Technology-Based Instructional Strategies Show Promise in Improving Self-Regulated Learning Skills at Broad-Access Postsecondary Institutions
Authors: 
Renzhe Yu, Hui Yang, Xiaoying Lin, Chengyuan Yao, Paul Burkander, Krystal Thomas, Jessica MislevyAuthors Info & Claims
Renzhe Yu, Hui Yang, Xiaoying Lin, Chengyuan Yao, Paul Burkander, Krystal Thomas, Jessica MislevyAuthors Info & ClaimsPages 408 - 411
Abstract
Self-regulated learning (SRL) is critical for student success in online postsecondary education. Many technology-based interventions have been studied to improve SRL skills, but few were situated in broad-access institutions that disproportionately serve systemically marginalized student populations in STEM fields. This study presents preliminary findings from a rapid-cycle evaluation that tests two technology-supported instructional strategies (videos and prompts) designed to improve SRL in online learning. Using fine-grained clickstream data from 141 students across ten sections of five courses taught at a minority-serving community college, we generate measures of SRL behavior and correlate them with students' exposure to tested strategies. Our results indicate modestly positive relationships between both videos and prompts and SRL behavior. In addition, prompts are more strongly correlated with SRL behavior for first-generation and female students than for their peers. These initial findings reveal the promise and complexity of implementing effective and equitable technology-supported interventions to develop SRL skills and mindsets among diverse student populations in online STEM education.
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Index Terms
- Technology-Based Instructional Strategies Show Promise in Improving Self-Regulated Learning Skills at Broad-Access Postsecondary Institutions
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Published In
July 2024
582 pages
ISBN:9798400706332
DOI:10.1145/3657604
- General Chair:
- David Joyner,
- Program Chairs:
- Min Kyu Kim,
- Xu Wang,
- Meng Xia
Copyright © 2024 Owner/Author.
This work is licensed under a Creative Commons Attribution International 4.0 License.
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Association for Computing Machinery
New York, NY, United States
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Published: 15 July 2024
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