Investigating Pre-Service Teachers’ Behavioral Intentions to Use Web 2.0 Gamification Tools

Year 2022, Volume: 9 Issue: 4, 172 - 189, 01.07.2022

Zeynep Turan , Sevda Küçük , Sinem Karabey

https://doi.org/10.17275/per.22.85.9.4

Cited By: 7

Abstract

This study aimed to investigate the pre-service teachers’ behavioural intentions about using gamification tools and the critical factors affecting their usage. The data were collected from 313 pre-service teachers from two large-scale universities in Turkey through a questionnaire with seven constructs: perceived ease of use, usefulness, self-efficacy, enjoyment, computer anxiety, attitude, and behavioural intention. Firstly, students were trained on the gamification method and Web 2.0 gamification tools (Kahoot!, Classdojo, and Jeopardylabs), then data were collected through a questionnaire. This study used the Technology Acceptance Model as a research framework. The data were analyzed by Structural Equation Modeling. The results showed that perceived self-efficacy and attitude factors had significant direct effects on pre-service teachers’ behavioural intentions to use gamification tools. Furthermore, the perceived enjoyment and usefulness factors significantly affected pre-service teachers’ attitudes towards using gamification tools. Additionally, the perceived self-efficacy and attitude factors had significant direct effects on perceived enjoyment to use gamification tools. Moreover, indirect effects on the dependent variables were revealed. Eventually, six constructs accounted for 75% of the variance for intention to use gamification tools. As a result, the research model appeared to have a good fit. Based on the findings within the scope of this study, various suggestions for researchers and practitioners were presented.

Keywords

Gamification, gamification tools, teacher education, pre-service teachers, structural equation modeling

