GIKT: A Graph-Based Interaction Model for Knowledge Tracing
| Autor: | Jian Shen, Yong Yu, Yanru Qu, Weinan Zhang, Yang Yang, Yaoming Zhu, Yunfei Liu, Kerong Wang |
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| Rok vydání: | 2021 |
| Předmět: |
Dependency (UML)
Theoretical computer science Degree (graph theory) Computer science Perspective (graphical) Interaction model 02 engineering and technology Tracing 020204 information systems ComputingMilieux_COMPUTERSANDEDUCATION 0202 electrical engineering electronic engineering information engineering Embedding Graph (abstract data type) 020201 artificial intelligence & image processing State (computer science) |
| Zdroj: | Machine Learning and Knowledge Discovery in Databases ISBN: 9783030676575 ECML/PKDD (1) |
| DOI: | 10.1007/978-3-030-67658-2_18 |
| Popis: | With the rapid development in online education, knowledge tracing (KT) has become a fundamental problem which traces students’ knowledge status and predicts their performance on new questions. Questions are often numerous in online education systems, and are always associated with much fewer skills. However, the previous literature fails to involve question information together with high-order question-skill correlations, which is mostly limited by data sparsity and multi-skill problems. From the model perspective, previous models can hardly capture the long-term dependency of student exercise history, and cannot model the interactions between student-questions, and student-skills in a consistent way. In this paper, we propose a Graph-based Interaction model for Knowledge Tracing (GIKT) to tackle the above problems. More specifically, GIKT utilizes graph convolutional network (GCN) to substantially incorporate question-skill correlations via embedding propagation. Besides, considering that relevant questions are usually scattered throughout the exercise history, and that question and skill are just different instantiations of knowledge, GIKT generalizes the degree of students’ master of the question to the interactions between the student’s current state, the student’s history states, the target question, and related skills. Experiments on three datasets demonstrate that GIKT achieves the new state-of-the-art performance, with 2%–6% absolute AUC improvement. |
| Databáze: | OpenAIRE |
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