Abstract
Based on the Technology Acceptance Model (TAM), the aims of the present cross-sectional study were i) to investigate acceptance by teachers of an open-source, collaborative, free m-learning app, named Artefac, ii) to examine whether teachers’ self-approach goals (i.e., the motivation to teach more effectively than before) may be a relevant external variable to include in the TAM, and iii) to investigate potential differences of acceptance between school subjects (humanities and social science teachers vs. science teachers), status (in-service teachers vs. pre-service teachers), and contexts (teachers in schools classified as difficult vs. teachers in schools not so classified). A total of 419 French teachers (277 women, 142 men) took part in the present correlational survey. After reading a text with pictures presenting an open-source, collaborative, free m-learning app, named Artefac, the participants filled out a self-reported questionnaire about its acceptance before use, assessing perceived usefulness for teaching, perceived ease of use, perceived enjoyment, and intention to use. Teachers’ self-approach goals were also assessed. One-sample t-tests and structural equation modeling were used to analyze the data. The results showed that Artefac was rather well accepted by teachers (with middle to strong effect sizes), whatever their school subject, their status, and their teaching context. The results also highlighted that teachers’ self-approach goals positively predicted perceived enjoyment and perceived ease of use but did not predict perceived usefulness for teaching, indicating that the more teachers wanted to increase their teaching skills, the more they found Artefac easy to use and fun to use.
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The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
References
Ahmed, S. N., & ur Rehman, S. (2021). An examination of students’ attitude towards the use of Google Classroom in preparatory year English program. Bulletin of Education and Research, 43(2), 39–59.
Al-Emran, M., Elsherif, H. M., & Shaalan, K. (2016). Investigating attitudes towards the use of mobile learning in higher education. Computers in Human Behavior, 56, 93–102. https://doi.org/10.1016/j.chb.2015.11.033
Al-Emran, M., Mezhuyev, V., & Kamaludin, A. (2018). Technology Acceptance Model in M-learning context: A systematic review. Computers & Education, 125, 389–412. https://doi.org/10.1016/j.compedu.2018.06.008
Alexandre, B., Reynaud, E., Osiurak, F., & Navarro, J. (2018). Acceptance and acceptability criteria: A literature review. Cognition, Technology & Work, 20(2), 165–177. https://doi.org/10.1007/s10111-018-0459-1
Alvarenga, C. E. A., Ginestié, J., & Brandt-Pomares, P. (2017). How and why Brazilian and French teachers use learning objects. Education and Information Technologies, 22(5), 1973–2000. https://doi.org/10.1007/s10639-016-9523-8
Asghar, M. Z., Barberà, E., & Younas, I. (2021). Mobile learning technology readiness and acceptance among pre-service teachers in Pakistan during the COVID-19 pandemic. Knowledge Management & ELearning, 13(1), 83–101. https://doi.org/10.34105/j.kmel.2021.13.005
Bakhsh, M., Mahmood, A., & Sangi, N. A. (2017). Examination of factors influencing students and faculty behavior towards m-learning acceptance: An empirical study. The International Journal of Information and Learning Technology, 34(3), 166–188. https://doi.org/10.1108/IJILT-08-2016-0028
Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming. Routledge.
Çetin, G., & Eren, A. (2022). Pre-service teachers’ achievement goal orientations, teacher identity, and sense of personal responsibility: The moderated mediating effects of emotions about teaching. Educational Research for Policy and Practice, 21(2), 245–283. https://doi.org/10.1007/s10671-021-09303-y
Chan, D. (2011). Advances in analytical strategies. In S. Zedeck (Ed.), APA handbook of industrial and organizational psychology (Vol. 1, pp. 85–113). American Psychological Association.
