Improving Prediction of Student Performance in a Blended Course

Sergey Sosnovsky; Almed Hamzah

doi:10.1007/978-3-031-11644-5_54

Improving Prediction of Student Performance in a Blended Course

Sergey Sosnovsky^*, Almed Hamzah

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

Abstract

Traditionally, systems supporting blended learning focus only on one portion of the course by tracing students’ interaction with learning content at home. In this paper, we argue that in-class activity can be also instrumental in eliciting the true state of students’ knowledge and can lead to more accurate models of their performance. Quizitor is an online platform that delivers both the at-home and the in-class assessment. We show that a combination of the two streams of data that Quizitor collects from students can help build more accurate models of students’ mastery that help predict their course performance better than models separately trained on either of these two types of activity.

Original language	English
Title of host publication	Artificial Intelligence in Education
Subtitle of host publication	23rd International Conference, AIED 2022, Durham, UK, July 27–31, 2022, Proceedings, Part I
Editors	Maria Mercedes Rodrigo, Noburu Matsuda, Alexandra I. Cristea, Vania Dimitrova
Place of Publication	Cham
Publisher	Springer
Pages	594-599
Number of pages	6
Edition	1
ISBN (Electronic)	978-3-031-11644-5
ISBN (Print)	978-3-031-11643-8
DOIs	https://doi.org/10.1007/978-3-031-11644-5_54
Publication status	Published - 27 Jul 2022
Event	23rd International Conference on Artificial Intelligence in Education, AIED 2022 - Durham, United Kingdom Duration: 27 Jul 2022 → 31 Jul 2022

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	13355
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	23rd International Conference on Artificial Intelligence in Education, AIED 2022
Country/Territory	United Kingdom
City	Durham
Period	27/07/22 → 31/07/22

Bibliographical note

Funding Information:
The research presented in this paper is partially supported by Universitas Islam Indonesia under Doctoral Grant for Lecturer 2019 (grant no 1296).

Publisher Copyright:
© 2022, Springer Nature Switzerland AG.

Funding

The research presented in this paper is partially supported by Universitas Islam Indonesia under Doctoral Grant for Lecturer 2019 (grant no 1296).

Keywords

Adaptive learning support
Blended learning
Self-assessment
Student modelling
Voting tool

Access to Document

10.1007/978-3-031-11644-5_54

978-3-031-11644-5_54Final published version, 1.65 MBLicence: Taverne

Cite this

Sosnovsky, S., & Hamzah, A. (2022). Improving Prediction of Student Performance in a Blended Course. In M. M. Rodrigo, N. Matsuda, A. I. Cristea, & V. Dimitrova (Eds.), Artificial Intelligence in Education: 23rd International Conference, AIED 2022, Durham, UK, July 27–31, 2022, Proceedings, Part I (1 ed., pp. 594-599). (Lecture Notes in Computer Science; Vol. 13355). Springer. https://doi.org/10.1007/978-3-031-11644-5_54

Sosnovsky, Sergey ; Hamzah, Almed. / Improving Prediction of Student Performance in a Blended Course. Artificial Intelligence in Education: 23rd International Conference, AIED 2022, Durham, UK, July 27–31, 2022, Proceedings, Part I. editor / Maria Mercedes Rodrigo ; Noburu Matsuda ; Alexandra I. Cristea ; Vania Dimitrova. 1. ed. Cham : Springer, 2022. pp. 594-599 (Lecture Notes in Computer Science).

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abstract = "Traditionally, systems supporting blended learning focus only on one portion of the course by tracing students{\textquoteright} interaction with learning content at home. In this paper, we argue that in-class activity can be also instrumental in eliciting the true state of students{\textquoteright} knowledge and can lead to more accurate models of their performance. Quizitor is an online platform that delivers both the at-home and the in-class assessment. We show that a combination of the two streams of data that Quizitor collects from students can help build more accurate models of students{\textquoteright} mastery that help predict their course performance better than models separately trained on either of these two types of activity.",

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Sosnovsky, S & Hamzah, A 2022, Improving Prediction of Student Performance in a Blended Course. in MM Rodrigo, N Matsuda, AI Cristea & V Dimitrova (eds), Artificial Intelligence in Education: 23rd International Conference, AIED 2022, Durham, UK, July 27–31, 2022, Proceedings, Part I. 1 edn, Lecture Notes in Computer Science, vol. 13355, Springer, Cham, pp. 594-599, 23rd International Conference on Artificial Intelligence in Education, AIED 2022, Durham, United Kingdom, 27/07/22. https://doi.org/10.1007/978-3-031-11644-5_54

Improving Prediction of Student Performance in a Blended Course. / Sosnovsky, Sergey; Hamzah, Almed.
Artificial Intelligence in Education: 23rd International Conference, AIED 2022, Durham, UK, July 27–31, 2022, Proceedings, Part I. ed. / Maria Mercedes Rodrigo; Noburu Matsuda; Alexandra I. Cristea; Vania Dimitrova. 1. ed. Cham: Springer, 2022. p. 594-599 (Lecture Notes in Computer Science; Vol. 13355).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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Sosnovsky S, Hamzah A. Improving Prediction of Student Performance in a Blended Course. In Rodrigo MM, Matsuda N, Cristea AI, Dimitrova V, editors, Artificial Intelligence in Education: 23rd International Conference, AIED 2022, Durham, UK, July 27–31, 2022, Proceedings, Part I. 1 ed. Cham: Springer. 2022. p. 594-599. (Lecture Notes in Computer Science). doi: 10.1007/978-3-031-11644-5_54