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

分割 计算机科学 剪辑 人工智能
作者
Hua Zheng,Robert Maribe Branch,Lu Ding,Dongho Kim,Eulho Jung,Zhenqiu Lu,Tong Li,Zilong Pan,Meehyun Yoon
出处
期刊:Active Learning in Higher Education [SAGE Publishing]
卷期号:25 (2): 285-302 被引量:3
标识
DOI:10.1177/14697874221126920
摘要

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.
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