HGKT: Introducing Hierarchical Exercise Graph for Knowledge Tracing

• URL: http://arxiv.org/abs/2006.16915v6
• Date: Mon, 29 Aug 2022 14:59:42 GMT
• Title: HGKT: Introducing Hierarchical Exercise Graph for Knowledge Tracing
• Authors: Hanshuang Tong, Zhen Wang, Yun Zhou, Shiwei Tong, Wenyuan Han, Qi Liu
• Abstract summary: We propose a hierarchical graph knowledge tracing model called HGKT to explore the latent hierarchical relations between exercises. Specifically, we introduce the concept of problem schema to construct a hierarchical exercise graph that could model the exercise learning dependencies. In the testing stage, we present a K&S diagnosis matrix that could trace the transition of mastery of knowledge and problem schema.
• Score: 19.416373111152613
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Knowledge tracing (KT) which aims at predicting learner's knowledge mastery plays an important role in the computer-aided educational system. In recent years, many deep learning models have been applied to tackle the KT task, which have shown promising results. However, limitations still exist. Most existing methods simplify the exercising records as knowledge sequences, which fail to explore rich information that existed in exercises. Besides, the existing diagnosis results of knowledge tracing are not convincing enough since they neglect prior relations between exercises. To solve the above problems, we propose a hierarchical graph knowledge tracing model called HGKT to explore the latent hierarchical relations between exercises. Specifically, we introduce the concept of problem schema to construct a hierarchical exercise graph that could model the exercise learning dependencies. Moreover, we employ two attention mechanisms to highlight the important historical states of learners. In the testing stage, we present a K&S diagnosis matrix that could trace the transition of mastery of knowledge and problem schema, which can be more easily applied to different applications. Extensive experiments show the effectiveness and interpretability of our proposed models.

Related papers

• Temporal Graph Memory Networks For Knowledge Tracing [0.40964539027092906]
  We propose a novel method that jointly models the relational and temporal dynamics of the knowledge state using a deep temporal graph memory network. We also propose a generic technique for representing a student's forgetting behavior using temporal decay constraints on the graph memory module.
  arXiv  Detail & Related papers  (2024-09-23T07:47:02Z)
• 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)
• Leveraging Pedagogical Theories to Understand Student Learning Process with Graph-based Reasonable Knowledge Tracing [11.082908318943248]
  We introduce GRKT, a graph-based reasonable knowledge tracing method to address these issues. We propose a fine-grained and psychological three-stage modeling process as knowledge retrieval, memory strengthening, and knowledge learning/forgetting.
  arXiv  Detail & Related papers  (2024-06-07T10:14:30Z)
• Recognizing Unseen Objects via Multimodal Intensive Knowledge Graph Propagation [68.13453771001522]
  We propose a multimodal intensive ZSL framework that matches regions of images with corresponding semantic embeddings. We conduct extensive experiments and evaluate our model on large-scale real-world data.
  arXiv  Detail & Related papers  (2023-06-14T13:07:48Z)
• GIF: A General Graph Unlearning Strategy via Influence Function [63.52038638220563]
  Graph Influence Function (GIF) is a model-agnostic unlearning method that can efficiently and accurately estimate parameter changes in response to a $epsilon$-mass perturbation in deleted data. We conduct extensive experiments on four representative GNN models and three benchmark datasets to justify GIF's superiority in terms of unlearning efficacy, model utility, and unlearning efficiency.
  arXiv  Detail & Related papers  (2023-04-06T03:02:54Z)
• State of the Art and Potentialities of Graph-level Learning [54.68482109186052]
  Graph-level learning has been applied to many tasks including comparison, regression, classification, and more. Traditional approaches to learning a set of graphs rely on hand-crafted features, such as substructures. Deep learning has helped graph-level learning adapt to the growing scale of graphs by extracting features automatically and encoding graphs into low-dimensional representations.
  arXiv  Detail & Related papers  (2023-01-14T09:15:49Z)
• DAGKT: Difficulty and Attempts Boosted Graph-based Knowledge Tracing [11.147659368393263]
  We propose a difficulty and attempts boosted graph-based knowledge tracing (DAGKT) using rich information from students' records. In this paper, a novel method is designed to establish the question similarity relationship inspired by the F1 score.
  arXiv  Detail & Related papers  (2022-10-18T14:39:50Z)
• Deep Graph Memory Networks for Forgetting-Robust Knowledge Tracing [5.648636668261282]
  We propose a novel knowledge tracing model, namely emphDeep Graph Memory Network (DGMN) In this model, we incorporate a forget gating mechanism into an attention memory structure in order to capture forgetting behaviours. This model has the capability of learning relationships between latent concepts from a dynamic latent concept graph.
  arXiv  Detail & Related papers  (2021-08-18T12:04:10Z)
• Model-Agnostic Graph Regularization for Few-Shot Learning [60.64531995451357]
  We present a comprehensive study on graph embedded few-shot learning. We introduce a graph regularization approach that allows a deeper understanding of the impact of incorporating graph information between labels. Our approach improves the performance of strong base learners by up to 2% on Mini-ImageNet and 6.7% on ImageNet-FS.
  arXiv  Detail & Related papers  (2021-02-14T05:28:13Z)
• GIKT: A Graph-based Interaction Model for Knowledge Tracing [36.07642261246016]
  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. Experiments on three datasets demonstrate that GIKT achieves the new state-of-the-art performance, with at least 1% absolute AUC improvement.
  arXiv  Detail & Related papers  (2020-09-13T12:50:32Z)
• All About Knowledge Graphs for Actions [82.39684757372075]
  We propose a better understanding of knowledge graphs (KGs) that can be utilized for zero-shot and few-shot action recognition. We study three different construction mechanisms for KGs: action embeddings, action-object embeddings, visual embeddings. We present extensive analysis of the impact of different KGs on different experimental setups.
  arXiv  Detail & Related papers  (2020-08-28T01:44:01Z)

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.