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Prediction of Cognitive Load during Industry-Academia Collaboration via a Web Platform

  • Anthony Yusuf
  • Abiola Akanmu
  • Adedeji Afolabi
  • Homero Murzi

Web platforms are increasingly being used to connect communities, including construction industry and academia. Design features of such platforms could impose excessive cognitive workload thereby impacting the use of the platform. This is a crucial consideration especially for new web platforms to secure users’ interest in continuous usage. Understanding users’ cognitive workloads while using web platforms could help make necessary modifications and adapt the features to users’ preferences. Users’ usage patterns can be leveraged to predict the needs of users. Hence, the pattern of cognitive demand that users experience can be used to predict the cognitive load of web platform users. This could provide insights, generate feedback, and identify areas of modification that are critical for sustaining acceptability of web platforms. Using recurrent neural network, this study adopts electroencephalogram (EEG) data as a physiological measure of brain activity to predict brain signals (cognitive load) of users while interacting with a web platform designed to connect industry and academia for future workforce development. This paper presents a Long Short-Term Memory (LSTM) based approach to develop a model for predicting users’ cognitive load via EEG signals. Nineteen (19) potential end-users of the proposed web platform were recruited as participants in this study. The participants interacted with the web-platform in a real case scenario and their brain signals were captured using a five-channel EEG device. The validity of the proposed method was evaluated using root mean square error (RMSE), coefficient of determination (R2), and comparison of the predicted and actual EEG signals and mental workload. The results revealed the reliability of the model and provided a suitable method for predicting users brain signals while using web platforms. This could be leveraged to understand users’ cognitive demand which could provide insights for web platform improvements to engender users’ continuous usage

  • Keywords:
  • Cognitive load,
  • electroencephalogram,
  • industry-academia collaboration,
  • long short-term memory,
  • web platform,
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Anthony Yusuf

Virginia Tech/Myers Lawson School of Construction, United States

Abiola Akanmu

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

Adedeji Afolabi

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

Homero Murzi

Virginia Tech, United States - ORCID: 0000-0003-3849-2947

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  • Publication Year: 2023
  • Pages: 57-68

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  • Publication Year: 2023

Chapter Information

Chapter Title

Prediction of Cognitive Load during Industry-Academia Collaboration via a Web Platform

Authors

Anthony Yusuf, Abiola Akanmu, Adedeji Afolabi, Homero Murzi

DOI

10.36253/979-12-215-0289-3.06

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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