Bitte benutzen Sie diese Kennung, um auf die Ressource zu verweisen:
https://doi.org/10.21256/zhaw-26814| Publikationstyp: | Working Paper – Gutachten – Studie |
| Titel: | A learned simulation environment to model student engagement and retention in automated online courses |
| Autor/-in: | Imstepf, Nicolas Senn, Saskia Fortin, Antonio Russell, Benjamin Horn, Claus |
| et. al: | No |
| DOI: | 10.48550/arXiv.2212.14693 10.21256/zhaw-26814 |
| Umfang: | 6 |
| Erscheinungsdatum: | 22-Dez-2022 |
| Verlag / Hrsg. Institution: | arXiv |
| Andere Identifier: | arXiv:2212.14693 |
| Sprache: | Englisch |
| Schlagwörter: | Education technology; Student modeling; Deep reinforcement learning |
| Fachgebiet (DDC): | 378: Hochschulbildung |
| Zusammenfassung: | We 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. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/26814 |
| Lizenz (gemäss Verlagsvertrag): | CC BY-NC-ND 4.0: Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International |
| Departement: | Life Sciences und Facility Management |
| Organisationseinheit: | Institut für Chemie und Biotechnologie (ICBT) Institut für Computational Life Sciences (ICLS) |
| Publiziert im Rahmen des ZHAW-Projekts: | Optimierung von Online-Bildungssystemen mit Hilfe von Reinforcement Learning |
| Enthalten in den Sammlungen: | Publikationen Life Sciences und Facility Management |
Dateien zu dieser Ressource:
| Datei | Beschreibung | Größe | Format | |
|---|---|---|---|---|
| 2022_Imstepf-etal_Learned-simulation.environment-student-engagement-retention.pdf | 738 kB | Adobe PDF |  Öffnen/Anzeigen |
Zur Langanzeige
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.
Alle Ressourcen in diesem Repository sind urheberrechtlich geschützt, soweit nicht anderweitig angezeigt.