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Investigating the Effectiveness of Video Segmentation on Decreasing Learners’ Cognitive Load in Mobile Learning

  • Pei-Yu Cheng
  • Yueh-Min HuangEmail author
  • Rustam Shadiev
  • Chih-Wei Hsu
  • Shao-Tsu Chu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8699)

Abstract

Researchers have recommended that watching a video for learning purpose can increase learners’ motivation and interests effectively. On the other hand, related studies suggest that lengthy videos need to be segmented in order to decrease learners’ cognitive load. Following abovementioned suggestions, this study administered mobile video lectures for students and investigated how videos of various lengths influence students’ cognitive load and learning performance. Forty freshmen students of one technological university participated in this study; they were randomly assigned into the experiment group and the control group. Segmented video was demonstrated for the experimental group while the control group watched non-segmented video. Results of this study indicate that students in the experiment group had better learning performance and they were less cognitively overloaded compared to students of the control group. Results also showed that the experiment group had less work load compared to the control group. Based on research findings, this study suggests that using segmentation strategy for creating mobile video learning material can help not only to reduce cognitive load but also to increase learning performance.

Keywords

Video Segmentation Cognitive load Learning performance 

Notes

Acknowledgment

This research is partially supported by the “International Research-Intensive Center of Excellence Program” of NTNU and National Science Council, Taiwan, R.O.C. under Grant no. NSC 103-2911-I-003-301, NSC 102-3113-P-006-019-, NSC 100-2511-S-006-014-MY3, and NSC 100-2511-S-006-015-MY3.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Pei-Yu Cheng
    • 1
  • Yueh-Min Huang
    • 1
    Email author
  • Rustam Shadiev
    • 1
  • Chih-Wei Hsu
    • 2
  • Shao-Tsu Chu
    • 3
  1. 1.Department of Engineering ScienceNational Cheng Kung UniversityTainanTaiwan
  2. 2.Graduate School of Digital Living TechnologyKsu Shan UniversityTainanTaiwan
  3. 3.Department of Information and CommunicationKsu Shan UniversityTainanTaiwan

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