EKT: Exercise-Aware Knowledge Tracing for Student Performance Prediction

By Qi Liu, Zhenya Huang, Yu Yin, Enhong Chen, Hui Xiong, Yu-Ho Su, Guoping Hu. IEEE Transactions on Knowledge and Data Engineering, 2019

500 citations recorded by the source corpus.

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 Knowledge Tracing (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.

Subfields: Artificial Intelligence, Computer Networks and Communications, Computer Science Applications

View Paper: https://www.semanticscholar.org/paper/fc6f4ff4360e6062bd7ae5b6982ad72120151779

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xiangnan He
2. Xiang Wang
3. Meng Wang
4. Le Wu
5. Kun Zhang
6. Tat-seng Chua
7. Bernardo Pereira Nunes
8. Qing Wang

In Computer Networks and Communications

1. Xiang Wang
2. Meng Wang
3. Dongwon Lee
4. Le Wu
5. Qi Liu
6. Xiangnan He
7. Xintao Wu
8. Tat-seng Chua

In Computer Science Applications

1. Xiangnan He
2. Bernardo Pereira Nunes
3. Qing Wang
4. Ghodai M. Abdelrahman
5. Neil Heffernan III
6. Xintao Wu

Citations in the corpus

• A Survey on Accuracy-Oriented Neural Recommendation: From Collaborative Filtering to Information-Rich Recommendation (2021, background): “Based on RRNs, rich context factors were considered, such as the social influence [153], [207], item metadata [208], [250] and multimedia data fusion [251].”
• Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracing (2020, methodology): “This method follows the attention mechanism of SAKT [14] and EKT [8].”
• Fine-Grained Similarity Measurement between Educational Videos and Exercises (2020, background): “Generally, similar videos and exercises are those having common concepts (short for knowledge concepts [26, 45] or knowledge points [15]).”
• Knowledge Tracing: A Survey (2022, background): “Publication date: February 2023. such as DKT which uses a hidden state to model knowledge states over a single KC, to later works by memory-augmented KT models (e.g., DKVMN and SKVMN) and by text-aware KT models (e.g., EKT) which use matrices to model knowledge states over multiple KCs, a trend in d”
• MathBERT: A Pre-trained Language Model for General NLP Tasks in Mathematics Education (2021, background): “Predicting students’ next question correctness as a KT task is, for instance, well studied [4, 14, 15, 18, 28] but these solutions tend to rely on highdimensional sequential data.”