Chen, F. F. (2007). Sensitivity of goodness of fit indexes to lack of measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal, 14(3), 464–504. https://doi.org/10.1080/10705510701301834
Cheon, J., Sangno, L., Steven, M. C., & Jaeki, S. (2012). An investigation of mobile learning readiness in higher education based on the theory of planned behavior. Computers & Education, 59(3), 1054–1064. https://doi.org/10.1016/j.compedu.2012.04.015
Crompton, H., Burke, D., & Gregory, K. H. (2017). The use of mobile learning in PK-12 education: A systematic review. Computers & Education, 110, 51–63. https://doi.org/10.1016/j.compedu.2017.03.013
Curran, P. J., West, S. G., & Finch, J. F. (1996). The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis. Psychological Methods, 1(1), 16–29. https://doi.org/10.1037/1082-989x.1.1.16
da Silva, L. G., Neto, E. G. D. A., Francisco, R., Barbosa, J. L. V., Silva, L. A., & Leithardt, V. R. Q. (2021). Ulearnenglish: An open ubiquitous system for assisting in learning english vocabulary. Electronics, 10(14), 1692. https://doi.org/10.3390/electronics10141692
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319. https://doi.org/10.2307/249008
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22(14), 1111–1132. https://doi.org/10.1111/j.1559-1816.1992.tb00945.x
Dianati, S., Nguyen, M., Dao, P., Iwashita, N., & Vasquez, C. (2020). Student perceptions of technological tools for flipped instruction: The case of Padlet, Kahoot! and Cirrus. Journal of University Teaching & Learning Practice, 17(5), 4. https://doi.org/10.53761/1.17.5.4
Dunn, T. J., Baguley, T., & Brunsden, V. (2014). From alpha to omega: A practical solution to the pervasive problem of internal consistency estimation. British Journal of Psychology, 105(3), 399–412. https://doi.org/10.1111/bjop.12046
Elliot, A. J., & McGregor, H. A. (2001). A 2× 2 achievement goal framework. Journal of Personality and Social Psychology, 80(3), 501–519. https://doi.org/10.1037/0022-3514.80.3.501
Elliot, A. J., Murayama, K., & Pekrun, R. (2011). A 3×2 achievement goal model. Journal of Educational Psychology, 103(3), 632–648. https://doi.org/10.1037/a0023952
Everitt, B., & Hothorn, T. (2011). An introduction to applied multivariate analysis with R. Springer Science & Business Media.
Fornell, C., & Larcker, D. F. (1981). Structural Equation Models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 18(3), 382–388. https://doi.org/10.2307/3151312
Gikas, J., & Grant, M. M. (2013). Mobile computing devices in higher education: Student perspectives on learning with cellphones, smartphones & social media. The Internet and Higher Education, 19, 18–26. https://doi.org/10.1016/j.iheduc.2013.06.002
Gillet, N., Huyghebaert, T., Barrault, S., Bucourt, E., Gimenes, G., Maillot, A., ... & Sorel, O. (2017). Autonomous and controlled reasons underlying self-approach and self-avoidance goals and educational outcomes. Social Psychology of Education, 20(1), 179–193. https://doi.org/10.1007/s11218-017-9368-z
Granić, A., & Marangunić, N. (2019). Technology acceptance model in educational context: A systematic literature review. British Journal of Educational Technology, 50(5), 2572–2593. https://doi.org/10.1111/bjet.12864
Heradio, R., Chacon, J., Vargas, H., Galan, D., Saenz, J., De La Torre, L., & Dormido, S. (2018). Open-source hardware in education: A systematic mapping study. Ieee Access, 6, 72094–72103. https://doi.org/10.1109/access.2018.2881929
Hoareau, L., Thomas, A., Tazouti, Y., Dinet, J., Luxembourger, C., & Jarlégan, A. (2021). Beliefs about digital technologies and teachers’ acceptance of an educational app for preschoolers. Computers & Education, 172, 104264. https://doi.org/10.1016/j.compedu.2021.104264
Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
Huang, C. Y., Wang, H. Y., Yang, C. L., & Shiau, S. J. (2020). A derivation of factors influencing the diffusion and adoption of an open source learning platform. Sustainability, 12(18), 7532. https://doi.org/10.3390/su12187532
Huang, C., Wu, X., Wang, X., He, T., Jiang, F., & Yu, J. (2021). Exploring the relationships between achievement goals, community identification and online collaborative reflection. Educational Technology & Society, 24(3), 210–223.
