Students’ acceptance of mobile-based assessment
Abstract
The effective development of a Mobile-Based Assessment (MBA) depends on students’ acceptance. The aim of this paper was to examine the determinant factor of students’ behavioral intention to use mobile-based assessment. Data were collected from 105 second grade students of a vocational high school through an online survey questionnaire. Partial Least Squares (PLS) was used to test the measurement and the structural model. Results showed Perceived Ease of Use as the strongest direct predictor of Behavioral Intention to Use, followed by Perceived Usefulness. Content and Mobile Self-Efficacy only has an indirect effect. These four variables explain 51.3 percent of the variance of Behavioral Intention to Use.
References
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Fishbein, M. (1979). A theory of reasoned action: Some applications and implications. Nebraska Symposium on Motivation, 27, 65–116.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equations models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.
Fuad, M., Deb, D., Etim, J., & Gloster, C. (2018). Mobile response system: A novel approach to interactive and hands-on activity in the classroom. Educational Technology Research and Development, 66, 493–514. https://doi.org/10.1007/s11423-018-9570-5
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Ringle, C. M., Wende, Sven, & Becker, J.-M. (2015). SmartPLS 3. Bönningstedt: SmartPLS. Retrieved from http://www.smartpls.com
Roschelle, J., Rafanan, K., Bhanot, R., & Claro, S. (2010). Scaffolding group explanation and feedback with handheld technology : Impact on students’ mathematics learning. Educational Technology Research & Development, 58, 399–419. https://doi.org/10.1007/s11423-009-9142-9
Rosenberg, J. M., & Koehler, M. J. (2015). Context and Technological Pedagogical Content Knowledge (TPACK): A Systematic Review. Journal of Research on Technology in Education, 47(3), 186–210. https://doi.org/10.1080/15391523.2015.1052663
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Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers ’ adoption of digital technology in education. Computers & Education, 128, 13–35. https://doi.org/10.1016/j.compedu.2018.09.009
Teo, T. (2009). Modelling technology acceptance in education: A study of pre-service teachers. Computers & Education, 52(2), 302–312. https://doi.org/https://doi.org/10.1016/j.compedu.2008.08.006
Terzis, V., & Economides, A. A. (2011). The acceptance and use of computer based assessment. Computers & Education, 56(4), 1032–1044. https://doi.org/10.1016/j.compedu.2010.11.017
Terzis, V., Moridis, C. N., & Economides, A. A. (2013). Continuance acceptance of computer based assessment through the integration of user ’ s expectations and perceptions. Computers & Education Journal, 62, 50–61. https://doi.org/10.1016/j.compedu.2012.10.018
Van Raaij, E., & Schepers, J. (2008). The acceptance and use of virtual learning environment in China. Computers & Education, 50, 838–852. https://doi.org/10.1016/j.compedu.2006.09.001
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425–478.
Wang, A. I. (2015). The wear out effect of a game-based student response system *. Computers & Education, 82, 217–227. https://doi.org/10.1016/j.compedu.2014.11.004
Ajzen, I., & Madden, T. J. (1986). Prediction of Goal-Directed Behavior : Attitudes , Intentions , and Perceived Behavioral Control. Journal of Experimental Social Psychology, 474, 453–474.
Alioon, Y., & Delialio glu, €Omer. (2017). The effect of authentic m-learning activities on student engagement and motivation. British Journal of Educational Technology, 00(00). https://doi.org/10.1111/bjet.12559
Barclay, D., Higgins, C., & Thompson, R. (1995). The partial least squares approach to causal modelling: personal computer adoption and use as an illustration. Technology Studies, 2, 285–309.
Bogdanović, Z., Barać, D., Jovanić, B., Popović, S., & Radenković, B. (2014). Evaluation of mobile assessment in a learning management system. British Journal of Educational Technology, 45(2), 231–244. https://doi.org/10.1111/bjet.12015
Brinke, D. J., van Bruggen, J., Hermans, H., Burgers, J., Giesbers, B., Koper, R., & Latour, I. (2007). Modeling assessment for re-use of traditional and new types of assessment. Computers in Human Behavior, 23, 2721–2741.
