Students’ Acceptance of a Mobile Robotic Training Kit: Extending the Technology Acceptance Model with Cognitive Load in Vocational Education

Aim/Purpose: This study aims to analyze students’ acceptance of mobile robotics training kits in vocational education by extending the Technology Acceptance Model (TAM) through the integration of the cognitive load dimension. This study stems from the limited number of studies that explain the acceptance of robotics learning technology from the perspective of students’ cognitive processes. Background: Although robotics-based learning is increasingly being implemented in engineering and vocational education, most previous research has focused primarily on psychological, social, and technological factors in explaining technology acceptance. This study addresses this gap by integrating Cognitive Load Theory into the Technology Acceptance Model to explain how intrinsic, extraneous, and germane cognitive load influence the perceived usefulness and perceived ease of use of a mobile robotic training kit. Methodology: This study employed a quantitative approach using Partial Least Squares Structural Equation Modeling (PLS-SEM). The study participants consisted of 118 students from the Industrial Electronics Technology program. Data were collected using a 26-item Likert-scale questionnaire measuring intrinsic cognitive load, extraneous cognitive load, germane cognitive load, perceived usefulness, perceived ease of use, attitude toward using, and behavioral intention. Contribution: This study significantly expands the TAM model by integrating CLT as a cognitive mechanism that explains how perceptions of ease of use and usefulness of mobile robotics training kits are formed during learning robotics, microcontrollers, or complex embedded systems. The study reveals that cognitive load is not merely an additional factor but a key determinant of technology acceptance in the learning process. Findings: The results indicate that nine out of ten hypotheses were supported. Extraneous and germane cognitive load significantly influenced perceived usefulness and perceived ease of use, while intrinsic cognitive load affected perceived usefulness but not perceived ease of use. Furthermore, perceived usefulness and perceived ease of use significantly influenced attitude toward using (R² = 0.676), and attitude toward using had a very strong effect on behavioral intention (R² = 0.614). These findings demonstrate that the interplay between cognitive load and perceptions of technology shapes students’ acceptance of the mobile robotic training kit. Recommendations for Practitioners: Teachers and educational media developers need to design robotics training kits that are structured, intuitive, and aligned with vocational competencies to reduce unnecessary cognitive load and enhance perceptions of ease of use and utility. Learning should also be structured progressively, moving from basic concepts to more complex projects, so that students can use the technology more effectively. Recommendation for Researchers: Future researchers are encouraged to continue developing models of educational technology acceptance by simultaneously incorporating cognitive, pedagogical, and other individual factors. Additionally, further studies could compare this model with other learning technologies or across different educational levels to broaden the generalizability of the findings. Impact on Society: The findings of this study have significant implications for educators regarding the design of mobile robot training kits that take cognitive load into account, particularly by reducing unnecessary cognitive load through simplified interfaces, integrated representations, and the elimination of redundant information, while optimizing relevant cognitive load through scaffolding, guided exploration, and repetitive problem-solving tasks. The application of a systematic learning framework, ranging from foundational knowledge to the integration of complex systems, is crucial for supporting students’ understanding and thereby enhancing perceived ease of use, perceived usefulness, and, ultimately, students’ acceptance of robotics technology in vocational education. Future Research: Given the limitations of this study, the researcher offers several recommendations for future research. First, this model needs to be tested on a larger and more diverse sample with similar learning characteristics, such as robotics, microcontrollers, or embedded systems, at the vocational high school level. Second, the model should be expanded to include other pedagogical, individual, and technological variables to gain a more comprehensive understanding of the assessment of the acceptance of mobile robotic training kits in vocational education.