Publications
2024

1. Item-Difficulty-Aware Learning Path Recommendation: From a Real Walking Perspective

   ACM SIGKDD Conference on Knowledge Discovery and Data Mining 2024 (KDD24 Oral)
   Zhang H, Shen S, Xu B, et al. Item-Difficulty-Aware Learning Path Recommendation: From a Real Walking Perspective[C]//Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2024: 4167-4178.
   

   Abstract

   Learning path recommendation aims to provide learners with a reasonable order of items to achieve their learning goals. Intuitively, the learning process on the learning path can be metaphorically likened to walking. Despite extensive efforts in this area, most previous methods mainly focus on the relationship among items but overlook the difficulty of items, which may raise two issues from a real walking perspective: (1) The path may be rough: When learners tread the path without considering item difficulty, it's akin to walking a dark, uneven road, making learning harder and dampening interest. (2) The path may be inefficient: Allowing learners only a few attempts on very challenging items before switching, or persisting with a difficult item despite numerous attempts without mastery, can result in inefficiencies in the learning journey. To conquer the above limitations, we propose a novel method named Difficulty-constrained Learning Path Recommendation (DLPR), which is aware of item difficulty. Specifically, we first explicitly categorize items into learning items and practice items, then construct a hierarchical graph to model and leverage item difficulty adequately. Then we design a Difficulty-driven Hierarchical Reinforcement Learning (DHRL) framework to facilitate learning paths with efficiency and smoothness. Finally, extensive experiments on three different simulators demonstrate our framework achieves state-of-the-art performance.

   
   
2. Graph-based Student Knowledge Profile for Online Intelligent Education

   SIAM International Conference on Data Mining 2024 (SDM24 Oral)
   Wu J, Zhang H, Huang Z, et al. Graph-based Student Knowledge Profile for Online Intelligent Education[C]//Proceedings of the 2024 SIAM International Conference on Data Mining (SDM). Society for Industrial and Applied Mathematics, 2024: 379-387.
   

   Abstract

   Student knowledge profile is the basis for adaptive learning applications in online learning resulting from modeling the student mastery of knowledge concepts. In recent years, typical works based on knowledge tracing (KT) expect to profile students and have achieved significant success for the next performance prediction. However, in practical online learning scenarios, current methods tend to suffer from the following challenges: 1) Prediction inconsistency: The accuracy of the next performance prediction is inconsistent with the accuracy of student knowledge profile prediction, which is the more required result. 2) Cold start of knowledge: In online learning scenarios, it is often necessary to profile some knowledge concepts without learning records in advance. In this paper, we propose a novel Graph-based Student Knowledge Profile Model (GSKPM), along with a new end-to-end training objective, to tackle these challenges. We first define a new training objective to ensure the model is capable of inferring consistent student knowledge profiles. Then in this model, a two-stage hyper-aggregation process is employed to make full use of the topological relations between knowledge concepts and knowledge domains to provide information during profiling, especially for cold start knowledge concepts. Finally, through extensive experiments on real-world datasets, we will show that GSKPM achieves better prediction performances on student knowledge profiles and well deals with the cold start problem.

   
   
3. FDKT: Towards an interpretable deep knowledge tracing via fuzzy reasoning

   ACM Transactions on Information Systems (TOIS)
   Liu F, Bu C, Zhang H, et al. FDKT: Towards an interpretable deep knowledge tracing via fuzzy reasoning[J]. ACM Transactions on Information Systems, 2024.
   

   Abstract

   In educational data mining, knowledge tracing (KT) aims to model learning performance based on student knowledge mastery. Deep-learning-based KT models perform remarkably better than traditional KT and have attracted considerable attention. However, most of them lack interpretability, making it challenging to explain why the model performed well in the prediction. In this paper, we propose an interpretable deep KT model, referred to as fuzzy deep knowledge tracing (FDKT) via fuzzy reasoning. Specifically, we formalize continuous scores into several fuzzy scores using the fuzzification module. Then, we input the fuzzy scores into the fuzzy reasoning module (FRM). FRM is designed to deduce the current cognitive ability, based on which the future performance was predicted. FDKT greatly enhanced the intrinsic interpretability of deep-learning-based KT through the interpretation of the deduction of student cognition. Furthermore, it broadened the application of KT to continuous scores. Improved performance with regard to both the advantages of FDKT was demonstrated through comparisons with the state-of-the-art models.

