Language Model Can Do Knowledge Tracing: Simple but Effective Method to Integrate Language Model and Knowledge Tracing Task

• URL: http://arxiv.org/abs/2406.02893v2
• Date: Sun, 9 Jun 2024 10:53:29 GMT
• Title: Language Model Can Do Knowledge Tracing: Simple but Effective Method to Integrate Language Model and Knowledge Tracing Task
• Authors: Unggi Lee, Jiyeong Bae, Dohee Kim, Sookbun Lee, Jaekwon Park, Taekyung Ahn, Gunho Lee, Damji Stratton, Hyeoncheol Kim,
• Abstract summary: This paper proposes Language model-based Knowledge Tracing (LKT), a novel framework that integrates pre-trained language models (PLMs) with Knowledge Tracing methods. LKT effectively incorporates textual information and significantly outperforms previous KT models on large benchmark datasets.
• Score: 3.1459398432526267
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Knowledge Tracing (KT) is a critical task in online learning for modeling student knowledge over time. Despite the success of deep learning-based KT models, which rely on sequences of numbers as data, most existing approaches fail to leverage the rich semantic information in the text of questions and concepts. This paper proposes Language model-based Knowledge Tracing (LKT), a novel framework that integrates pre-trained language models (PLMs) with KT methods. By leveraging the power of language models to capture semantic representations, LKT effectively incorporates textual information and significantly outperforms previous KT models on large benchmark datasets. Moreover, we demonstrate that LKT can effectively address the cold-start problem in KT by leveraging the semantic knowledge captured by PLMs. Interpretability of LKT is enhanced compared to traditional KT models due to its use of text-rich data. We conducted the local interpretable model-agnostic explanation technique and analysis of attention scores to interpret the model performance further. Our work highlights the potential of integrating PLMs with KT and paves the way for future research in KT domain.

Related papers

• Beyond Right and Wrong: Mitigating Cold Start in Knowledge Tracing Using Large Language Model and Option Weight [0.14999444543328289]
  Knowledge Tracing (KT) is vital in educational data mining, enabling personalized learning. This study introduces the LOKT (Large Language Model Option-weighted Knowledge Tracing) model to address the cold start problem.
  arXiv  Detail & Related papers  (2024-10-14T16:25:48Z)
• SINKT: A Structure-Aware Inductive Knowledge Tracing Model with Large Language Model [64.92472567841105]
  Knowledge Tracing (KT) aims to determine whether students will respond correctly to the next question. Structure-aware Inductive Knowledge Tracing model with large language model (dubbed SINKT) SINKT predicts the student's response to the target question by interacting with the student's knowledge state and the question representation.
  arXiv  Detail & Related papers  (2024-07-01T12:44:52Z)
• CLST: Cold-Start Mitigation in Knowledge Tracing by Aligning a Generative Language Model as a Students' Knowledge Tracer [1.6713666776851528]
  We propose cold-start mitigation in knowledge tracing by aligning a generative language model as a students' knowledge tracer (T) We framed the KT task as a natural language processing task, wherein problem-solving data are expressed in natural language. We evaluated the performance of the CLST in situations of data scarcity using various baseline models for comparison.
  arXiv  Detail & Related papers  (2024-06-13T09:21:43Z)
• Improving Low-Resource Knowledge Tracing Tasks by Supervised Pre-training and Importance Mechanism Fine-tuning [25.566963415155325]
  We propose a low-resource KT framework called LoReKT to address above challenges. Inspired by the prevalent "pre-training and fine-tuning" paradigm, we aim to learn transferable parameters and representations from rich-resource KT datasets. We design an encoding mechanism to incorporate student interactions from multiple KT data sources.
  arXiv  Detail & Related papers  (2024-03-11T13:44:43Z)
• Cross-Lingual NER for Financial Transaction Data in Low-Resource Languages [70.25418443146435]
  We propose an efficient modeling framework for cross-lingual named entity recognition in semi-structured text data. We employ two independent datasets of SMSs in English and Arabic, each carrying semi-structured banking transaction information. With access to only 30 labeled samples, our model can generalize the recognition of merchants, amounts, and other fields from English to Arabic.
  arXiv  Detail & Related papers  (2023-07-16T00:45:42Z)
• Language models are weak learners [71.33837923104808]
  We show that prompt-based large language models can operate effectively as weak learners. We incorporate these models into a boosting approach, which can leverage the knowledge within the model to outperform traditional tree-based boosting. Results illustrate the potential for prompt-based LLMs to function not just as few-shot learners themselves, but as components of larger machine learning pipelines.
  arXiv  Detail & Related papers  (2023-06-25T02:39:19Z)
• Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning [104.58874584354787]
  In recent years, pre-trained large language models (LLMs) have demonstrated remarkable efficiency in achieving an inference-time few-shot learning capability known as in-context learning. This study aims to examine the in-context learning phenomenon through a Bayesian lens, viewing real-world LLMs as latent variable models.
  arXiv  Detail & Related papers  (2023-01-27T18:59:01Z)
• Large Language Models with Controllable Working Memory [64.71038763708161]
  Large language models (LLMs) have led to a series of breakthroughs in natural language processing (NLP) What further sets these models apart is the massive amounts of world knowledge they internalize during pretraining. How the model's world knowledge interacts with the factual information presented in the context remains under explored.
  arXiv  Detail & Related papers  (2022-11-09T18:58:29Z)
• pyKT: A Python Library to Benchmark Deep Learning based Knowledge Tracing Models [46.05383477261115]
  Knowledge tracing (KT) is the task of using students' historical learning interaction data to model their knowledge mastery over time. DLKT approaches are still left somewhat unknown and proper measurement and analysis of these approaches remain a challenge. We introduce a comprehensive python based benchmark platform, textscpyKT, to guarantee valid comparisons across DLKT methods.
  arXiv  Detail & Related papers  (2022-06-23T02:42:47Z)
• qDKT: Question-centric Deep Knowledge Tracing [29.431121650577396]
  We introduce qDKT, a variant of DKT that models every learner's success probability on individual questions over time. qDKT incorporates graph Laplacian regularization to smooth predictions under each skill. Experiments on several real-world datasets show that qDKT achieves state-of-art performance on predicting learner outcomes.
  arXiv  Detail & Related papers  (2020-05-25T23:43:55Z)
• Towards Interpretable Deep Learning Models for Knowledge Tracing [62.75876617721375]
  We propose to adopt the post-hoc method to tackle the interpretability issue for deep learning based knowledge tracing (DLKT) models. Specifically, we focus on applying the layer-wise relevance propagation (LRP) method to interpret RNN-based DLKT model. Experiment results show the feasibility using the LRP method for interpreting the DLKT model's predictions.
  arXiv  Detail & Related papers  (2020-05-13T04:03:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.