Hulleman, C. S., Schrager, S. M., Bodmann, S. M., & Harackiewicz, J. M. (2010). A meta-analytic review of achievement goal measures: Different labels for the same constructs or different constructs with similar labels? Psychological Bulletin, 136(3), 422–449. https://doi.org/10.1037/a0018947
Impedovo, M. A., Touhami, F. S., & Brandt-Pomares, P. (2016). Educational technology in a French teacher training university: Teacher educators’ “voice.” International Journal of E-Learning & Distance Education, 31(1), 1–14.
In’nami, Y., & Koizumi, R. (2013). Review of sample size for structural equation models in second language testing and learning research: A Monte Carlo approach. International Journal of Testing, 13(4), 329–353. https://doi.org/10.1080/15305058.2013.806925
Islamoglu, H., Kabakci Yurdakul, I., & Ursavas, O. F. (2021). Pre-service teachers’ acceptance of mobile-technology-supported learning activities. Educational Technology Research and Development, 69(2), 1025–1054. https://doi.org/10.1007/s11423-021-09973-8
Jung, H. J. (2015). Fostering an English teaching environment: Factors influencing English as a foreign language teachers’ adoption of mobile learning. Informatics in Education-an International Journal, 14(2), 219–241. https://doi.org/10.15388/infedu.2015.13
Karahan, B. Ü. (2018). Examining the Relationship between the Achievement Goals and Teacher Engagement of Turkish Teachers. Journal of Education and Training Studies, 6(3), 101–107. https://doi.org/10.11114/jets.v6i3.2919
Kirschner, P. A., & De Bruyckere, P. (2017). The myths of the digital native and the multitasker. Teaching and Teacher Education, 67, 135–142. https://doi.org/10.1016/j.tate.2017.06.001
Lai, K.-W.W., & Smith, L. (2018). Socio-demographic factors relating to perception and use of mobile technologies in tertiary teaching. British Journal of Educational Technology, 49(3), 492–504. https://doi.org/10.1111/bjet.12544
Lai, H.-C., Chang, C.-Y., Wen-Shiane, L., Fan, Y.-L., & Wu, Y.-T. (2013). The implementation of mobile learning in outdoor education: Application of QR codes. British Journal of Educational Technology, 44(2), E57–E62. https://doi.org/10.1111/j.1467-8535.2012.01343.x
Liaw, S. S., Huang, H. M., & Chen, G. D. (2007). An activity-theoretical approach to investigate learners’ factors toward e-learning systems. Computers in Human Behavior, 23(4), 1906–1920. https://doi.org/10.1016/j.chb.2006.02.002
Lüftenegger, M., van de Schoot, R., Schober, B., Finsterwald, M., & Spiel, C. (2014). Promotion of students’ mastery goal orientations: Does TARGET work? Educational Psychology, 34, 451–469. https://doi.org/10.1080/01443410.2013.814189
Maican, C. I., Cazan, A. M., Lixandroiu, R. C., & Dovleac, L. (2019). A study on academic staff personality and technology acceptance: The case of communication and collaboration applications. Computers & Education, 128, 113–131. https://doi.org/10.1016/j.compedu.2018.09.010
Maphosa, V., Dube, B., & Jita, T. (2020). A UTAUT Evaluation of WhatsApp as a Tool for Lecture Delivery during the COVID-19 Lockdown at a Zimbabwean University. International Journal of Higher Education, 9(5), 84–93. https://doi.org/10.5430/ijhe.v9n5p84
Mascret, N., Elliot, A. J., & Cury, F. (2015). Extending the 3× 2 achievement goal model to the sport domain: The 3×2 Achievement Goal Questionnaire for Sport. Psychology of Sport and Exercise, 17, 7–14. https://doi.org/10.1016/j.psychsport.2014.11.001
Mascret, N., Elliot, A. J., & Cury, F. (2017). The 3× 2 achievement goal questionnaire for teachers. Educational Psychology, 37(3), 346–361. https://doi.org/10.1037/t61186-000
