TY - JOUR

T1 - Learning analytics and the Universal Design for Learning (UDL)

T2 - A clustering approach

AU - Roski, Marvin

AU - Jagan Sebastian, Ratan

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 - 2024/6

Y1 - 2024/6

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 -