Evaluating the Impact of Lightboard Videos on College Students' Performance in a Mathematical Optimization Course

The lightboard, an affordable and readily accessible tool, has become a promising approach for enhancing engagement in instructional videos. Despite its potential, previous studies have primarily highlighted the benefits of lightboard videos by evaluating learners' subjective experiences, with limited empirical research examining their impact on learning outcomes. Moreover, the psychological factors underlying the potential advantages of lightboard videos have remained largely unexplored. To address these gaps, the present study conducted an online learning task in a mathematical optimization course, randomly assigning 78 college students to three groups: lightboard, whiteboard, and no-instructor. Learning outcomes and experiences during the learning process were measured and analyzed. The results showed that the lightboard group experienced significantly lower cognitive load while achieving learning outcomes comparable to the other two groups, suggesting that lightboard videos can reduce students' cognitive load without compromising learning outcomes. Further analysis of the psychological factors revealed that cognitive load played a more critical role than perceived social presence or learning motivation in explaining learning outcomes. These findings underscore the positive impact of lightboard videos on online learning, provide insights into the underlying psychological mechanisms, and offer implications for their integration into educational practices.

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Publication Details

Journal
IEEE Transactions on Learning Technologies
Published
2025-01-01
DOI
https://doi.org/10.1109/tlt.2025.3556527
Primary Topic
Education and Technology Integration
Type
article
Field-Weighted Citation Impact
0.00

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article

Evaluating the Impact of Lightboard Videos on College Students' Performance in a Mathematical Optimization Course

Yilin Mo, Shuai Sun, Jingjing Chen, Dan Zhang et al.
IEEE Transactions on Learning Technologies
Education and Technology Integration
article

Evaluating the Impact of Lightboard Videos on College Students' Performance in a Mathematical Optimization Course

Yilin Mo, Shuai Sun, Jingjing Chen, Dan Zhang, R. Hassan
article en

Abstract

The lightboard, an affordable and readily accessible tool, has become a promising approach for enhancing engagement in instructional videos. Despite its potential, previous studies have primarily highlighted the benefits of lightboard videos by evaluating learners' subjective experiences, with limited empirical research examining their impact on learning outcomes. Moreover, the psychological factors underlying the potential advantages of lightboard videos have remained largely unexplored. To address these gaps, the present study conducted an online learning task in a mathematical optimization course, randomly assigning 78 college students to three groups: lightboard, whiteboard, and no-instructor. Learning outcomes and experiences during the learning process were measured and analyzed. The results showed that the lightboard group experienced significantly lower cognitive load while achieving learning outcomes comparable to the other two groups, suggesting that lightboard videos can reduce students' cognitive load without compromising learning outcomes. Further analysis of the psychological factors revealed that cognitive load played a more critical role than perceived social presence or learning motivation in explaining learning outcomes. These findings underscore the positive impact of lightboard videos on online learning, provide insights into the underlying psychological mechanisms, and offer implications for their integration into educational practices.

IEEE Transactions on Learning TechnologiesVol. 18
Tsinghua University (CN)
National Natural Science Foundation of China
Quality Education
Openalex Percentile: Top 94%
Education and Technology Integration
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Evaluating the Impact of Lightboard Videos on College Students' Performance in a Mathematical Optimization Course — Yilin Mo, Shuai Sun, et al. · IEEE Transactions on Learning Technologies (2025) | TGRS Research Map | TGRS