Knowledge tracing is a fundamental task in the computer-aid educational system. In this paper, we propose a hierarchical exercise feature enhanced knowledge tracing framework, which could enhance the ability of knowledge tracing by incorporating knowledge distribution, semantic features, and difficulty features from exercise text. Extensive experiments show the high performance of our framework.
Microsoft Corporation（微软公司）；AIXUEXI Education Group Ltd（爱学习教育集团有限公司）；University of Science and Technology of China（中国科学技术大学）
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 …
Riiid! AI Research（Riiid! 人工智能研究院）；Yale University（耶鲁大学）；University of Michigan（密歇根大学）；UC Berkeley（加州大学伯克利分校）
Knowledge tracing, the act of modeling a student's knowledge through learning activities, is an extensively studied problem in the field of computer-aided education. Although models with attention mechanism have outperformed traditional approaches such as Bayesian knowledge tracing and collaborative filtering, they share two limitations. Firstly, the models rely on shallow attention layers and fail to capture complex relations among exercises and responses over time. Secondly, different combinations of queries, keys and values for the self-attention layer for knowledge tracing were not extensi…
Shandong University of Finance and Economics（山东财经大学）；Shandong Jianzhu University（山东建筑大学）；Shandong University（山东大学）；Hong Kong Polytechnic University（香港理工大学）；Education University of Hong Kong（香港教育大学）
Knowledge tracing aims to trace students' evolving knowledge states by predicting their future performance on concept-related exercises. Recently, some graph-based models have been developed to incorporate the relationships between exercises to improve knowledge tracing, but only a single type of relationship information is generally explored. In this paper, we present a novel Dual Graph Ensemble learning method for Knowledge Tracing (DGEKT), which establishes a dual graph structure of students' learning interactions to capture the heterogeneous exercise-concept associations and interaction tr…
University of Science and Technology of China（中国科学技术大学）；Rutgers, the State University of New Jersey（罗格斯，新泽西州立大学）；IFLYTEK Co., Ltd（科大讯飞股份有限公司）
For offering proactive services to students in intelligent education, one of the fundamental tasks is predicting their performance (e.g., scores) on future exercises, where it is necessary to track each student's knowledge acquisition during her exercising activities. However, existing approaches can only exploit the exercising records of students, and the problem of extracting rich information existed in the exercise's materials (e.g., knowledge concepts, exercise content) to achieve both precise predictions of student performance and interpretable analysis of knowledge acquisition remains un…
Deakin University（迪肯大学）；Xidian University（西安电子科技大学）；China University of Geosciences（中国地质大学）；The University of Western Australia（西澳大学）
The goal of Knowledge Tracing (KT) is to estimate how well students have mastered a concept based on their historical learning of related exercises. The benefit of knowledge tracing is that students' learning plans can be better organised and adjusted, and interventions can be made when necessary. With the recent rise of deep learning, Deep Knowledge Tracing (DKT) has utilised Recurrent Neural Networks (RNNs) to accomplish this task with some success. Other works have attempted to introduce Graph Neural Networks (GNNs) and redefine the task accordingly to achieve significant improvements. Howe…
Australian National University（澳大利亚国立大学）；University of New South Wales（新南威尔士大学）
Online education has gained an increasing importance over the last decade for providing affordable high-quality education to students worldwide. This has been further magnified during the global pandemic as more students switched to study online. The majority of online education tasks, e.g., course recommendation, exercise recommendation, or automated evaluation, depends on tracking students' knowledge progress. This is known as the \emph{Knowledge Tracing} problem in the literature. Addressing this problem requires collecting student evaluation data that can reflect their knowledge evolution …
KT4EQG: Personalized Exercise Question Generation via Knowledge Tracing
University of California, Santa Barbara（加州大学圣巴巴拉分校）；University of South Alabama（南方大学）；University of Michigan（密歇根大学）；MIT-IBM Watson AI Lab（麻省理工-IBM沃森人工智能实验室）；Harvard University（哈佛大学）
AAKT: Enhancing Knowledge Tracing with Alternate Autoregressive Modeling
School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）；School of Information Technology & Management, University of International Business and Economics（对外经济贸易大学信息技术与管理学院）
Heilongjiang Institute of Technology（黑龙江工程学院）；Central China Normal University（华中师范大学）
Knowledge tracing (KT) serves as a primary part of intelligent education systems. Most current KTs either rely on expert judgments or only exploit a single network structure, which affects the full expression of learning features. To adequately mine features of students' learning process, Deep Knowledge Tracing Based on Spatial and Temporal Deep Representation Learning for Learning Performance Prediction (DKT-STDRL) is proposed in this paper. DKT-STDRL extracts spatial features from students' learning history sequence, and then further extracts temporal features to extract deeper hidden inform…
BamaER: A Behavior-Aware Memory-Augmented Model for Exercise Recommendation
Guilin University of Electronic Technology（桂林电子科技大学）；Jinan University（吉林大学）；Beijing University（北京大学）；Xiamen university（厦门大学）；Nanjing University of Science and Technology（南京理工大学）
Transformer based knowledge tracing model is an extensively studied problem in the field of computer-aided education. By integrating temporal features into the encoder-decoder structure, transformers can processes the exercise information and student response information in a natural way. However, current state-of-the-art transformer-based variants still share two limitations. First, extremely long temporal features cannot well handled as the complexity of self-attention mechanism is O(n2). Second, existing approaches track the knowledge drifts under fixed a window size, without considering di…
