Mobile Learning Article
Factors that influence behavioral intention to use mobile-based assessment: A STEM teachers’ perspective
Stavros A. Nikou,
Anastasios A. Economides,
Corresponding Author
Stavros A. Nikou
Address for correspondence: Dr Stavros Nikou, University of Macedonia, 156 Egnatia street, Thessaloniki 54636, Greece. Tel: 2310891768, Email: [email protected] and [email protected]Search for more papers by this authorAnastasios A. Economides
Search for more papers by this authorStavros A. Nikou,
Anastasios A. Economides,
Corresponding Author
Stavros A. Nikou
Address for correspondence: Dr Stavros Nikou, University of Macedonia, 156 Egnatia street, Thessaloniki 54636, Greece. Tel: 2310891768, Email: [email protected] and [email protected]Search for more papers by this authorAnastasios A. Economides
Search for more papers by this authorAbstract
Teachers' role can be catalytic in the introduction of innovative digital tools in order to create new learning and assessment opportunities. This study explores science technology engineering and mathematics (STEM) teachers' intention to use mobile-based assessments in the teaching practice. The study proposes the teachers' acceptance mobile-based assessment (TAMBA) model which extends the technology acceptance model by introducing individual, social, institutional and instructional design factors. An appropriate questionnaire was developed and answered by 161 STEM teachers from 32 European countries. Their responses were analyzed using structural equation modeling. The proposed TAMBA model explains about 50% of the variance in teachers' intention to adopt mobile-based assessment. Perceived Ease of Use was found to be the most important determinant in teachers' intention to use mobile-based assessment. Facilitating Conditions and Output Quality were the most influential external variables in the model. The study findings revealed that focusing on mobile assessment quality design as well as on institutional support are important factors for STEM teachers in order to accept mobile-based assessments in schools.
Supporting Information
| Filename | Description |
|---|---|
| bjet12609-sup-0001-supInfo.docxWord document, 17.5 KB | Fig S1. Questionnaire Items used |
Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
References
- Arpaci, I. (2015). A comparative study of the effects of cultural differences on the adoption of mobile learning. British Journal of Educational Technology, 46, 699–712.
- Baran, E. (2014). A review of research on mobile learning in teacher education. Educational Technology & Society, 17, 17–32.
- Baydas, O., & Yilmaz, R. M. (2018). Pre-service teachers' intention to adopt mobile learning: a motivational model. British Journal of Educational Technology, 49, 137–152. doi:10.1111/bjet.12521
- Bogdanovic, Z., Barac, D., Jovanic, B., Popovic, S., & Radenkovic, B. (2013). Evaluation of mobile assessment in a learning management system. British Journal of Educational Technology, 45, 231–244.
- Campbell, A., & Main, S. J. (2014). Performance, assessment and communication in one app: mobile tablet assessment is here to stay. eCULTURE, 7.
- Chen, C.-H. (2010). The implementation and evaluation of a mobile self- and peer-assessment system. Computers & Education, 55, 229–236.
- Cheung, W. S., & Hew, K. F. (2009). A review of research methodologies used in studies on mobile handheld devices in K-12 and higher education settings. Australasian Journal of Educational Technology, 25, 153–183.
- Chien, S.-P., Wu, H.-K., & Hsu, Y.-S. (2014). An investigation of teachers' beliefs and their use of technology-based assessments. Computers in Human Behavior, 31, 198–210.
- Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In G. A. Marcoulides (Ed.), Modern business research methods (pp. 295–336). Mahwah, NJ: Lawrence Erlbaum Associates.
- Chiu, T. K. F. (2017). Introducing electronic textbooks as daily-use technology in schools: a top-down adoption process. British Journal of Educational Technology, 48, 524–537.
- Chiu, T. K. F., & Churchill, D. (2016). Adoption of mobile devices in teaching: changes in teacher beliefs, attitudes and anxiety. Interactive Learning Environments, 24, 317–327.
