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Analysis on Pre-Service Physics Teachers' Acceptance of a Microcomputer-Based Laboratory (MBL) Based on the Technology Acceptance Model
New Phys.: Sae Mulli 2018; 68: 217~224
Published online February 28, 2018;
© 2018 New Physics: Sae Mulli.

Sang-Won RHEE, Sungmin IM*

Department of Physics Education, Daegu University, Gyeongsan 38453, Korea
Correspondence to:
Received December 15, 2017; Revised January 6, 2018; Accepted January 6, 2018.
cc This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
This study analyzed the pre-service physics teachers' acceptance of a microcomputer-based laboratory (MBL) based on the technology acceptance model (TAM). In the TAM, the user's attitude toward using technology can be mediated by one's perceived ease of use and perceived usefulness, which, in turn, can be affected by external variables consisting of quality factors and a personal factor. In this study, we set system quality and information quality as the quality factors and self-efficacy on a computer as a personal factor. We constructed a model of the acceptance of a MBL based on the TAM, which included latent variables, such as system quality, information quality, self-efficacy on a computer, perceived ease of use, perceived usefulness, and behavioral intention to use the MBL; then, we measured such variables by using questionnaires. We analyzed the model by using partial least-squares structural equation modeling with the R package. As a result, we found that both system quality and self-efficacy on a computer affected the perceived ease of use while only system quality affected the perceived usefulness, which, in turn, affected the behavioral intention to use the MBL.
PACS numbers: 01.40.J-, 01.50.H-
Keywords: Microcomputer-based laboratory, Technology acceptance model, R package, Structural equation, Partial least-squares path modeling

February 2018, 68 (2)
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