Mascret, N., Montagne, G., Devrièse-Sence, A., Vu, A., & Kulpa, R. (2022). Acceptance by athletes of a virtual reality head-mounted display intended to enhance sport performance. Psychology of Sport and Exercise, 61, 102201. https://doi.org/10.1016/j.psychsport.2022.102201
Mohammed, Q. A., Naidu, V. R., Hasan, R., Mustafa, M., & Jesrani, K. A. (2019). Digital education using free and open-source tools to enhance collaborative learning. International E-Journal of Advances in Education, 5(13), 50–57. https://doi.org/10.18768/ijaedu.531636
Moreira, F., Ferreira, M. J., Santos, C. P., & Durão, N. (2017). Evolution and use of mobile devices in higher education: A case study in Portuguese higher education institutions between 2009/2010 and 2014/2015. Telematics and Informatics, 34(6), 838–852. https://doi.org/10.1016/j.tele.2016.08.010
OECD. (2015). Students, computers, and learning: Making the connection. OECD Publishing. https://doi.org/10.1787/9789264239555-en
Peng, S. L., Cherng, B. L., & Chen, H. C. (2013). The effects of classroom goal structures on the creativity of junior high school students. Educational Psychology, 33(5), 540–560. https://doi.org/10.1080/01443410.2013.812616
Persico, D., Manca, S., & Pozzi, F. (2014). Adapting the technology acceptance model to evaluate the innovative potential of e-learning systems. Computers in Human Behavior, 30, 614–622. https://doi.org/10.1016/j.chb.2013.07.045
Phillips, D. L., & Clancy, K. J. (1972). Some effects of" social desirability" in survey studies. American Journal of Sociology, 77(5), 921–940. https://doi.org/10.1086/225231
Putnick, D. L., & Bornstein, M. H. (2016). Measurement invariance conventions and reporting: The state of the art and future directions for psychological research. Developmental Review, 41, 71–90. https://doi.org/10.1016/j.dr.2016.06.004
Pynoo, B., Devolder, P., Tondeur, J., Van Braak, J., Duyck, W., & Duyck, P. (2011). Predicting secondary school teachers’ acceptance and use of a digital learning environment: A cross-sectional study. Computers in Human Behavior, 27(1), 568–575. https://doi.org/10.1016/j.chb.2010.10.005
Racero, F. J., Bueno, S., & Gallego, M. D. (2020). Predicting students’ behavioral intention to use open-source software: A combined view of the technology acceptance model and self-determination theory. Applied Sciences, 10(8), 2711. https://doi.org/10.3390/app10082711
Sánchez-Mena, A., Martí-Parreño, J., & Aldás-Manzano, J. (2017). The effect of age on teachers’ intention to use educational video games: A TAM approach. Electronic Journal of e-Learning, 15(4), 355–366. https://doi.org/10.1080/14703297.2018.1433547
Sánchez-Prieto, J. C., Olmos-Migueláñez, S., & García-Peñalvo, F. J. (2016). Informal tools in formal contexts: Development of a model to assess the acceptance of mobile technologies among teachers. Computers in Human Behavior, 55, 519–528. https://doi.org/10.1016/j.chb.2015.07.002
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. https://doi.org/10.1016/j.chb.2016.09.061
Sánchez-Prieto, J. C., Hernández-García, Á., García-Peñalvo, F. J., Chaparro-Peláez, J., & Olmos-Migueláñez, S. (2019a). Break the walls! Second-Order barriers and the acceptance of mLearning by first-year pre-service teachers. Computers in Human Behavior, 95, 158–167. https://doi.org/10.1016/j.chb.2019.01.019
Sánchez-Prieto, J. C., Huang, F., Olmos-Migueláñez, S., García-Peñalvo, F. J., & Teo, T. (2019b). Exploring the unknown: The effect of resistance to change and attachment on mobile adoption among secondary pre-service teachers. British Journal of Educational Technology, 50(5), 2433–2449. https://doi.org/10.1111/bjet.12822
