Details
| Original language | English |
|---|---|
| Article number | 105028 |
| Journal | Computers & education |
| Volume | 214 |
| Early online date | 2 Mar 2024 |
| Publication status | E-pub ahead of print - 2 Mar 2024 |
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 I 3Learn. 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.
Keywords
- Clustering, Education inclusive education, Web-based learning science
ASJC Scopus subject areas
- Social Sciences(all)
- Education
- Computer Science(all)
Sustainable Development Goals
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In: Computers & education, Vol. 214, 105028, 06.2024.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Learning analytics and the Universal Design for Learning (UDL)
T2 - A clustering approach
AU - Roski, Marvin
AU - Sebastian, Ratan J.
AU - Ewerth, Ralph
AU - Hoppe, Anett
AU - Nehring, Andreas
N1 - Funding Information: This paper results from the PhD program LernMINT, founded by the Ministry of Science and Culture, Lower Saxony, Germany.
PY - 2024/3/2
Y1 - 2024/3/2
N2 - 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 I 3Learn. 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.
AB - 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 I 3Learn. 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.
KW - Clustering
KW - Education inclusive education
KW - Web-based learning science
UR - http://www.scopus.com/inward/record.url?scp=85186653675&partnerID=8YFLogxK
U2 - 10.1016/j.compedu.2024.105028
DO - 10.1016/j.compedu.2024.105028
M3 - Article
VL - 214
JO - Computers & education
JF - Computers & education
SN - 0360-1315
M1 - 105028
ER -