dc.identifier.uri |
http://dx.doi.org/10.15488/17071 |
|
dc.identifier.uri |
https://www.repo.uni-hannover.de/handle/123456789/17199 |
|
dc.contributor.author |
Roski, Marvin
|
|
dc.contributor.author |
Sebastian, Ratan
|
|
dc.contributor.author |
Ewerth, Ralph
|
|
dc.contributor.author |
Hoppe, Anett
|
|
dc.contributor.author |
Nehring, Andreas
|
|
dc.date.accessioned |
2024-04-16T05:58:53Z |
|
dc.date.available |
2024-04-16T05:58:53Z |
|
dc.date.issued |
2024 |
|
dc.identifier.citation |
Roski, M.; Sebastian, R.; Ewerth, R.; Hoppe, A.; Nehring, A.: Learning analytics and the Universal Design for Learning (UDL): A clustering approach. In: Computers & Education 214 (2024), 105028. DOI: https://doi.org/10.1016/j.compedu.2024.105028 |
|
dc.description.abstract |
In the context of inclusive education, Universal Design for Learning (UDL) is a framework used worldwide to create learning opportunities accessible to all learners. While much research focused on the design and students' perceptions of UDL-based learning settings, studies on students’ usage patterns in UDL-guided elements, particularly in digital environments, are still scarce. Therefore, we analyze and cluster the usage patterns of 9th and 10th graders in a web-based learning platform called I3Learn. The platform focuses on chemistry learning, and UDL principles guide its design. We collected the temporal usage patterns of UDL-guided elements of 384 learners in detailed log files. The collected data includes the time spent using video and/or text as a source of information, working on learning tasks with or without help and working on self-assessments. We used Exploratory Factor Analysis (EFA) to identify relevant factors in the observed usage behaviors. Based on the factor loadings, we extracted features for k-means clustering and named the resulting groups based on their usage patterns and learner characteristics. The EFA revealed four factors suggesting that learners remain consistent in selecting UDL-guided elements that require a decision (video or text, tasks with or without help). Based on these four factors, the cluster analysis identifies six different groups. We discuss these results as a starting point to provide individualized learning support through further artificial intelligence applications and inform educators about learner activity through a dashboard. |
eng |
dc.language.iso |
eng |
|
dc.publisher |
Amsterdam [u.a.] : Elsevier Science |
|
dc.relation.ispartofseries |
Computers & Education 214 (2024) |
|
dc.rights |
CC BY 4.0 Unported |
|
dc.rights.uri |
https://creativecommons.org/licenses/by/4.0 |
|
dc.subject |
Clustering |
eng |
dc.subject |
Education inclusive education |
eng |
dc.subject |
Web-based learning science |
eng |
dc.subject.ddc |
004 | Informatik
|
|
dc.title |
Learning analytics and the Universal Design for Learning (UDL): A clustering approach |
eng |
dc.type |
Article |
|
dc.type |
Text |
|
dc.relation.essn |
1873-782X |
|
dc.relation.issn |
0360-1315 |
|
dc.relation.doi |
https://doi.org/10.1016/j.compedu.2024.105028 |
|
dc.bibliographicCitation.volume |
214 |
|
dc.bibliographicCitation.firstPage |
105028 |
|
dc.description.version |
publishedVersion |
|
tib.accessRights |
frei zug�nglich |
|