KT4EQG: Personalized Exercise Question Generation via Knowledge Tracing

• URL: http://arxiv.org/abs/2605.23933v2
• Date: Wed, 27 May 2026 11:48:40 GMT
• Title: KT4EQG: Personalized Exercise Question Generation via Knowledge Tracing
• Abstract summary: Educational Question Generation (EQG) aims to synthesize customized exercise questions that enhance student learning.<n>An effective EQG system should ideally personalize questions for each student by modeling the student's knowledge state and generating questions that provide the greatest learning benefit.<n>We propose KT4EQG, a personalized EQG framework that generates effective questions for individual students under the guidance of a KT model.
• Score: 49.527882446935884
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Educational Question Generation (EQG) aims to synthesize customized exercise questions that enhance student learning. An effective EQG system should ideally personalize questions for each student by modeling the student's knowledge state and generating questions that provide the greatest learning benefit. However, few existing EQG approaches are able to achieve such fine-grained personalization. In this paper, we explore how EQG can benefit from knowledge tracing (KT), which models students' knowledge states based on historical performance and predicts future performance. We propose KT4EQG, a personalized EQG framework that generates effective questions for individual students under the guidance of a KT model. Specifically, KT4EQG seeks to maximize a student's potential improvement in overall knowledge mastery by leveraging the KT model to select the most suitable knowledge concept for the student to practice. An LLM-based question generator is then trained to produce a question faithfully grounded in the selected concept. Experimental results on XES3G5M and MOOCRadar show that KT4EQG consistently generates more effective questions than methods with limited or no personalization.

Related papers

• Automatic Question & Answer Generation Using Generative Large Language Model (LLM) [0.0]
  This research proposes to leverage unsupervised learning methods in NLP, primarily focusing on the English language.<n>A customized model will offer efficient solutions for educators, instructors, and individuals engaged in text-based evaluations.
  arXiv  Detail & Related papers  (2025-08-26T23:36:13Z)
• TutorLLM: Customizing Learning Recommendations with Knowledge Tracing and Retrieval-Augmented Generation [44.18659233932457]
  TutorLLM is a personalized learning recommender system based on Knowledge Tracing (KT) and Retrieval-Augmented Generation (RAG)<n>The novelty of TutorLLM lies in its unique combination of KT and RAG techniques with LLMs, which enables dynamic retrieval of context-specific knowledge.<n>The evaluation includes user assessment questionnaires and performance metrics, demonstrating a 10% improvement in user satisfaction.
  arXiv  Detail & Related papers  (2025-01-20T21:18:43Z)
• KBAlign: Efficient Self Adaptation on Specific Knowledge Bases [73.34893326181046]
  We present KBAlign, a self-supervised framework that enhances RAG systems through efficient model adaptation.<n>Our key insight is to leverage the model's intrinsic capabilities for knowledge alignment through two innovative mechanisms.<n> Experiments demonstrate that KBAlign can achieve 90% of the performance gain obtained through GPT-4-supervised adaptation.
  arXiv  Detail & Related papers  (2024-11-22T08:21:03Z)
• Automated Knowledge Concept Annotation and Question Representation Learning for Knowledge Tracing [59.480951050911436]
  We present KCQRL, a framework for automated knowledge concept annotation and question representation learning.<n>We demonstrate the effectiveness of KCQRL across 15 KT algorithms on two large real-world Math learning datasets.
  arXiv  Detail & Related papers  (2024-10-02T16:37:19Z)
• 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)
• Student Answer Forecasting: Transformer-Driven Answer Choice Prediction for Language Learning [2.8887520199545187]
  Recent research has primarily focused on the correctness of the answer rather than the student's performance on specific answer choices. We present MCQStudentBert, an answer forecasting model that integrates contextual understanding of students' answering history along with the text of the questions and answers. This work opens the door to more personalized content, modularization, and granular support.
  arXiv  Detail & Related papers  (2024-05-30T14:09:43Z)
• A Knowledge-Injected Curriculum Pretraining Framework for Question Answering [70.13026036388794]
  We propose a general Knowledge-Injected Curriculum Pretraining framework (KICP) to achieve comprehensive KG learning and exploitation for Knowledge-based question answering tasks. The KI module first injects knowledge into the LM by generating KG-centered pretraining corpus, and generalizes the process into three key steps. The KA module learns knowledge from the generated corpus with LM equipped with an adapter as well as keeps its original natural language understanding ability. The CR module follows human reasoning patterns to construct three corpora with increasing difficulties of reasoning, and further trains the LM from easy to hard in a curriculum manner.
  arXiv  Detail & Related papers  (2024-03-11T03:42:03Z)
• UKP-SQuARE: An Interactive Tool for Teaching Question Answering [61.93372227117229]
  The exponential growth of question answering (QA) has made it an indispensable topic in any Natural Language Processing (NLP) course. We introduce UKP-SQuARE as a platform for QA education. Students can run, compare, and analyze various QA models from different perspectives.
  arXiv  Detail & Related papers  (2023-05-31T11:29:04Z)
• Attentive Q-Matrix Learning for Knowledge Tracing [4.863310073296471]
  We propose Q-matrix-based Attentive Knowledge Tracing (QAKT) as an end-to-end style model. QAKT is capable of modeling problems hierarchically and learning the q-matrix efficiently based on students' sequences. Results of further experiments suggest that the q-matrix learned by QAKT is highly model-agnostic and more information-sufficient than the one labeled by human experts.
  arXiv  Detail & Related papers  (2023-04-06T12:31:34Z)
• Prophet: Prompting Large Language Models with Complementary Answer Heuristics for Knowledge-based Visual Question Answering [28.763437313766996]
  Knowledge-based visual question answering (VQA) requires external knowledge beyond the image to answer the question.<n>Recent works have resorted to using a powerful large language model (LLM) as an implicit knowledge engine to acquire the necessary knowledge for answering.<n>We present Prophet -- a conceptually simple, flexible, and general framework designed to prompt with answers for knowledge-based VQA.
  arXiv  Detail & Related papers  (2023-03-03T13:05:15Z)
• Differentiating Student Feedbacks for Knowledge Tracing [28.669001606806525]
  We propose a framework to reweight the contribution of different responses based on their discrimination in training.<n>We also introduce an adaptive predictive score fusion technique to maintain accuracy on less discriminative responses.
  arXiv  Detail & Related papers  (2022-12-16T13:55:07Z)
• Knowledge-driven Data Construction for Zero-shot Evaluation in Commonsense Question Answering [80.60605604261416]
  We propose a novel neuro-symbolic framework for zero-shot question answering across commonsense tasks. We vary the set of language models, training regimes, knowledge sources, and data generation strategies, and measure their impact across tasks. We show that, while an individual knowledge graph is better suited for specific tasks, a global knowledge graph brings consistent gains across different tasks.
  arXiv  Detail & Related papers  (2020-11-07T22:52:21Z)

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.