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. 10.18260/1-2--48018

\bibitem{asee_peer_48018}
  Lee, M., \& Cutler, S., \& Zappe, S. E., \& Spiegel, S., \& Osunbunmi, I. S. (2024, June), \emph{Student Preferences and Performance in Active Learning Online Environments }
  Paper presented at 2024 ASEE Annual Conference \& Exposition, Portland, Oregon.
  10.18260/1-2--48018

Minkyung Lee, Stephanie Cutler, Sarah E Zappe, Sam Spiegel, and Ibukun Samuel Osunbunmi.  "Student Preferences and Performance in Active Learning Online Environments ".  2024 ASEE Annual Conference & Exposition, Portland, Oregon, 2024, June.  ASEE Conferences, 2024.  https://peer.asee.org/48018 Internet.  15 Dec, 2024

\bibitem{asee_peer_48018}
  Minkyung Lee, Stephanie Cutler, Sarah E Zappe, Sam Spiegel, and Ibukun Samuel Osunbunmi.
  "Student Preferences and Performance in Active Learning Online Environments ".
  \emph{2024 ASEE Annual Conference \& Exposition, Portland, Oregon, 2024, June}.
  ASEE Conferences, 2024.
  https://peer.asee.org/48018 Internet.  15 Dec, 2024

@INPROCEEDINGS{asee_peer_48018
author = "Minkyung Lee, Stephanie Cutler, Sarah E Zappe, Sam Spiegel, and Ibukun Samuel Osunbunmi"
title = "Student Preferences and Performance in Active Learning Online Environments "
booktitle = "2024 ASEE Annual Conference \& Exposition"
year = "2024"
month = "June"
address = "Portland, Oregon"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/48018}
number = {10.18260/1-2--48018}
}

TY  - CPAPER
AB  - 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. 
AU  - Minkyung Lee
AU  - Stephanie Cutler
AU  - Sarah E Zappe
AU  - Sam Spiegel
AU  - Ibukun Samuel Osunbunmi
CY  - Portland, Oregon
DA  - 2024/06/23
PB  - ASEE Conferences
TI  - Student Preferences and Performance in Active Learning Online Environments 
UR  - https://peer.asee.org/48018
DO  - 10.18260/1-2--48018
ER  -