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A Study on the Factors Affecting Student Behavior Intention to Attend Robotics Courses at the Primary and Secondary School Levels

Authors: Jingwen Shan


In order to explore the key factors affecting the robot program learning intention of school students, this study takes the technology acceptance model as the theoretical basis and invites 167 students from Jiading District of Shanghai as the research subjects. In the robot course, the model of school students on their learning behavior is constructed. By verifying the causal path relationship between variables, it is concluded that teachers can enhance students’ perceptual usefulness to robotics courses by enhancing subjective norms, entertainment perception, and reducing technical anxiety, such as focusing on the gradual progress of programming and analyzing learner characteristics. Students can improve perceived ease of use by enhancing self-efficacy. At the same time, robot hardware designers can optimize in terms of entertainment and interactivity, which will directly or indirectly increase the learning intention of the robot course. By changing these factors, the learning behavior of primary and secondary school students can be more sustainable.

Keywords: TAM, primary and secondary school students, learning behavior intentions, robot courses

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[1] Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
[2] Venkatesh, V. (2000). Determinants of perceived ease of use: integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342-365.
[3] Kandlhofer, M., & Steinbauer, G. (2016). Evaluating the impact of educational robotics on pupils’ technical- and social-skills and science related attitudes. Robotics and Autonomous Systems, 75, 679-685.
[4] Venkatesh, V., & Bala, H. (2010). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273-315.
[5] Wu Minglong (2010). Structural equation model: The operation and application of AMOS. Chongqing: Chongqing University Press.
[6] Rong Taisheng (2009). AMOS and research methods. Chongqing: Chongqing University Press.
[7] Igbaria, M., Zinatelli, N., & Cragg, P. (1997). Personal computing acceptance factors in small firms: a structural equation model. Mis Quarterly, 21(3), 279-305.
[8] Hui, C., Law, K. S., & Chen, Z. X. (1999). A structural equation model of the effects of negative affectivity, leader-member exchange, and perceived job mobility on in-role and extra-role performance: a Chinese case. Organizational Behavior & Human Decision Processes, 77(1), 3.
[9] Fathema, N., Shannon, D., & Ross, M., (2015). Expanding the Technology Acceptance Model (TAM) to examine faculty use of Learning Management Systems (LMS). Journal of Online Learning and Teaching.11(2),210-233.
[10] Lunceford, Brett. (2009). “Reconsidering Technology Adoption and Resistance: Observations of a Semi-Luddite.” Explorations in Media Ecology, 8 (1), 29-47.