- Chu, H. C., Hwang, G. J., Tsai, C. C., & Tseng, J. C. R. (2010). A two-tier test approach to developing location-aware mobile learning systems for natural science course. Computers & Education, 55, 1618–1627.
- Chung, H.-H.,
Chen, S.-C., &
Kuo, M.-H. (2015). A study of EFL college students' acceptance of mobile learning. Procedia - Social and Behavioral Sciences, 176, 333–339.
10.1016/j.sbspro.2015.01.479 Google Scholar
- Cochrane, T., & Narayan, V. (2017). Design considerations for mobile learning. In C. Reigeluth, B. J. Beatty, & R. Myers (Eds), Instructional-design theories and models (pp. 385–414). Abingdon, UK: Routledge.
- Cohen, J. (1988). Statistical power analysis for the behavioural sciences ( 2nd ed.). Hillsdale, NJ: Erlbaum.
- Compeau, D., & Higgins, C. (1995). Computer self-efficacy: development of a measure and initial test. MIS Quarterly, 19, 189–211.
- Coulby, C., Hennessey, S., Davie, N., & Fuller, R. (2010). The use of mobile technology for work-based assessment: the student experience. British Journal of Educational Technology, 42, 251–265.
- Davis, F. D. (1989). Perceived usefulness, perceived ease of use and user acceptance of information technology. MIS Quarterly, 13, 319–340.
- Elliot, S., & Mikulas, C. (2012). Improving student learning through teacher technology training: a study of the effectiveness of technology integration training on student achievement. In P. Resta (Eds), Proceedings of society for information technology & teacher education international conference 2012 (pp. 1759–1766). Chesapeake, VA: AACE.
- El-Gayar, O., Moran, M., & Hawkes, M. (2011). Students' acceptance of Tablet PCs and implications for educational institutions. Educational Technology and Society, 14, 58–70.
- Farrell, T., & Rushby, N. (2016). Assessment and learning technologies: an overview. British Journal of Educational Technology, 47, 106–120.
- Gao, Q., Yan, Z., Wei, C., Liang, Y., & Mo, L. (2017). Three different roles, five different aspects: differences and similarities in viewing school mobile phone policies among teachers, parents, and students. Computers & Education, 106, 13–25.
- Hu, H., & Garimella, U. (2014). iPads for STEM teachers: a case study on perceived usefulness, perceived proficiency, intention to adopt and integration in K-12 instruction. Journal of Educational Technology Development and Exchange (JETDE), 7, 49–66.
- Hwang, G. J., & Chang, H. F. (2011). A formative assessment-based mobile learning approach to improving the learning attitudes and achievements of students. Computers & Education, 56, 1023–1031.
- Johnson, L., Becker, S. A., Cummins, M., Estrada, V., Freeman, A. & Hall, C. (2016). NMC horizon report: 2016 higher education edition. Austin, TX: The New Media Consortium.
- Kim, C., Kim, M. K., Lee, C., Spector, J. M., & DeMeester, K. (2013). Teacher beliefs and technology integration. Teaching and Teacher Education, 29, 76–85.
- King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information & Management, 43, 740–755.
- Liu, Y., Li, H., & Carlsson, C. (2010). Factors driving the adoption of m-learning: an empirical study. Computers & Education, 55, 1211–1219.
- Lu, H., Hu, Y-P., Gao, J-J., & Kinshuk, (2016). The effects of computer self-efficacy, training satisfaction and test anxiety on attitude and performance in computerized adaptive testing. Computers & Education, 100, 45–55. Retrieved from https://doi.org/10.1016/j.compedu.2016.04.012.
- Mac Callum, K.,
Jeffrey, L., & Kinshuk, (2014). Factors impacting teachers' adoption of mobile learning. Journal of Information Technology Education Research, 13, Retrieved from http://www.jite.org/documents/Vol13/JITEv13ResearchP141-162MacCallum0455.pdf
10.28945/1970 Google Scholar
- Miranda, H. P., & Russell, M. (2012). Understanding factors associated with teacher-directed student use of technology in elementary classrooms: a structural equation modeling approach. British Journal of Educational Technology, 43, 652–666.