References

• Abdel-Maksoud, N.F. (2018). The relationship between students' satisfaction in the LMS" Acadox" and their perceptions of its usefulness, and ease of use. Journal of Education and Learning, 7(2), 184-190.
• Adukaite, A., Zyl, I., Er, Ş., & Cantoni, L. (2017). Teacher perceptions on the use of digital gamified learning in tourism education: The case of South African secondary schools. Computers & Education, 111, 172-190.
• Anderson, S. E., & Maninger, R. M. (2007). Preservice teachers’ abilities, beliefs, and intentions regarding technology integration. Journal of Educational Computing Research, 37(2), 151-172.
• Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.
• Al-Adwan, A. S., Al-Madadha, A., & Zvirzdinaite, Z. (2018). Modeling students’ readiness to adopt mobile learning in higher education: An empirical study. International Review of Research in Open and Distributed Learning, 19(1), 221-241.
• Al-Haderi, S. M. S. (2013). The effect of self-efficacy in the acceptance of information technology in the public sector. International Journal of Business and Social Science, 4(9), 188-198.
• Alharbi, S., & Drew, S. (2014). Using the technology acceptance model in understanding academics’ behavioural intention to use learning management systems. International Journal of Advanced Computer Science and Applications, 5(1), 143-155.
• Asiri, M. J. (2019). Do teachers' attitudes, perception of usefulness, and perceived social influences predict their behavioral intentions to use gamification in EFL classrooms? Evidence from the Middle East. International Journal of Education and Practice, 7(3), 112-122.
• Baker, R. K., & White, K. M. (2010). Predicting adolescents’ use of social networking sites from an extended theory of planned behaviour perspective. Computers in Human Behavior, 26, 1591-1597.
• Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs.
• Başal, A., & Kaynak, N. E. (2020) Perceptions of pre-service English teachers towards the use of digital badges. Innovations in Education and Teaching International, 57(2), 148-162. doi: 10.1080/14703297.2019.1649172.
• Baumgartner, H., & Homburg, C. (1996). Applications of structural equation modeling in marketing and consumer research: A review. International Journal of Research in Marketing, 13(2), 139-161.
• Biesta, G., Priestley, M., & Robinson, S. (2015). The role of beliefs in teacher agency. Teachers and Teaching, 21(6), 624-640.
• Bingimlas, K. A. (2009). Barriers to the successful integration of ICT in teaching and learning environments: A review of the literature. Eurasia Journal of Mathematics, Science and Technology Education, 5(3), 235-245.
• Bock, G.-W., Zmud, R. W., Kim, Y.-G., & Lee, J.-N. (2005). Behavioral intention formation in knowledge sharing: Examining the roles of extrinsic motivators, social-psychological forces, and organizational climate. MIS Quarterly, 29(1), 87-111.
• Chaffin, A. J., & Harlow, S. D. (2005). Cognitive learning applied to older adult learners and technology. Educational Gerontology, 31(4), 301-329.
• Cheema, U., Rizwan, M., Jalal, R., Durrani, F., & Sohail, N. (2013). The trend of online shopping in 21st century: Impact of enjoyment in TAM Model. Asian Journal of Empirical Research, 3(2), 131-141.
• Cheng, Y. (2014). Exploring the intention to use mobile learning: The moderating role of personal innovativeness. Journal of Systems and Information Technology, 16(1), 40-61.
• Chintalapati, N., & Daruri, V. S. K. (2017). Examining the use of YouTube as a learning resource in higher education: Scale development and validation of TAM model. Telematics and Informatics, 34(6), 853-860.
• Choi, D., & Kim, J. (2004). Why people continue to play online games: In search of critical design factors to increase customer loyalty to online contents. CyberPsychology & Behavior, 7(1), 11-24.
• Chung, C. H., Shen, C., & Qiu, Y. Z. (2019). Students' acceptance of gamification in higher education. International Journal of Game-Based Learning, 9(2), 1-19.
• Chung, J., & Tan, F. B. (2004). Antecedents of perceived playfulness: an exploratory study on user acceptance of general information-searching websites. Information & Management, 41(7), 869-881.
• Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Erlbaum.
• Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189-211.
• Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
• 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.
• 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.
• Ekizoglu, N., & Ozcinar, Z. (2010). The relationship between the teacher candidates’ computer and internet based anxiety and perceived self-efficacy. Procedia-Social and Behavioral Sciences, 2(2), 5881-5890.
• Elmas, R., & Geban, Ö. (2012). Web 2.0 tools for 21st century teachers. International Online Journal of Educational Sciences, 4(1), 243-254.
• El Shamy, N., & Hassanein, K. (2017). A meta-analysis of enjoyment effect on technology acceptance: the moderating role of technology conventionality. In: Proceedings of the 50th Hawaii International Conference on System Sciences.
• Field, A. (2013). Discovering statistics using IBM SPSS statistics. sage.
• Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.