Chen, C.-M., & Chen, M.-C. (2009). Mobile formative assessment tool based on data mining techniques for supporting web-based learning. Computers & Education, 52(1), 256–273. Retrieved from https://www.learntechlib.org/p/67119
Chen, K., Chen, J. V, & Yen, D. C. (2011). Dimensions of self-efficacy in the study of smartphone acceptance. Computer Standards & Interfaces, 33(4), 422–431. https://doi.org/10.1016/j.csi.2011.01.003
Chin, W. W. (2014). The Partial Least Squares Approach to Structural Equation Modeling, (April).
Chin, W. W., & Todd, P. A. (1995). On the Use, Usefulness, and Ease of Use of Structural Equation Modeling in MIS Research: A Note of Caution. MIS Quarterly, 19(2), 237–246. https://doi.org/10.2307/249690
Chou, P., Chang, C., & Lin, C. (2017). BYOD or not : A comparison of two assessment strategies for student learning. Computers in Human Behavior, 74, 63–71. https://doi.org/10.1016/j.chb.2017.04.024
Chu, H., Hwang, G., Tsai, C., & Tseng, J. C. R. (2010). A two-tier test approach to developing location-aware mobile learning systems for natural science courses. Computers & Education, 55(4), 1618–1627. https://doi.org/10.1016/j.compedu.2010.07.004
Compeau, D. R., & Higgins, C. A. (1995). Computer Self-Efficacy: Development of a Measure and Initial Test. MIS Quarterly, 19(2), 189–211. https://doi.org/10.2307/249688
Dalby, D., & Swan, M. (2018). Using digital technology to enhance formative assessment in mathematics classrooms. British Journal of Educational Technology, 00(00). https://doi.org/10.1111/bjet.12606
Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results. Massachusetts Institute of Technology.
Davis, F. D. (1989a). Perceived Usefulness , Perceived Ease of Use , and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
Davis, F. D. (1989b). User Acceptance of Computer Technology : A Comparison of Two Theoretical Models. Management Science, 35, 982–1003. https://doi.org/10.1287/mnsc.35.8.982
Diseko, R., & Modiba, W. (2016). Learners’ Experiences of an Authentic Online Assessment Test in Understanding of Basic Accounting Content Knowledge : A Case Study. Proceedings of ADVED 2016 2nd International Conference on Advances in Education and Social Sciences, (October), 339–350.
Fidenia s.r.l. (2020). Quizzez and Surveys for Everyone. Retrieved from https://www.questbase.com/product/
Fishbein, M. (1979). A theory of reasoned action: Some applications and implications. Nebraska Symposium on Motivation, 27, 65–116.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equations models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.
Fuad, M., Deb, D., Etim, J., & Gloster, C. (2018). Mobile response system: A novel approach to interactive and hands-on activity in the classroom. Educational Technology Research and Development, 66, 493–514. https://doi.org/10.1007/s11423-018-9570-5
Hair Jr, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications.
Kuo-hung, C., Kuo-en, C., Chung-hsien, L., & Yao-ting, S. (2016). Integration of Mobile AR Technology in Performance Assessment. Educational Technology & Society, 19(4), 239–251.
