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The combination of segmentation and self-explanation to enhance video-based learning

Cited 3 time in Web of Science Cited 2 time in Scopus
Authors

Zheng, Hua; Branch, Robert Maribe; Ding, Lu; Kim, Dongho; Jung, Eulho; Lu, Zhenqiu; Li, Tong; Pan, Zilong; Yoon, Meehyun

Issue Date
2024-07
Publisher
SAGE Publications
Citation
Active Learning in Higher Education, Vol.25 No.2, pp.285-302
Abstract
This study examined the effects of an integrated approach that combines segmentation and self-explanation designs on learner achievement for meaningful video-based learning. This was a pretest-posttest research design with a sample size of 121 participants randomly assigned to one of four different types of video instructions (continuous, segmentation, self-explanation, or the combination of segmentation and self-explanation). Participants engaged in video instruction that used either a continuous video or segmented video clips and incorporated either self-explanation prompts or no self-explanation prompts. The results showed that participants in the combination and segmentation conditions outperformed those in the continuous condition in evaluation ability, and participants in the combination condition outperformed those in the continuous condition in the overall performance after controlling for prior knowledge. The current study indicates that the combined design can effectively facilitate student learning by engaging them in meaningful video-based learning.
ISSN
1469-7874
URI
https://hdl.handle.net/10371/219115
DOI
https://doi.org/10.1177/14697874221126920
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  • College of Agriculture and Life Sciences
  • Department of Vocational Education and Workforce Development
Research Area AI-Human Interaction, People Analytics, Technology-Based Career Development

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