• Fontana, M. T. (2020). Gamification of ChemDraw during the COVID-19 pandemic: Investigating how a serious, educational-game tournament (Molecule Madness) impacts student wellness and organic chemistry skills while distance learning. Journal of Chemical Education, 97, 3358-3368.
• Gallego, M. D., Luna, P., & Bueno, S. (2008). User acceptance model of open source software. Computers in Human Behavior, 24(5), 2199-2216.
• Gibson, S. G., Harris, M. L., & Colaric, S. M. (2008). Technology acceptance in an academic context: Faculty acceptance of online education. Journal of Education for Business, 83(6), 355-359.
• Gong, M., Xu, Y., & Yu, Y. (2004). An enhanced technology acceptance model for web-based learning. Journal of Information Systems Education, 15(4), 365-374.
• Hamari, J. & J. Koivisto, (2013). Social motivations to use gamification: An empirical study of gamifying exercise. Paper Presented at the Proceedings of the 21st European Conference on Information Systems, Aalto, Finland.
• Heijden, H. (2003). Factors influencing the usage of website: The case of generic portal in the Netherlands. Information & Management, 40(4), 541-549.
• Hoyle, R. H. (1995). The structural equation modeling approach: Basic concepts and fundamental issues. CA: Sage publication, 1-15.
• Hsu, C. & Lu, H. (2007). Consumer behavior in online game community: A motivational factor perspective. Computers in Human Behavior, 23(3), 1642-1659.
• Huang, F., Teo, T., & Zhou, M. (2019). Factors affecting Chinese English as a foreign language teachers’ technology acceptance: A qualitative study. Journal of Educational Computing Research, 57(1), 83-105.
• Igbaria, M., Zinatelli, N., Cragg, P., & Cavaye, A. (1997). Personal computing acceptance factors in small firms: A structural equation model. MIS Quarterly, 21(3), 279-302.
• Joo, Y. J., Lim, K. Y., & Kim, N. H. (2016). The effects of secondary teachers’ technostress on the intention to use technology in South Korea. Computers & Education, 95, 114-122.
• Jung, Y., Peng, W., Moran, M., Jin, S. A. A., McLaughlin, M., Cody, M., ... & Silverstein, M. (2010). Low-income minority seniors' enrollment in a cybercafé: psychological barriers to crossing the digital divide. Educational Gerontology, 36(3), 193-212.
• Kao, C.-P. & Tsai, C.-C. (2009). Teachers’ attitudes toward web-based professional development, with relation to Internet self-efficacy and beliefs about web-based learning. Computers & Education, 53(1), 66-73.
• Ketelhut, D. J., & Schifter, C. C. (2011). Teachers and game-based learning: Improving understanding of how to increase efficacy of adoption. Computers & Education, 56(2), 539-546.
• Klem, L. (2000). Structural equation modeling. In L. Grimm & P. Yarnold (Eds.), Reading and understanding multivariate statistics (Vol. II). Washington, DC: American Psychological Association.
• Kline, R. B. (2011). Principles and practice of structural equation modeling. New York: The Guilford Press.
• Lai, H. M., & Chen, C. P. (2011). Factors influencing secondary school teachers’ adoption of teaching blogs. Computers & Education, 56(4), 948-960.
• Laguna, K., & Babcock, R. L. (1997). Computer anxiety in young and older adults: Implications for human-computer interactions in older populations. Computers in Human Behavior, 13(3), 317-326.
• Lee, W., Xiong, L., & Hu, C. (2012). The effect of Facebook users arousal and valence on intention to go to the festival: Applying an extension of the technology acceptance model. International Journal of Hospitality Management, 31(3), 819-827.
• Leng, G., & Lada, S. (2011). An Exploration of Social Networking Sites (SNS) Adoption in Malaysia Using Technology Acceptance Model (TAM), Theory of Planned Behavior (TPB) And Intrinsic Motivation. Journal of Internet Banking & Commerce, 16(2), 1-27.
• Leso, T., & Peck, K. L. (1992). Computer anxiety and different types of computer courses. Journal of Educational Computing Research, 8(4), 469-478.
• Lin, C.-P., and Bhattacherjee, A. (2008). Elucidating individual intention to use interactive information technologies: The role of network externalities. International Journal of Electronic Commerce, 13(1), 85-108.
• Malhotra, Y., Galletta, D. F., & Kirsch, L. J. (2008). How endogenous motivations influence user intentions: Beyond the dichotomy of extrinsic and intrinsic user motivations. Journal of Management Information Systems, 25(1), 267-300.
• Marangunić, N., & Granić, A. (2015). Technology acceptance model: A literature review from 1986 to 2013. Universal Access in the Information Society, 14(1), 81-95.
• Martí-Parreño, J., Galbis-Córdova, A., & Currás-Pérez, R. (2021). Teachers’ beliefs about gamification and competencies development: A concept mapping approach. Innovations in Education and Teaching International, 58(1), 84-94, doi: 10.1080/14703297.2019.1683464.
• Moon, J. W., & Kim, Y. G. (2001). Extending the TAM for a World-Wide-Web context. Information & Management, 38(4), 217-230.
• Mumtaz, S. (2000). Factors affecting teachers' use of information and communications technology: a review of the literature. Journal of Information Technology for Teacher Education, 9(3), 319-342.
• Newland, B., & Byles, N. (2014). Changing academic teaching with Web 2.0 technologies. Innovations in Education and Teaching International, 51(3), 315-325.
• O'Connor, D. L., & Menaker, E. S. (2008). Can massively multiplayer online gaming environments support team training?. Performance Improvement Quarterly, 21(3), 23-41.
