%0 Conference Paper %1 10.1007/978-3-030-23207-8_56 %A Wang, Zhiwei %A Feng, Xiaoqin %A Tang, Jiliang %A Huang, Gale Yan %A Liu, Zitao %B Artificial Intelligence in Education %C Cham %D 2019 %E Isotani, Seiji %E Millán, Eva %E Ogan, Amy %E Hastings, Peter %E McLaren, Bruce %E Luckin, Rose %I Springer International Publishing %K knowledge-tracing neural-network %P 303--308 %T Deep Knowledge Tracing with Side Information %X Monitoring student knowledge states or skill acquisition levels known as knowledge tracing, is a fundamental part of intelligent tutoring systems. Despite its inherent challenges, recent deep neural networks based knowledge tracing models have achieved great success, which is largely from models' ability to learn sequential dependencies of questions in student exercise data. However, in addition to sequential information, questions inherently exhibit side relations, which can enrich our understandings about student knowledge states and has great potentials to advance knowledge tracing. Thus, in this paper, we exploit side relations to improve knowledge tracing and design a novel framework DTKS. The experimental results on real education data validate the effectiveness of the proposed framework and demonstrate the importance of side information in knowledge tracing. %@ 978-3-030-23207-8

@inproceedings{10.1007/978-3-030-23207-8_56, abstract = {Monitoring student knowledge states or skill acquisition levels known as knowledge tracing, is a fundamental part of intelligent tutoring systems. Despite its inherent challenges, recent deep neural networks based knowledge tracing models have achieved great success, which is largely from models' ability to learn sequential dependencies of questions in student exercise data. However, in addition to sequential information, questions inherently exhibit side relations, which can enrich our understandings about student knowledge states and has great potentials to advance knowledge tracing. Thus, in this paper, we exploit side relations to improve knowledge tracing and design a novel framework DTKS. The experimental results on real education data validate the effectiveness of the proposed framework and demonstrate the importance of side information in knowledge tracing.}, added-at = {2019-12-28T05:28:55.000+0100}, address = {Cham}, author = {Wang, Zhiwei and Feng, Xiaoqin and Tang, Jiliang and Huang, Gale Yan and Liu, Zitao}, biburl = {https://www.bibsonomy.org/bibtex/29582863d8477530b98dfe6114aa5010e/brusilovsky}, booktitle = {Artificial Intelligence in Education}, description = {Deep Knowledge Tracing with Side Information | SpringerLink}, editor = {Isotani, Seiji and Mill{\'a}n, Eva and Ogan, Amy and Hastings, Peter and McLaren, Bruce and Luckin, Rose}, interhash = {edd8cc22e751c24a68e39aae6ae98a40}, intrahash = {9582863d8477530b98dfe6114aa5010e}, isbn = {978-3-030-23207-8}, keywords = {knowledge-tracing neural-network}, pages = {303--308}, publisher = {Springer International Publishing}, timestamp = {2019-12-28T05:28:55.000+0100}, title = {Deep Knowledge Tracing with Side Information}, year = 2019 }