   
   

2023

1. 可信的端到端深度学生知识画像建模方法

   计算机研究与发展 2023 (CCFAI23 Oral, Excellent Paper Award)
   王士进, 吴金泽, 张浩天, 沙晶, 黄振亚, 刘淇. 可信的端到端深度学生知识画像建模方法[J]. 计算机研究与发展, 2023, 60(8): 1822-1833. doi: 10.7544/issn1000-1239.202330229
   

   摘要

   学生知识画像是对学生在不同知识概念掌握程度的全面精准的表示. 通常，智能教育系统中使用知识追踪方法，基于显式的学生交互数据，对学生在某些知识概念的隐式掌握程度进行建模. 然而知识追踪方法的预测结果与学生知识画像存在着时序、预测粒度不一致的情况，导致其产生的学生知识画像不可信. 对此，首先基于端到端的学生知识掌握度预测目标定义并形式化学生知识画像预测任务，然后提出了一种深度知识画像（deep knowledge portrait, DKP）模型. 该方法首先在知识粒度上学习交互表征，引入了知识难度、知识概念等特征在知识粒度上区分交互；然后，采用双向长短时记忆网络基于学生历史交互序列，建模学生知识状态变化. 最后针对待预测知识概念，使用了多头注意力池化层强化历史序列中的相关交互以进行该概念下的学生掌握度预测. 在3个真实的数据集上的实验结果表明，所提出的方法更适合学生知识画像预测任务从而获得更可信的学生知识画像，并在各项性能上超过了现有的方法.

   
   

2022

1. APGKT: Exploiting Associative Path on Skills Graph for Knowledge Tracing

   Pacific Rim International Conference on Artificial Intelligence (PRICAI 2022 Oral)
   Zhang, H., Bu, C., Liu, F., Liu, S., Zhang, Y., Hu, X. (2022). APGKT: Exploiting Associative Path on Skills Graph for Knowledge Tracing. In: Khanna, S., Cao, J., Bai, Q., Xu, G. (eds) PRICAI 2022: Trends in Artificial Intelligence. PRICAI 2022. Lecture Notes in Computer Science, vol 13629. Springer, Cham. https://doi.org/10.1007/978-3-031-20862-1_26
   

   Abstract

   Knowledge tracing (KT) is a fundamental task in educational data mining that mainly focuses on students’ dynamic cognitive states of skills. The question-answering process of students can be regarded as a thinking process that considers the following two problems. One problem is which skills are needed to answer the question, and the other is how to use these skills in order. If a student wants to answer a question correctly, the student should not only master the set of skills involved in the question, but also think and obtain the associative path on the skills graph. The nodes in the associative path refer to the skills needed and the path shows the order of using them. The associative path is referred to as the skill mode. Thus, obtaining the skill modes is the key to answering questions successfully. However, most existing KT models only focus on a set of skills, without considering the skill modes. We propose a KT model, called APGKT, that exploits skill modes. Specifically, we extract the subgraph topology of the skills involved in the question and combine the difficulty level of the skills to obtain the skill modes via encoding; then, through multi-layer recurrent neural networks, we obtain a student’s higher-order cognitive states of skills, which is used to predict the student’s future answering performance. Experiments on five benchmark datasets validate the effectiveness of the proposed model.

   
   

Patents

1. 多重逻辑问题的认知数据处理方法及装置 , CN113344204B, 卜晨阳, 张浩天, 刘菲, 李磊, 胡学钢, 2022.11, 已授权.
2. 一种融合知识联想路径的认知跟踪方法, CN114861916B, 卜晨阳, 张浩天, 刘朔辰, 刘菲, 胡学钢, 2022.08, 已授权.
3. 知识追踪方法、装置、非易失性存储介质及电子装置, CN113919979A, 刘菲, 卜晨阳, 张浩天, 胡学钢, 2022.01, 已公开.