%0 Journal Article %1 8744302 %A q. liu, %A Huang, Z. %A Yin, Y. %A Chen, E. %A Xiong, H. %A Su, Y. %A Hu, G. %D 2019 %J IEEE Transactions on Knowledge and Data Engineering %K deep-learning knowledge-tracing student-modeling %P 1-1 %R 10.1109/TKDE.2019.2924374 %T EKT: Exercise-aware Knowledge Tracing for Student Performance Prediction %U https://ieeexplore.ieee.org/document/8744302 %X 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. Unfortunately, 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, we present a holistic study of student 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, where we simply summarize each student's state into an integrated vector. For making final predictions, we design two implementations on the basis of EERNN with different 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 state vector is extended to a knowledge state matrix. The experimental results on a large-scale data demonstrate the effectiveness of two frameworks as well as the interpretability of EKT.

@article{8744302, abstract = {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. Unfortunately, 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, we present a holistic study of student 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, where we simply summarize each student's state into an integrated vector. For making final predictions, we design two implementations on the basis of EERNN with different 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 state vector is extended to a knowledge state matrix. The experimental results on a large-scale data demonstrate the effectiveness of two frameworks as well as the interpretability of EKT.}, added-at = {2020-07-16T17:37:11.000+0200}, author = {q. {liu} and {Huang}, Z. and {Yin}, Y. and {Chen}, E. and {Xiong}, H. and {Su}, Y. and {Hu}, G.}, biburl = {https://www.bibsonomy.org/bibtex/20eb65c45b04d003eb104a3b26b8c4372/brusilovsky}, description = {EKT: Exercise-aware Knowledge Tracing for Student Performance Prediction - IEEE Journals & Magazine}, doi = {10.1109/TKDE.2019.2924374}, interhash = {01889d94feac3b5960fa1c302095d984}, intrahash = {0eb65c45b04d003eb104a3b26b8c4372}, issn = {1558-2191}, journal = {IEEE Transactions on Knowledge and Data Engineering}, keywords = {deep-learning knowledge-tracing student-modeling}, pages = {1-1}, timestamp = {2020-07-16T17:37:11.000+0200}, title = {EKT: Exercise-aware Knowledge Tracing for Student Performance Prediction}, url = {https://ieeexplore.ieee.org/document/8744302}, year = 2019 }