1. ZHAW digitalcollection
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  4. Publikationen Life Sciences und Facility Management
Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-26814
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dc.contributor.authorImstepf, Nicolas-
dc.contributor.authorSenn, Saskia-
dc.contributor.authorFortin, Antonio-
dc.contributor.authorRussell, Benjamin-
dc.contributor.authorHorn, Claus-
dc.date.accessioned2023-02-09T09:18:13Z-
dc.date.available2023-02-09T09:18:13Z-
dc.date.issued2022-12-22-
dc.identifier.otherarXiv:2212.14693de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/26814-
dc.description.abstractWe developed a simulator to quantify the effect of exercise ordering on both student engagement and retention. Our approach combines the construction of neural network representations for users and exercises using a dynamic matrix factorization method. We further created machine learning models of success and dropout prediction. As a result, our system is able to predict student engagement and retention based on a given sequence of exercises selected. This opens the door to the development of versatile reinforcement learning agents which can substitute the role of private tutoring in exam preparation.de_CH
dc.format.extent6de_CH
dc.language.isoende_CH
dc.publisherarXivde_CH
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0/de_CH
dc.subjectEducation technologyde_CH
dc.subjectStudent modelingde_CH
dc.subjectDeep reinforcement learningde_CH
dc.subject.ddc378: Hochschulbildungde_CH
dc.titleA learned simulation environment to model student engagement and retention in automated online coursesde_CH
dc.typeWorking Paper – Gutachten – Studiede_CH
dcterms.typeTextde_CH
zhaw.departementLife Sciences und Facility Managementde_CH
zhaw.organisationalunitInstitut für Chemie und Biotechnologie (ICBT)de_CH
zhaw.organisationalunitInstitut für Computational Life Sciences (ICLS)de_CH
dc.identifier.doi10.48550/arXiv.2212.14693de_CH
dc.identifier.doi10.21256/zhaw-26814-
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.webfeedAutonomous Systems and Reinforcement Learningde_CH
zhaw.webfeedBiokatalysede_CH
zhaw.funding.zhawOptimierung von Online-Bildungssystemen mit Hilfe von Reinforcement Learningde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Imstepf, N., Senn, S., Fortin, A., Russell, B., & Horn, C. (2022). A learned simulation environment to model student engagement and retention in automated online courses. arXiv. https://doi.org/10.48550/arXiv.2212.14693
Imstepf, N. et al. (2022) A learned simulation environment to model student engagement and retention in automated online courses. arXiv. Available at: https://doi.org/10.48550/arXiv.2212.14693.
N. Imstepf, S. Senn, A. Fortin, B. Russell, and C. Horn, “A learned simulation environment to model student engagement and retention in automated online courses,” arXiv, Dec. 2022. doi: 10.48550/arXiv.2212.14693.
IMSTEPF, Nicolas, Saskia SENN, Antonio FORTIN, Benjamin RUSSELL und Claus HORN, 2022. A learned simulation environment to model student engagement and retention in automated online courses. arXiv
Imstepf, Nicolas, Saskia Senn, Antonio Fortin, Benjamin Russell, and Claus Horn. 2022. “A Learned Simulation Environment to Model Student Engagement and Retention in Automated Online Courses.” arXiv. https://doi.org/10.48550/arXiv.2212.14693.
Imstepf, Nicolas, et al. A Learned Simulation Environment to Model Student Engagement and Retention in Automated Online Courses. arXiv, 22 Dec. 2022, https://doi.org/10.48550/arXiv.2212.14693.


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