- Nikou, S. A., & Economides, A. A. (2013). Mobile assessment: state of the art. In Z. L. Berge & L. Y. Muilenburg (Eds), Handbook of mobile learning (pp. 346–355). Florence, NY: Routledge.
- Nikou, S. A., & Economides, A. A. (2017). Mobile-based assessment: investigating the factors that influence behavioral intention to use. Computers & Education, 109, 56–73. Retrieved from https://doi.org/10.1016/j.compedu.2017.02.005
- Nistor, N., Gogus, A., & Lerche, T. (2013). Educational technology acceptance across national and professional cultures: a European study. Education Technology Research Development, 61, 33–749.
- O'Bannon, B. W., & Thomas, K. (2014). Teacher perceptions of using mobile phones in the classroom: age matters! Computers & Education, 74, 15–25.
- Park, S. Y., Nam, M.-W., & Cha, S.-B. (2012). University students' behavioral intention to use mobile learning: evaluating the technology acceptance model. British Journal of Educational Technology, 43, 592–605.
- Ringle, C. M., Wende, S., & Will, A. (2005). SmartPLS 2.0 (beta), [computer software]. Retrieved from http://www.smartpls.de
- Sánchez-Prieto, J. C., Olmos-Migueláñez, S., & García-Peñalvo, F. J. (2017). MLearning and pre-service teachers: an assessment of the behavioral intention using an expanded TAM model. Computers in Human Behavior, 72, 644–654. Retrieved from https://doi.org/10.1016/j.chb.2016.09.061.
- Scherer, R., Siddiq, F., & Teo, T. (2015). Becoming more specific: measuring and modeling teachers' perceived usefulness of ICT in the context of teaching and learning. Computers & Education, 88, 202–214.
- Stowell, J. R. (2015). Use of clickers vs. mobile devices for classroom polling. Computers & Education, 82, 329–334.
- Šumak, B., Hericko, M., & Pušnik, 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.
- Sung, Y.-T., Chang, K.-E., & Liu, T.-C. (2016). The effects of integrating mobile devices with teaching and learning on students' learning performance: a meta-analysis and research synthesis. Computers & Education, 94, 252–275.
- Tarhini, A., Hone, K., & Liu, X. (2015). A cross-cultural examination of the impact of social, organisational and individual factors on educational technology acceptance between British and Lebanese university students. British Journal of Educational Technology, 46, 739–755. doi:10.1111/bjet.12169
- Thomas, K. M.,
O'bannon, B. W., &
Britt, V. G. (2014). Standing in the schoolhouse door: teacher perceptions of mobile phones in the classroom. Journal of Research on Technology in Education, 46, 373–395.
10.1080/15391523.2014.925686 Google Scholar
- UNESCO IITE, Institute for Information Technologies in Education (2012). Policy brief: technology enhanced assessment in education. Moscow: Russian Federation.
- Uzunboylu, H., & Özdamli, F. (2011). Teacher perception for m-learning: scale development and teachers' perceptions. Journal of Computer Assisted Learning, 27, 544–556.
- Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39, 273–315.
- Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: four longitudinal field studies. Management Science, 46, 186–204.
- Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: toward a unified view. MIS Quarterly, 27, 425–478.
- Wang, Y.-S., Wu, M.-C., & Wang, H.-Y. (2009). Investigating the determinants and age and gender differences in the acceptance of mobile learning. British Journal of Educational Technology, 40, 92–118.
- West, M. (2012). Mobile learning for teachers: global themes. Paris: UNESCO.
- Wu, W.-H., Wu, Y.-C. J., Chen, C. Y., Kao, H.-Y., Lin, C.-H., & Huang, S.-H. (2012). Review of trends from mobile learning studies: a meta-analysis. Computers & Education, 59, 817–827.
