GIKT: A Graph-based Interaction Model for Knowledge Tracing

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 probems. 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 related exercises, the target question, and related skills. Experiments on three datasets demonstrate that GIKT achieves the new state-of-the-art performance, with at least 1% absolute AUC improvement.

[1]  Hisashi Kashima,et al.  Knowledge Tracing Machines: Factorization Machines for Knowledge Tracing , 2018, AAAI.

[2]  Yixin Cao,et al.  KGAT: Knowledge Graph Attention Network for Recommendation , 2019, KDD.

[3]  Yuan Luo,et al.  Graph Convolutional Networks for Text Classification , 2018, AAAI.

[4]  Penghe Chen,et al.  Prerequisite-Driven Deep Knowledge Tracing , 2018, 2018 IEEE International Conference on Data Mining (ICDM).

[5]  Vincent Aleven,et al.  More Accurate Student Modeling through Contextual Estimation of Slip and Guess Probabilities in Bayesian Knowledge Tracing , 2008, Intelligent Tutoring Systems.

[6]  Kenneth R. Koedinger,et al.  Individualized Bayesian Knowledge Tracing Models , 2013, AIED.

[7]  Enhong Chen,et al.  Exercise-Enhanced Sequential Modeling for Student Performance Prediction , 2018, AAAI.

[8]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[9]  Tomoko Ohkuma,et al.  Augmenting Knowledge Tracing by Considering Forgetting Behavior , 2019, WWW.

[10]  Leonidas J. Guibas,et al.  Deep Knowledge Tracing , 2015, NIPS.

[11]  Byungsoo Kim,et al.  EdNet: A Large-Scale Hierarchical Dataset in Education , 2020, AIED.

[12]  Zachary A. Pardos,et al.  Modeling Individualization in a Bayesian Networks Implementation of Knowledge Tracing , 2010, UMAP.

[13]  Yutaka Matsuo,et al.  Graph-based Knowledge Tracing: Modeling Student Proficiency Using Graph Neural Network , 2019, 2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI).

[14]  John R. Anderson,et al.  Knowledge tracing: Modeling the acquisition of procedural knowledge , 2005, User Modeling and User-Adapted Interaction.

[15]  Max Welling,et al.  Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.

[16]  Ronald J. Williams,et al.  A Learning Algorithm for Continually Running Fully Recurrent Neural Networks , 1989, Neural Computation.

[17]  Weinan Zhang,et al.  An end-to-end neighborhood-based interaction model for knowledge-enhanced recommendation , 2019, Proceedings of the 1st International Workshop on Deep Learning Practice for High-Dimensional Sparse Data.

[18]  Joan Bruna,et al.  Few-Shot Learning with Graph Neural Networks , 2017, ICLR.

[19]  Kenneth R. Koedinger,et al.  Performance Factors Analysis - A New Alternative to Knowledge Tracing , 2009, AIED.

[20]  Jing Xiao,et al.  Dynamic Student Classiffication on Memory Networks for Knowledge Tracing , 2019, PAKDD.

[21]  Pietro Liò,et al.  Graph Attention Networks , 2017, ICLR.

[22]  Joseph E. Beck,et al.  Going Deeper with Deep Knowledge Tracing , 2016, EDM.

[23]  Zachary A. Pardos,et al.  KT-IDEM: introducing item difficulty to the knowledge tracing model , 2011, UMAP'11.

[24]  Xavier Bresson,et al.  Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering , 2016, NIPS.

[25]  Fenglong Ma,et al.  Deep Hierarchical Knowledge Tracing , 2019, EDM.

[26]  Yi Yu,et al.  Deep Knowledge Tracing and Dynamic Student Classification for Knowledge Tracing , 2018, 2018 IEEE International Conference on Data Mining (ICDM).

[27]  Dit-Yan Yeung,et al.  Dynamic Key-Value Memory Networks for Knowledge Tracing , 2016, WWW.

[28]  Sanja Fidler,et al.  3D Graph Neural Networks for RGBD Semantic Segmentation , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[29]  Gholamreza Haffari,et al.  Graph-to-Sequence Learning using Gated Graph Neural Networks , 2018, ACL.

[30]  Hermann Ebbinghaus (1885).  Memory: A Contribution to Experimental Psychology , 2013, Annals of Neurosciences.

[31]  Steffen Rendle,et al.  Factorization Machines , 2010, 2010 IEEE International Conference on Data Mining.

[32]  Jure Leskovec,et al.  Inductive Representation Learning on Large Graphs , 2017, NIPS.

[33]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[34]  Qing Wang,et al.  Knowledge Tracing with Sequential Key-Value Memory Networks , 2019, SIGIR.