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Book Chapter

Evaluation of Computer Vision-Aided Multimedia Learning in Construction Engineering Education

  • Anthony Yusuf
  • Adedeji Afolabi
  • Abiola Akanmu
  • Johnson Olayiwola

Due to the practice-oriented nature of construction engineering education and barriers associated with physical site visits, videos are invaluable means to expose students to practical curricula content. Prior studies have investigated various design principles of multimedia pedagogical tools to enhance student learning and reduce cognitive load. These design principles and computer vision techniques can afford the design and usage of a multimedia learning environment with annotated content to teach students construction safety practices. Hence, using subjective and objective measures such as self-reported cognitive load, eye tracking metrics and verbal feedback, this study assesses the effectiveness of a computer vision-aided multimedia learning environment as well as examines variations across students’ demographics. Students were exposed to both annotated and unannotated versions of the learning environment. The annotated version of the learning environment was considered more effective in triggering students’ attention to learning content, but higher cognitive load levels were reported by participants. The same demographic groups that dwelled longer and on more annotated areas of interest also reported higher overall cognitive load. Keeping with individual differences principle of multimedia learning, demographic variations in participants' cognitive load and effectiveness of the learning environment were reported. The study provides implications for instructors in construction engineering programs on effective use of computer vision-aided annotated videos as instructional materials. This study could serve as a benchmark for future studies on artificial intelligence techniques for signaling in multimedia learning. This study reveals the affordances of computer vision-aided multimedia learning in construction engineering education and the need for adaptation of multimedia learning tools to students’ demographics

  • Keywords:
  • Computer vision,
  • construction engineering education,
  • demographic differences,
  • multimedia learning,
  • video.,
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Anthony Yusuf

Virginia Tech/Myers Lawson School of Construction, United States

Adedeji Afolabi

Virginia Tech/Myers Lawson School of Construction, United States - ORCID: 0000-0002-9065-4766

Abiola Akanmu

Virginia Tech/Myers Lawson School of Construction, United States - ORCID: 0000-0001-9145-4865

Johnson Olayiwola

Virginia Tech/Myers Lawson School of Construction, United States - ORCID: 0000-0003-2795-6195

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  • Publication Year: 2023
  • Pages: 242-253

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  • Publication Year: 2023

Chapter Information

Chapter Title

Evaluation of Computer Vision-Aided Multimedia Learning in Construction Engineering Education

Authors

Anthony Yusuf, Adedeji Afolabi, Abiola Akanmu, Johnson Olayiwola

DOI

10.36253/979-12-215-0289-3.23

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Bibliographic Information

Book Title

CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality

Book Subtitle

Managing the Digital Transformation of Construction Industry

Editors

Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Publisher Name

Firenze University Press

DOI

10.36253/979-12-215-0289-3

eISBN (pdf)

979-12-215-0289-3

eISBN (xml)

979-12-215-0257-2

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Proceedings e report

Series ISSN

2704-601X

Series E-ISSN

2704-5846

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