Sang, G., Valcke, M., Braak, J. V., & Tondeur, J. (2010). Student teachers’ thinking processes and ICT integration: Predictors of prospective teaching behaviors with educational technology. Computers & Education, 54(1), 103–112. https://doi.org/10.1016/j.compedu.2009.07.010
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
Tang, K. Y., Hsiao, C. H., Tu, Y. F., Hwang, G. J., & Wang, Y. (2021). Factors influencing university teachers’ use of a mobile technology-enhanced teaching (MTT) platform. Educational Technology Research and Development, 69(5), 2705–2728. https://doi.org/10.1007/s11423-021-10032-5
Teo, T., Lee, C. B., & Chai, C. S. (2008). Understanding pre-service teachers’ computer attitudes: Applying and extending the technology acceptance model. Journal of Computer Assisted Learning, 24(2), 128–143. https://doi.org/10.1111/j.1365-2729.2007.00247.x
Teo, T., Lee, C. B., Chai, C. S., & Wong, S. L. (2009). Assessing the intention to use technology among pre-service teachers in Singapore and Malaysia: A multigroup invariance analysis of the Technology Acceptance Model (TAM). Computers & Education, 53(3), 1000–1009. https://doi.org/10.1016/j.compedu.2009.05.017
UNESCO. (2016). Teacher’s guide on the prevention of violent extremism. Paris: UNESCO. Retrieved from https://www.casede.org/BibliotecaCasede/Novedades-PDF/UNESCO_Guia_educacion_contra_extremismo_violento.pdf
Van der Heijden, H. (2004). User acceptance of hedonic information systems. MIS Quarterly, 28(4), 695–704. https://doi.org/10.2307/25148660
Vangrieken, K., Dochy, F., Raes, E., & Kyndt, E. (2015). Teacher collaboration: A systematic review. Educational Research Review, 15, 17–40. https://doi.org/10.1016/j.edurev.2015.04.002
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. https://doi.org/10.2139/ssrn.4062395
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
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. https://doi.org/10.2307/30036540
Wolters, C. A. (2004). Advancing achievement goal theory: Using goal structures and goal orientations to predict students’ motivation, cognition, and achievement. Journal of Educational Psychology, 96, 236–250. https://doi.org/10.1037/0022-0663.96.2.236
Wu, K., Zhao, Y., Zhu, Q., Tan, X., & Zheng, H. (2011). A meta-analysis of the impact of trust on technology acceptance model: Investigation of moderating influence of subject and context type. International Journal of Information Management, 31(6), 572–581. https://doi.org/10.1016/j.ijinfomgt.2011.03.004
Zigarmi, D., Galloway, F. J., & Roberts, T. P. (2018). Work locus of control, motivational regulation, employee work passion, and work intentions: An empirical investigation of an appraisal model. Journal of Happiness Studies, 19(1), 231–256. https://doi.org/10.1007/s10902-016-9813-2
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This work was carried out within the pilot center Ampiric, funded by the French State’s Future Investment Program (PIA3/France 2030) operated by the Caisse des dépôts as part of the “Territories of Educational Innovation” action.
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Mascret, N., Marlin, K., Laisney, P. et al. Teachers’ acceptance of an open-source, collaborative, free m-learning app: The predictive role of teachers’ self-approach goals. Educ Inf Technol 28, 16373–16401 (2023). https://doi.org/10.1007/s10639-023-11832-3
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DOI: https://doi.org/10.1007/s10639-023-11832-3