Evaluation of Instructors’ Demographic Variations on a Web-based Platform for Connecting with Practitioners

Abstract

Exploration of demographic variations is required to develop dynamic web platforms that cater to the varying preferences of diverse users. Hence, this study evaluated instructors’ demographic variations on a web-based platform for connecting with practitioners for student development. Both objective and subjective measures were adopted to investigate age- and gender-related differences in gaze behavior, task completion time, perceived cognitive load, perceived usability, and trust. Compared to male instructors, female instructors had higher fixation counts, longer task completion times, and statistically significant longer fixation duration. Female instructors gave higher usability and trust ratings but reported a higher cognitive workload. Compared to Generation Y instructors, Generation X instructors had longer fixation duration, higher fixation count, and statistically longer task completion time. Generation X instructors reported high cognitive load, lower usability, and trust ratings. The study also reveals demographic differences in parameters that instructors focused on while connecting with practitioners via a web platform.

Keywords:

• Age
• demographic differences
• gender
• Generation X
• Generation Y
• instructors
• web platform

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data for this study are available from the corresponding author upon reasonable request.

Additional information

Funding

This research is based on work supported by the National Science Foundation (NSF) via Grant No. 2201641.

Notes on contributors

Anthony Yusuf

Anthony Yusuf is currently a PhD student at the Myers-Lawson School of Construction, Virginia Tech. He holds a BSc and an MSc in Quantity Surveying from Obafemi Awolowo University. His research interests include future professional workforce development, human–computer interaction, ergonomics, machine learning, and smart construction.

Adedeji Afolabi

Adedeji Afolabi is a Research Associate at the Myers-Lawson School of Construction at Virginia Tech. He received his PhD in Building Technology (Construction Management) from Covenant University. His research interests include workforce sustainability, construction automation, data analytics, and exoskeletons.

Abiola Akanmu

Abiola Akanmu is an Associate Professor at the Myers-Lawson School of Construction at Virginia Tech. She holds a PhD in Architectural Engineering. Her research interests include the application of intelligence to the design, construction, and maintenance of building and civil infrastructure systems using information and communication technologies.

Homero Murzi

Homero Murzi is an Associate Professor in the Department of Engineering Education at Virginia Tech. He holds a PhD in Engineering Education from Virginia Tech. His research interests include culturally relevant teaching and learning, Latinx/é, indigenous, and international engineering education, emotions in engineering, and global engineering competencies.

Andrea Ofori-Boadu

Andrea Ofori-Boadu is an Associate Professor in the Department of Built Environment, at North Carolina Agricultural and Technical State University. She holds a PhD in Technology Management from Indiana State University. Her research interests include sustainable built environments, and professional identity development processes towards architecture, engineering, and construction careers.

Sheryl Ball

Sheryl Ball is a Professor in the Department of Economics, at Virginia Tech. Ball received her PhD in Managerial Economics and Decision Sciences from Northwestern University. Her research interests include behavioral economics methods and theories, experimental economics, and neuroeconomics.