A SEM Approach to Assess M-Learning Intentions Among Students of Design: An Empirical Analysis Using the TRUTAUT Model

Sachin Srivastava, Narender SINGH Bhati
Journal of Information Technology Education: Research  •  Volume 23  •  2024  •  pp. 015

This research aims to examine the mobile learning (m-learning) intentions of students pursuing design courses at graduate and undergraduate levels in higher education institutions in a developing country like India. This study integrated the Technology Readiness Index (TRI 2.0) and the Unified Theory of Acceptance and Use of Technology (UTAUT) to examine students’ intentions.

Teaching-learning in design programs at institutions predominantly takes place in design studios. Studios are the place where the constant presence of the educator, along with peers, guides the students in all aspects of creative solutions. This interaction has been hindered during the COVID-19 pandemic. Over the last decade, m-learning has grown in popularity among professionals and students. However, the intentions of students pursuing design courses still need to be evaluated.

Using a quantitative approach, a survey of 334 graduate and postgraduate students was held in the National Capital Region of Delhi, India. The students were approached based on a convenience sampling strategy. Structural Equation Modeling (SEM) analysis was carried out to test the formulated hypotheses.

This is one of the first studies that empirically measured m-learning intentions of design students in the Indian context using TRUTAUT scales. The study will motivate educators to identify and integrate online content into the design curriculum. This will also help eliminate students’ insecurity related to performance in online learning.

The study found that the technology readiness (TRI) variables (optimism, innovativeness, and discomfort) had no significant relationship with the UTAUT variables. Design students also exhibited some insecurity about performance in their creative field, which is traditionally conducted face-to-face. But all variables of UTAUT had a significant influence, and the model explains 41% of the variance in m-learning intention.

Teachers, as content creators in design education, need to suitably add online content to their subject that facilitates m-learning among students. Institutional administrators should provide adequate infrastructural facilities like stable internet connectivity, computer labs, and technical staff who can help the students with technical issues.

The outcome of this TRUTAUT research among design students can be studied in other developing countries to examine design students’ intention to adopt m-learning. Researchers can examine the effectiveness of learning through online platforms in design programs.

Future studies could improve the model by extending it appropriately and conducting a longitudinal study with interviews and focus group discussions. Also, including teachers’ opinions and attitudes toward online learning in design programs is worth exploring.

m-learning, design students, UTAUT, technology readiness index, structural equation modeling, TRUTAUT model
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