What question did this study set out to answer?

The study aims to explore how AutoCAD-based spatial learning influences engineering students' spatial visualization skills in developing countries.

February 2, 2026

No Student Left Behind in Engineering Education: An Empirical Study on AutoCAD‐Based Spatial Learning and Its Associations With Visualization Skills in Developing Countries

Key Points

The study aims to explore how AutoCAD-based spatial learning influences engineering students' spatial visualization skills in developing countries.
Utilized a cross-sectional survey design with 322 engineering students in Vietnam.
Employed Structural Equation Modeling (SEM) with AMOS to analyze the data.
Investigated relationships among factors such as Software Interaction, Learning Motivation, and Active Engagement.
Software Interaction had the strongest positive association with spatial self-efficacy (β = 0.502, p < 0.001).
Learning Motivation was significantly associated with perceived spatial visualization skills (β = 0.323, p < 0.001).
Unguided Active Engagement was negatively associated with perceived spatial visualization skills (β = -0.142, p = 0.011).

Abstract

ABSTRACT This study empirically examines the associations between AutoCAD‐based spatial learning and engineering students perceived spatial visualization skills (SVS), measured via self‐reported spatial self‐efficacy (SS) rather than objective spatial tests, particularly in developing country contexts such as Vietnam. By integrating SERVQUAL and the Unified Theory of Acceptance and Use of Technology (UTAUT), the research examines perceived learning experiences alongside behavioral factors shaping students' adoption of AutoCAD in education. Given the cross‐sectional survey design, the identified relationships are interpreted as associative rather than causal. Data were collected from 322 engineering students in Vietnam using a structured survey, and Structural Equation Modeling (SEM) with AMOS was employed to analyze relationships among Facilitating Conditions (FC), Software Interaction (SI), Learning Motivation (LM), SS, and Active Engagement (AE). Results indicate that SI shows the strongest association with SS ( β = 0.502, p < 0.001), highlighting the role of intuitive software use in SS. LM was found to be significantly associated with perceived SVS ( β = 0.323, p < 0.001), underscoring the importance of motivation in technology‐enhanced learning contexts. However, the findings suggest that unguided AE was negatively associated with perceived SVS (β = –0.142, p = 0.011), suggesting that engagement without instructional scaffolding may be counterproductive, and that Performance Expectancy (PE) alone may not translate into meaningful engagement without adequate instructional support. The findings highlight the importance of supportive learning environments that ensure infrastructure readiness, user‐friendly software, and clear instructional strategies. The study proposes an integrated SERVQUAL–UTAUT framework to jointly capture service quality and technology acceptance factors in AutoCAD‐based learning. Situated in a developing‐country context, the study offers implications for more inclusive technology adoption in engineering education and provides practical insights for educators and policymakers.

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