Text

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

---

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

---

Availability

No copy data

Detail Information

Series Title

Proceedings e report,

Call Number

Virtualization

Publisher

www.fupress.com/ : Firenze University Press., Florence, 2023

Collation

oer.unej.ac.id

Language

English

ISBN/ISSN

9791221502893

Classification

Virtualization

Content Type

text

Media Type

computer

Carrier Type

online resource

Edition

-

Subject(s)

Computer
Computer Vision
Virtualization

Specific Detail Info

-

Statement of Responsibility

Yusuf, Anthony

Other Information

Cataloger

-

Source

https://directory.doabooks.org/handle/20.500.12854/136375

Validator

Suwardi

Digital Object Identifier (DOI)

10.36253/979-12-215-0289-3.23

Journal Volume

-

Journal Issue

-

Subtitle

-

Parallel Title

-

Other version/related

File Attachment

Comments

---

You must be logged in to post a comment