%0 Conference Paper %1 8594965 %A Minn, S. %A Yu, Y. %A Desmarais, M. C. %A Zhu, F. %A Vie, J. %B 2018 IEEE International Conference on Data Mining (ICDM) %D 2018 %K deep-learning knowledge-tracing modeling student %P 1182-1187 %R 10.1109/ICDM.2018.00156 %T Deep Knowledge Tracing and Dynamic Student Classification for Knowledge Tracing %U https://ieeexplore.ieee.org/document/8594965 %X In Intelligent Tutoring System (ITS), tracing the student's knowledge state during learning has been studied for several decades in order to provide more supportive learning instructions. In this paper, we propose a novel model for knowledge tracing that i) captures students' learning ability and dynamically assigns students into distinct groups with similar ability at regular time intervals, and ii) combines this information with a Recurrent Neural Network architecture known as Deep Knowledge Tracing. Experimental results confirm that the proposed model is significantly better at predicting student performance than well known state-of-the-art techniques for student modelling.

@inproceedings{8594965, abstract = {In Intelligent Tutoring System (ITS), tracing the student's knowledge state during learning has been studied for several decades in order to provide more supportive learning instructions. In this paper, we propose a novel model for knowledge tracing that i) captures students' learning ability and dynamically assigns students into distinct groups with similar ability at regular time intervals, and ii) combines this information with a Recurrent Neural Network architecture known as Deep Knowledge Tracing. Experimental results confirm that the proposed model is significantly better at predicting student performance than well known state-of-the-art techniques for student modelling.}, added-at = {2020-10-22T17:21:00.000+0200}, author = {{Minn}, S. and {Yu}, Y. and {Desmarais}, M. C. and {Zhu}, F. and {Vie}, J.}, biburl = {https://www.bibsonomy.org/bibtex/21baebd97936065db3acb81c2d2784d09/brusilovsky}, booktitle = {2018 IEEE International Conference on Data Mining (ICDM)}, description = {Deep Knowledge Tracing and Dynamic Student Classification for Knowledge Tracing - IEEE Conference Publication}, doi = {10.1109/ICDM.2018.00156}, interhash = {19dc76976ab5cbafe6c62c765b7e1d0b}, intrahash = {1baebd97936065db3acb81c2d2784d09}, issn = {2374-8486}, keywords = {deep-learning knowledge-tracing modeling student}, month = nov, pages = {1182-1187}, timestamp = {2020-10-22T17:21:00.000+0200}, title = {Deep Knowledge Tracing and Dynamic Student Classification for Knowledge Tracing}, url = {https://ieeexplore.ieee.org/document/8594965}, year = 2018 }