Learning analytics and the Universal Design for Learning (UDL)

A clustering approach

authored by

Marvin Roski, Ratan Jagan Sebastian, Ralph Ewerth, Anett Hoppe, Andreas Nehring

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.

Organisation(s)

Chemistry Education Section
Visual Analytics Section
Knowledge-Based Systems Section
L3S Research Centre

External Organisation(s)

German National Library of Science and Technology (TIB)

Type

Article

Journal

Computers & education

Volume

214

ISSN

0360-1315

Publication date

06.2024

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Education, Computer Science(all)

Sustainable Development Goals

SDG 4 - Quality Education

Electronic version(s)

https://doi.org/10.1016/j.compedu.2024.105028 (Access: Open)