Student Preferences and Performance in Active Learning Online Environments
- Conference
- Location
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Portland, Oregon
- Publication Date
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June 23, 2024
- Start Date
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June 23, 2024
- End Date
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July 12, 2024
- Conference Session
- Tagged Division
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Computers in Education Division (COED)
- Permanent URL
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https://peer.asee.org/48018
Paper Authors
Minkyung Lee Pennsylvania State University
Minkyung Lee is a doctoral candidate in the Department of Learning and Performance Systems at Penn State University and serves as a Graduate Assistant at the Leonhard Center, an engineering education center at Penn State. Her academic journey and professional contributions reflect her dedication to the field of educational technology and design.
Stephanie Cutler Pennsylvania State University
Dr. Stephanie Cutler has degrees in Mechanical Engineering, Industrial and Systems Engineering, and a PhD in Engineering Education from Virginia Tech. She is an Associate Research Professor and the Director of Assessment and Instructional Support in the Leonhard Center at Penn State.
Sarah E Zappe Pennsylvania State University
Dr. Sarah Zappe is Director of the Leonhard Center for the Enhancement of Engineering Education and Assistant Dean of Teaching and Learning at Penn State. She holds a doctoral degree in educational psychology emphasizing applied measurement.
Sam Spiegel Colorado School of Mines
Dr. Spiegel is Assistant Vice President for Online Education and was the founding Director, Trefny Innovative Instruction Center at the Colorado School of Mines. He served as Chair, Disciplinary Literacy in Science and as Associate Director, Engineering Education Research Center at the University of Pittsburgh; Director of Research & Development for a multimedia company; and as founding Director of the Center for Integrating Research & Learning (CIRL) at the National High Magnetic Field Laboratory. His current efforts focus on innovation of teaching practices in STEM fields and systemic change within higher education.
Ibukun Samuel Osunbunmi
Pennsylvania State University
orcid.org/0000-0002-7821-9059
Ibukun Samuel Osunbunmi is an Assistant Research Professor, and Assessment and Instructional Specialist at Pennsylvania State University. He holds a Ph.D. degree in Engineering Education from Utah State University. Also, he has BSc and MSc degrees in mechanical engineering. His research interests include student engagement, design thinking, learning environment, evidence-based pedagogy, e-learning, broadening participation in STEM education, sustainable energy, and material characterization.
Abstract
Education environments are continually evolving to identify optimal learning environments tailored to student needs, especially with respect to instructional methodologies to engage students. One of the strategies proven effective in engineering education for the recent decade is active learning. Active learning emphasizes a student-centered approach to encourage higher-order thinking resulting in enhanced student performance across various disciplines in engineering education.
This research investigated the relationship between students' instructional mode preferences and academic performance across three online modules: statistics, material jetting, and Python programming. By employing a ranking-based survey, students' preferences among four distinct modes of instruction including traditional (synchronous with instructor), self-study (asynchronous, independent), gamified, and Virtual Reality (VR) were investigated to determine the correlations between learner preference and academic outcomes in the module as measured by a post-assessment. Beyond that, we aim to understand the predictability of learner performance based on their mode preferences. This exploration extends to understanding the impact of various factors on student outcomes when engaged with different instructional modes. More explicitly, this paper considers how students' mode ranking preference across modules focusing on different topics influence not just their test scores but also the broader metrics of content comprehension.
Participants in this study included 31 industry professionals and 33 engineering students from the western United States. Following their online sessions, students undertook performance tests to measure their understanding of the content materials. Then, they completed a survey, ranking their instructional preferences among Traditional, VR, Game-based, and Self-study in general as well as for each module topic.
We have several significant findings in our pursuit of understanding the intricacies between students’ instructional preferences related to modes of instruction and their academic performance on corresponding assessments. First, our results revealed a lack of substantial correlations between students’ preferences for specific instructional modes and their actual performance, which challenges the idea that aligning instruction with preferences leads to improved performance. We further observed strong or moderate correlation among these preferences. For example, students who exhibited a strong preference for the traditional learning mode often ranked Game based learning lower, and those who favored self-study tended to be less attracted to the VR mode. Finally, our regression analysis offered additional insights into the relationship between instructional modes and performance outcomes across different subjects. For the different module topics, learning mode preferences were ranked differently.
This study contributes to engineering education by informing the relationship between students' instructional preferences and their academic performance. Notably, it underscores that personal preferences towards certain instructional modes do not consistently align with better academic results. These findings encourage educators to re-evaluate teaching methods to align their instructional approach with the course content while balancing learner preference and engagement. Furthermore, the research informs educational institutions of the potential return on investment when integrating newer technologies like VR. Ultimately, this research invites a deeper exploration of pedagogical tools and strategies in engineering education, emphasizing the need for a harmonized approach that considers both student preference and educational efficacy.
Lee, M., & Cutler, S., & Zappe, S. E., & Spiegel, S., & Osunbunmi, I. S. (2024, June), Student Preferences and Performance in Active Learning Online Environments Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. https://peer.asee.org/48018
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