Dropout Prediction in a Web Environment Based on Universal Design for Learning

Publikation: Beitrag in Buch/Bericht/Sammelwerk/KonferenzbandAufsatz in KonferenzbandForschungPeer-Review

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  • Technische Informationsbibliothek (TIB) Leibniz-Informationszentrum Technik und Naturwissenschaften und Universitätsbibliothek
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Details

OriginalspracheEnglisch
Titel des SammelwerksArtificial Intelligence in Education - 24th International Conference, AIED 2023, Proceedings
Untertitel24th International Conference, AIED 2023, Tokyo, Japan, July 3–7, 2023, Proceedings
Herausgeber/-innenNing Wang, Genaro Rebolledo-Mendez, Noboru Matsuda, Olga C. Santos, Vania Dimitrova
Seiten515–527
Seitenumfang13
ISBN (elektronisch)978-3-031-36272-9
PublikationsstatusVeröffentlicht - 26 Juni 2023

Publikationsreihe

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Band13916 LNAI
ISSN (Print)0302-9743
ISSN (elektronisch)1611-3349

Abstract

Dropout prediction is an essential task in educational Web platforms to identify at-risk learners, enable individualized support, and eventually prevent students from quitting a course. Most existing studies on dropout prediction focus on improving machine learning methods based on a limited set of features to model students. In this paper, we contribute to the field by evaluating and optimizing dropout prediction using features based on personal information and interaction data. Multiple granularities of interaction and additional unique features, such as data on reading ability and learners’ cognitive abilities, are tested. Using the Universal Design for Learning (UDL), our Web-based learning platform called I 3Learn aims at advancing inclusive science learning by focusing on the support of all learners. A total of 580 learners from different school types have used the learning platform. We predict dropout at different points in the learning process and compare how well various types of features perform. The effectiveness of predictions benefits from the higher granularity of interaction data that describe intermediate steps in learning activities. The cold start problem can be addressed using assessment data, such as a cognitive abilities assessment from the pre-test of the learning platform. We discuss the experimental results and conclude that the suggested feature sets may be able to reduce dropout in remote learning (e.g., during a pandemic) or blended learning settings in school.

ASJC Scopus Sachgebiete

Zitieren

Dropout Prediction in a Web Environment Based on Universal Design for Learning. / Roski, Marvin; Jagan Sebastian, Ratan; Ewerth, Ralph et al.
Artificial Intelligence in Education - 24th International Conference, AIED 2023, Proceedings: 24th International Conference, AIED 2023, Tokyo, Japan, July 3–7, 2023, Proceedings. Hrsg. / Ning Wang; Genaro Rebolledo-Mendez; Noboru Matsuda; Olga C. Santos; Vania Dimitrova. 2023. S. 515–527 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 13916 LNAI).

Publikation: Beitrag in Buch/Bericht/Sammelwerk/KonferenzbandAufsatz in KonferenzbandForschungPeer-Review

Roski, M, Jagan Sebastian, R, Ewerth, R, Hoppe, A & Nehring, A 2023, Dropout Prediction in a Web Environment Based on Universal Design for Learning. in N Wang, G Rebolledo-Mendez, N Matsuda, OC Santos & V Dimitrova (Hrsg.), Artificial Intelligence in Education - 24th International Conference, AIED 2023, Proceedings: 24th International Conference, AIED 2023, Tokyo, Japan, July 3–7, 2023, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bd. 13916 LNAI, S. 515–527. https://doi.org/10.1007/978-3-031-36272-9_42
Roski, M., Jagan Sebastian, R., Ewerth, R., Hoppe, A., & Nehring, A. (2023). Dropout Prediction in a Web Environment Based on Universal Design for Learning. In N. Wang, G. Rebolledo-Mendez, N. Matsuda, O. C. Santos, & V. Dimitrova (Hrsg.), Artificial Intelligence in Education - 24th International Conference, AIED 2023, Proceedings: 24th International Conference, AIED 2023, Tokyo, Japan, July 3–7, 2023, Proceedings (S. 515–527). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 13916 LNAI). https://doi.org/10.1007/978-3-031-36272-9_42
Roski M, Jagan Sebastian R, Ewerth R, Hoppe A, Nehring A. Dropout Prediction in a Web Environment Based on Universal Design for Learning. in Wang N, Rebolledo-Mendez G, Matsuda N, Santos OC, Dimitrova V, Hrsg., Artificial Intelligence in Education - 24th International Conference, AIED 2023, Proceedings: 24th International Conference, AIED 2023, Tokyo, Japan, July 3–7, 2023, Proceedings. 2023. S. 515–527. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-36272-9_42
Roski, Marvin ; Jagan Sebastian, Ratan ; Ewerth, Ralph et al. / Dropout Prediction in a Web Environment Based on Universal Design for Learning. Artificial Intelligence in Education - 24th International Conference, AIED 2023, Proceedings: 24th International Conference, AIED 2023, Tokyo, Japan, July 3–7, 2023, Proceedings. Hrsg. / Ning Wang ; Genaro Rebolledo-Mendez ; Noboru Matsuda ; Olga C. Santos ; Vania Dimitrova. 2023. S. 515–527 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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title = "Dropout Prediction in a Web Environment Based on Universal Design for Learning",
abstract = "Dropout prediction is an essential task in educational Web platforms to identify at-risk learners, enable individualized support, and eventually prevent students from quitting a course. Most existing studies on dropout prediction focus on improving machine learning methods based on a limited set of features to model students. In this paper, we contribute to the field by evaluating and optimizing dropout prediction using features based on personal information and interaction data. Multiple granularities of interaction and additional unique features, such as data on reading ability and learners{\textquoteright} cognitive abilities, are tested. Using the Universal Design for Learning (UDL), our Web-based learning platform called I 3Learn aims at advancing inclusive science learning by focusing on the support of all learners. A total of 580 learners from different school types have used the learning platform. We predict dropout at different points in the learning process and compare how well various types of features perform. The effectiveness of predictions benefits from the higher granularity of interaction data that describe intermediate steps in learning activities. The cold start problem can be addressed using assessment data, such as a cognitive abilities assessment from the pre-test of the learning platform. We discuss the experimental results and conclude that the suggested feature sets may be able to reduce dropout in remote learning (e.g., during a pandemic) or blended learning settings in school.",
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AU - Ewerth, Ralph

AU - Hoppe, Anett

AU - Nehring, Andreas

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