Landry, B. J. L., Griffeth, R., & Hartman, S. (2006). Measuring Student Perceptions of Blackboard Using the Technology Acceptance Model. Decision Sciences Journal of Innovative Education, 4(1), 87–99. https://doi.org/10.1111/j.1540-4609.2006.00103.x
Nikou, S. A., & Economides, A. A. (2015). The impact of paper-based , computer-based and mobile-based self- assessment on students ’ science motivation and achievement. Computers in Human Behavior, 1–8. https://doi.org/10.1016/j.chb.2015.09.025
Nikou, S. A., & Economides, A. A. (2017a). Mobile-Based Assessment : Integrating acceptance and motivational factors into a combined model of Self-Determination Theory and Technology Acceptance. Computers in Human Behavior, 68, 83–95. https://doi.org/10.1016/j.chb.2016.11.020
Nikou, S. A., & Economides, A. A. (2017b). Mobile-based assessment : Investigating the factors that in fl uence behavioral intention to use. Computers & Education, 109, 56–73. https://doi.org/10.1016/j.compedu.2017.02.005
Ong, C., Lai, J., & Wang, Y. (2004). Factors affecting engineers ’ acceptance of asynchronous e-learning systems in high-tech companies, 41, 795–804. https://doi.org/10.1016/j.im.2003.08.012
Padilla-Meléndez, A., Garrido-Moreno, A., & Del Aguila-Obra, A. R. (2008). Factors affecting e-collaboration technology use among management students. Computers & Education, 51, 609–623. https://doi.org/10.1016/j.compedu.2007.06.013
Pušnik, M., Šumak, B., & Heric, M. (2011). A meta-analysis of e-learning technology acceptance : The role of user types and e-learning technology types. Computers in Human Behavior, 27, 2067–2077. https://doi.org/10.1016/j.chb.2011.08.005
Ringle, C. M., Wende, Sven, & Becker, J.-M. (2015). SmartPLS 3. Bönningstedt: SmartPLS. Retrieved from http://www.smartpls.com
Roschelle, J., Rafanan, K., Bhanot, R., & Claro, S. (2010). Scaffolding group explanation and feedback with handheld technology : Impact on students’ mathematics learning. Educational Technology Research & Development, 58, 399–419. https://doi.org/10.1007/s11423-009-9142-9
Rosenberg, J. M., & Koehler, M. J. (2015). Context and Technological Pedagogical Content Knowledge (TPACK): A Systematic Review. Journal of Research on Technology in Education, 47(3), 186–210. https://doi.org/10.1080/15391523.2015.1052663
Salisbury, W. D., Chin, W. W., Gopal, A., & Newsted, P. R. (2002). Research Report: Better Theory Through Measurement—Developing a Scale to Capture Consensus on Appropriation. Information Systems Research, 13(1), 91–103. Retrieved from http://www.jstor.org/stable/23015825
Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers ’ adoption of digital technology in education. Computers & Education, 128, 13–35. https://doi.org/10.1016/j.compedu.2018.09.009
Teo, T. (2009). Modelling technology acceptance in education: A study of pre-service teachers. Computers & Education, 52(2), 302–312. https://doi.org/https://doi.org/10.1016/j.compedu.2008.08.006
Terzis, V., & Economides, A. A. (2011). The acceptance and use of computer based assessment. Computers & Education, 56(4), 1032–1044. https://doi.org/10.1016/j.compedu.2010.11.017
Terzis, V., Moridis, C. N., & Economides, A. A. (2013). Continuance acceptance of computer based assessment through the integration of user ’ s expectations and perceptions. Computers & Education Journal, 62, 50–61. https://doi.org/10.1016/j.compedu.2012.10.018
Van Raaij, E., & Schepers, J. (2008). The acceptance and use of virtual learning environment in China. Computers & Education, 50, 838–852. https://doi.org/10.1016/j.compedu.2006.09.001
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425–478.
Wang, A. I. (2015). The wear out effect of a game-based student response system *. Computers & Education, 82, 217–227. https://doi.org/10.1016/j.compedu.2014.11.004
Authors
Saputri, N. A. K., Abdullah, A. G. ., & Hakim , D. L. . (2021). Students’ acceptance of mobile-based assessment. Momentum: Physics Education Journal, 5(1), 43–52. https://doi.org/10.21067/mpej.v5i1.4575
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