investigating cross-domain transfer between mathematics and coding. Additionally, we present an theoretically-informed integrated system combining CodeLKT with large language models to generate personalized, in-depth feedback to support students' programming learning. This work advances the field of Code Knowledge Tracing by expanding the knowledge base with language model-based approach and offering practical implications for programming education through data-informed feedback. |
| title | From Prediction to Application: Language Model-based Code Knowledge Tracing with Domain Adaptive Pre-Training and Automatic Feedback System with Pedagogical Prompting for Comprehensive Programming Education |
| topic | Computation and Language Software Engineering |
| url | https://arxiv.org/abs/2409.00323 |