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Dissertation
Self-regulated learning, study skills, and learning analytics dashboards in an undergraduate learning environment
University of Iowa
Doctor of Philosophy (PhD), University of Iowa
Summer 2025
DOI: 10.25820/etd.008124
Abstract
This study explored the use of learning analytics dashboards (LADs) to support self-regulated learning (SRL) for undergraduates. Learning analytics data have become more widely available in the context of undergraduate learning environments. In recent years, it has become much easier to capture students’ online interactions with an institution’s learning management system (LMS) and third-party platforms associated with a course (e.g., textbook publishers, homework platforms, student response systems, etc.). These interactions generate log data that can be shared with learners to help them understand aspects of their own learning process. This presents an opportunity to support learners’ development and execution of SRL skills. Additionally, there is the potential to supplement traditional methods of measuring SRL (e.g., self-report survey instruments) with measures of behavior which are less prone to certain pitfalls.
The study context was an introductory biology course in which an LAD was available for students’ voluntary use. It provided detailed information to students about their performance in the course and also aspects of their engagement with the course LMS materials relative to their peers. Each type of LMS trace data event was categorized in terms of relevance to a phase of SRL (Planning, Enacting, or Adapting). Results from generalized linear models and epistemic network analysis (ENA) indicated that students that used the LAD also differed in their general use of the LMS compared to those that did not use the LAD. Specifically, use of the LAD was associated with more distributed engagement with the LMS course site and more consistent monitoring of grades. Additional findings pertained to the potential negative association of mobile device use with LMS engagement and a distinction between static and dynamic resources for the purposes of planning in SRL. Implications for researchers, instructors, and designers of educational technologies are discussed.
Details
- Title: Subtitle
- Self-regulated learning, study skills, and learning analytics dashboards in an undergraduate learning environment
- Creators
- Salim George
- Contributors
- Kathy Schuh (Advisor)Ariel Aloe (Committee Member)Callie Sung (Committee Member)Matthew Lira (Committee Member)
- Resource Type
- Dissertation
- Degree Awarded
- Doctor of Philosophy (PhD), University of Iowa
- Degree in
- Psychological and Quantitative Foundations (Learning Sciences and Educational Psychology)
- Date degree season
- Summer 2025
- DOI
- 10.25820/etd.008124
- Publisher
- University of Iowa
- Number of pages
- xi, 121 pages
- Copyright
- Copyright 2025 Salim George
- Language
- English
- Date submitted
- 05/21/2025
- Description illustrations
- Illustrations, graphs, charts, tables
- Description bibliographic
- Includes bibliographical references (pages 100-115).
- Public Abstract (ETD)
As Big Data influences more and more aspects of our lives and the tools we use (social media, streaming services, smartwatches, etc.), it s no surprise that it could play an important role in the classroom. This study explored the use of a data tool designed to help college students prepare for their courses better. In addition to individual grades, the tool allowed students to see how their current standing compared to other students in the course. It also allowed students to see how engaged they were with some aspects of the course (things like logging in to the course website or viewing course materials online) compared to other students. There is some evidence that this kind of information can motivate students to study more. There is also some evidence that use of tools like this are associated with getting better grades.
The goal of this research study was to understand if using a data tool like the one described could lead to changes in how students choose to interact with course materials. This involved using statistical analysis to detect differences between two groups of students: (a) those that used the tool at least twice and (b) those that never used it or only used it once. The results indicated that the students that used the tool also interacted with course materials differently than those that did not. These students also performed much better in the course. This helps better explain how tools like this could support study skills and learning.
- Academic Unit
- Psychological and Quantitative Foundations
- Record Identifier
- 9984948341502771
Metrics
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