Graph-based Knowledge Tracing: Modeling Student Proficiency Using Graph Neural Network

Nakagawa, Hidemoto; Iwasawa, Yusuke; Matsuo, Yutaka

doi:10.1145/3350546.3352513

Cited by 203 publications

(113 citation statements)

References 5 publications

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“…Moreover, researchers were inspired by social recommendation systems and used the SocialMF technique to improve the prediction accuracy [27]. Furthermore, Nakagawa et al proposed GKT that viewed knowledge concepts and their dependencies as nodes and links in a graph, so that students' knowledge states on the answered concepts and their related concepts can be both updated over time [19]. Note that, students and exercises naturally form an interaction graph in ITSs.…”

Section: Collaborative Filtering In Itsmentioning

confidence: 99%

EKT: Exercise-Aware Knowledge Tracing for Student Performance Prediction

Liu

Huang

Yin

et al. 2021

IEEE Trans. Knowl. Data Eng.

303

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For offering proactive services (e.g., personalized exercise recommendation) to the students in computer supported intelligent education, one of the fundamental tasks is predicting student performance (e.g., scores) on future exercises, where it is necessary to track the change of each student's knowledge acquisition during her exercising activities. Unfortunately, to the best of our knowledge, existing approaches can only exploit the exercising records of students, and the problem of extracting rich information existed in the materials (e.g., knowledge concepts, exercise content) of exercises to achieve both more precise prediction of student performance and more interpretable analysis of knowledge acquisition remains underexplored. To this end, in this paper, we present a holistic study of student performance prediction. To directly achieve the primary goal of performance prediction, we first propose a general Exercise-Enhanced Recurrent Neural Network (EERNN) framework by exploring both student's exercising records and the text content of corresponding exercises. In EERNN, we simply summarize each student's state into an integrated vector and trace it with a recurrent neural network, where we design a bidirectional LSTM to learn the encoding of each exercise from its content. For making final predictions, we design two implementations on the basis of EERNN with different prediction strategies, i.e., EERNNM with Markov property and EERNNA with Attention mechanism. Then, to explicitly track student's knowledge acquisition on multiple knowledge concepts, we extend EERNN to an explainable Exercise-aware K nowledge T racing (EKT) framework by incorporating the knowledge concept information, where the student's integrated state vector is now extended to a knowledge state matrix. In EKT, we further develop a memory network for quantifying how much each exercise can affect the mastery of students on multiple knowledge concepts during the exercising process. Finally, we conduct extensive experiments and evaluate both EERNN and EKT frameworks on a large-scale real-world data. The results in both general and cold-start scenarios clearly demonstrate the effectiveness of two frameworks in student performance prediction as well as the superior interpretability of EKT.

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Section: Collaborative Filtering In Itsmentioning

confidence: 99%

EKT: Exercise-Aware Knowledge Tracing for Student Performance Prediction

Liu

Huang

Yin

et al. 2021

IEEE Trans. Knowl. Data Eng.

303

View full text Add to dashboard Cite

show abstract

“…Moreover, AKT uses the Rasch model based exercise and exercise-response embeddings to avoid overparameterization and overfitting. Recently, several works [13,23] attempt to incorporate graph structure to the knowledge tracing model. [13] formulate knowledge tracing as a time series node-level classification task in graph structure and proposes GKT which extracts representation of each knowledge concept by aggregating representations of neighboring concepts.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, several works [13,23] attempt to incorporate graph structure to the knowledge tracing model. [13] formulate knowledge tracing as a time series node-level classification task in graph structure and proposes GKT which extracts representation of each knowledge concept by aggregating representations of neighboring concepts. HGKT [23] applies graph neural network to get hierarchical exercise graph which better represent groups of similar exercises.…”

Section: Related Workmentioning

confidence: 99%

SAINT+: Integrating Temporal Features for EdNet Correctness Prediction

Shin¹,

Shim²,

Yu³

et al. 2021

LAK21: 11th International Learning Analytics and Knowledge Conference

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We propose SAINT+, a successor of SAINT which is a Transformer based knowledge tracing model that separately processes exercise information and student response information. Following the architecture of SAINT, SAINT+ has an encoder-decoder structure where the encoder applies self-attention layers to a stream of exercise embeddings, and the decoder alternately applies self-attention layers and encoder-decoder attention layers to streams of response embeddings and encoder output. Moreover, SAINT+ incorporates two temporal feature embeddings into the response embeddings: elapsed time, the time taken for a student to answer, and lag time, the time interval between adjacent learning activities. We empirically evaluate the effectiveness of SAINT+ on EdNet, the largest publicly available benchmark dataset in the education domain. Experimental results show that SAINT+ achieves state-of-the-art performance in knowledge tracing with an improvement of 1.25% in area under receiver operating characteristic curve compared to SAINT, the current state-of-the-art model in EdNet dataset. CCS CONCEPTS• Computing methodologies → Artificial intelligence; Neural networks; • Applied computing → Interactive learning environments; • Social and professional topics → Student assessment.

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“…Currently, many scholars have introduced graph neural networks in knowledge modeling. For instance, Nakagawa et al [24] uses a graph convolutional network to represent the correlation between knowledge, and updates the latent knowledge state by a weak-forgetting and weak-learning gate mechanism, thereby finally achieving good performance. Although both interpretability and performance are good, these models consider forgetting or learning factors just by using the erase gate and add gate, whose effectiveness remains weak, ignoring the time interval information.…”

Section: Diagnosis With Memory Networkmentioning

confidence: 99%

A Dynamic Knowledge Diagnosis Approach Integrating Cognitive Features

Huang

Yang

et al. 2021

IEEE Access

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The rapid development of data analytic technologies has advanced personalized learning and increased its popularity in K-12 education significantly. Specifically, one fundamental step in personalized learning is knowledge proficiency diagnosis which reveals blind spots in students' knowledge. However, existing approaches to diagnosis either exploit data from a one-time assessment for the cognitive diagnosis (ignoring the previous historical interactions) or trace the knowledge state using recurrent neural networks to predict students' future performance (ignoring the cognitive features). To this end, this study proposes a dynamic approach to knowledge diagnosis integrating cognitive features with a key-value memory network to store latent exercise information and capture long-term temporal features based on cognitive psychology. Specifically, given the characteristics of assessment data in China, our approach mainly aims to model sequence data with cognitive features, including forgetting and learning. Two corresponding gates are used to weaken the knowledge memory and strengthen the repeated knowledge memory over time, respectively, in the memory updating process. Finally, to evaluate our approach, we conducted extensive experiments on four real-world datasets collected from K-12 education. The results show that the approach can effectively process the time sequence in education, whose prediction results are better and more stable than other existing baseline models. We also conducted experiments for parameter sensitivity, different feature integration methods, and the effectiveness of cognitive features to ensure that the models achieved the best results. The application visualization further confirms the practicability of our approach in dealing with problems of dynamic knowledge diagnosis.INDEX TERMS Cognitive features, dynamic knowledge diagnosis, key-value memory, performance prediction.

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Graph-based Knowledge Tracing: Modeling Student Proficiency Using Graph Neural Network

Cited by 203 publications

References 5 publications

EKT: Exercise-Aware Knowledge Tracing for Student Performance Prediction

EKT: Exercise-Aware Knowledge Tracing for Student Performance Prediction

SAINT+: Integrating Temporal Features for EdNet Correctness Prediction

A Dynamic Knowledge Diagnosis Approach Integrating Cognitive Features

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