• Okazaki, S., & Renda Dos Santos, L. (2012). Understanding e-learning adoption in Brazil: Major determinants and gender effects. The International Review of Research in Open and Distributed Learning, 13(4), 91-106.
• Ozdener, N. (2018). Gamification for enhancing Web 2.0 based educational activities: The case of pre-service grade school teachers using educational Wiki pages. Telematics and Informatics, 35(3), 564-578.
• Padilla-Melendez, A. D., Aguila-Obra, A. R., & Garrido-Moreno, A. (2013). Perceived playfulness, gender differences and technology acceptance model in a blended learning scenario. Computers & Education, 63, 306-317.
• Pektas, M., & Kepceoglu, I. (2019). What Do Prospective Teachers Think about Educational Gamification?. Science Education International, 30(1), 65-74.
• Pikkarainen, T., Pikkarainen, K., Karajaluoto, H., & Pahnila, S. (2004). Consumer acceptance of online banking: An extension of the technology acceptance model. Internet Research, 14(3), 224-235.
• Poong, Y. S., Yamaguchi, S., & Takada, J. (2017). Investigating the drivers of mobile learning acceptance among young adults in the World Heritage town of Luang Prabang, Laos. Information Development, 33(1), 57-71.
• Prensky, M. (2014). The world needs a new curriculum: It's time to lose the "proxies," and go beyond "21st century skills"—and get all students in the world to the real core of education. Educational Technology, 54(4), 3-15.
• Proctor, M., & Marks, Y. (2013). A survey of exemplar teachers’ perceptions, use, and access of computer-based games and technology for classroom instruction. Computers & Education, 62, 171-180.
• Rodrigues, L. F., Oliveira, A., & Costa, C. J. (2016). Playing seriously-How gamification and social cues influence bank customers to use gamified e-business applications. Computers in Human Behavior, 63, 392-407.
• Sadaf, A., Newby, T. J., & Ertmer, P. A. (2016). An investigation of the factors that influence preservice teachers’ intentions and integration of Web 2.0 tools. Educational Technology Research and Development, 64(1), 37-64.
• Sánchez-Mena, A., Martí-Parreño, J., & Miquel-Romero, M. J. (2019). Higher education instructors’ intention to use educational video games: An fsQCA approach. Educational Technology Research and Development, 67, 1455-1478.
• Saunders, E. J. (2004). Maximizing computer use among the elderly in rural senior centers. Educational Gerontology, 30(7), 573-585.
• 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.
• Shah, M. M., Hassan, R., & Embi, R. (2012, May). Technology acceptance and computer anxiety. In 2012 International Conference on Innovation Management and Technology Research (pp. 306-309). IEEE.
• Suki, N. M., & Suki, N. M. (2011). Exploring the relationship between perceived usefulness, perceived ease of use, perceived enjoyment, attitude and subscribers’ intention towards using 3G mobile services. Journal of Information Technology Management, 22(1), 1-7.
• Teo, T. (2009). The impact of subjective norm and facilitating conditions on pre-service teachers’ attitude toward computer use: A structural equation modeling of an extended technology acceptance model. Journal of Educational Computing Research, 40(1), 89-109.
• Venkatesh, V. (2000). Determinants of perceived ease of use: integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11, 342-365.
• Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273-315.
• Venkatesh, V., Morris, M., Davis, G., & Davis, F. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478.
• Van der Heijden, H. (2004). User acceptance of hedonic information systems. Management Information Systems Quarterly, 28(4), 695-704.
• Wang, H. Y., & Wang, Y. S. (2008). Gender differences in the perception and acceptance of online games. British Journal of Educational Technology, 39(5), 787-806.
• Wong, G. K. (2016). The behavioral intentions of Hong Kong primary teachers in adopting educational technology. Educational Technology Research and Development, 64(2), 313-338.
• Yang, Y., & Wang, X. (2019). Modeling the intention to use machine translation for student translators: An extension of technology acceptance model. Computers & Education, 133, 116-126.
• Yang, Y., Asaad, Y., & Dwivedi, Y. (2017). Examining the impact of gamification on intention of engagement and brand attitude in the marketing context. Computers in Human Behavior, 73, 459-469.
• Yoo, C., Kwon, S., Na, H., & Chang, B. (2017). Factors affecting the adoption of gamified smart tourism applications: An integrative approach. Sustainability, 9, 1-21.
• Yu, J., Ha, I., Choi, M., & Rho, J. (2005). Extending the TAM for a t-commerce. Information & Management, 42(7), 965-976.
• Yurdakul, I. K. (2011). Examining technopedagogical knowledge competencies of preservice teachers based on ICT usage. Hacettepe University Journal of Education, 40, 397-408.
• Zain, M., Rose, R. C., Abdullah, I., & Masrom, M. (2005). The relationship between information technology acceptance and organizational agility in Malaysia. Information & Management, 42(6), 829-839.
• Zhang, S., & Liu, Q. (2019). Investigating the relationships among teachers’ motivational beliefs, motivational regulation, and their learning engagement in online professional learning communities. Computers & Education, 134, 145-155.