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Does the student's perspective on multimodal literacy influence their behavioural intention to use collaborative computer-based learning?

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Abstract

The support for the use of Computer-Supported Collaborative Learning is a sign of contributions and support for in-class and out-of-class learning. This study investigates the perspective of student’s digital multi-modal literacy on student's behavioral intention to use a CSCL. We proposed a theoretical model that examines student perspectives on the integration of digital multi-modal literacy in the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The study empirically examined and validated the proposed theoretical model based on a digital multi-modal computer-supported collaborative learning adoption. The data were analyzed with a partial least square, structural equation modeling (PLS-SEM) statistical approach. Results suggest that student's perspective on multi-modal literacy has a positive and significant impact on the behavioral intention to use. Furthermore, all the UTAUT factors have a strong and significant impact on the behavioral intention to support the use of digital multi-modal computer-supported collaborative learning. Therefore, student's perspective on multi-modal literacy contributes towards their behavioural intention to use collaborative computer-based learning. To further improve collaboration and communication based on the use of CSCL to support students learning environment.

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References

  • Abubakar, A. M., & Adeshola, I. (2019). Digital exam and assessments: A riposte to industry 4.0. In Handbook of Research on Faculty Development for Digital Teaching and Learning (pp. 245–263). IGI Global.

  • Ajzen, I. (1991). The theory of planned behaviour. Organizational Behaviour and Human Decision Processes, 50(2), 179–211.

    Article  Google Scholar 

  • Ajzen, I. (2001). Nature and operation of attitudes. Annual Review of Psychology, 52(1), 27–58.

    Article  Google Scholar 

  • Ali, F., Nair, P. K., & Hussain, K. (2016). An assessment of students’ acceptance and usage of computer supported collaborative classrooms in hospitality and tourism schools. Journal of Hospitality, Leisure, Sport and Tourism Education, 18, 51–60.

    Article  Google Scholar 

  • Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411.

    Article  Google Scholar 

  • Bagozzi, R. P. (1981). Attitudes, intentions, and behavior: A test of some key hypotheses. Journal of Personality and Social Psychology, 41(4), 607.

    Article  Google Scholar 

  • Belo, N., McKenney, S., Voogt, J., & Bradley, B. (2016). Teacher knowledge for using technology to foster early literacy: A literature review. Computers in Human Behaviour, 60, 372–383.

    Article  Google Scholar 

  • Bridgman, T. (2020). Overcoming compliance to change: Dynamics of power, obedience, and resistance in a classroom restructure. Management Teaching Review, 5(1), 32–40.

    Article  Google Scholar 

  • Carpenter, J., Moore, M., Doherty, A. M., & Alexander, N. (2012). Acculturation to the global consumer culture: A generational cohort comparison. Journal of Strategic Marketing, 20(5), 411–423.

    Article  Google Scholar 

  • Chang, C. T., Hajiyev, J., & Su, C. R. (2017). Examining the students’ behavioural intention to use e-learning in Azerbaijan? The general extended technology acceptance model for e-learning approach. Computers and Education, 111, 128–143.

    Article  Google Scholar 

  • Cheung, R., & Vogel, D. (2013). Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning. Computers & education, 63, 160-175.

  • Chen, J., Wang, M., Kirschner, P. A., & Tsai, C. C. (2018). The role of collaboration, computer use, learning environments, and supporting strategies in CSCL: A meta-analysis. Review of Educational Research, 88(6), 799–843.

    Article  Google Scholar 

  • Chi, M. T. (2009). Active-constructive-interactive: A conceptual framework for differentiating learning activities. Topics in Cognitive Science, 1(1), 73–105.

    Article  Google Scholar 

  • Chin, W. W. (1988). The partial least squares approach to structural equation modeling. Modern Methods for Business Research, 295(2), 295–336.

    MathSciNet  Google Scholar 

  • Chuang, T. T., Bernard, M., & Ali, S. I. (2002). Computer-supported collaborative learning performance and satisfaction: A muiti-stage study. Journal of International Information Management, 11(1), 2.

    Google Scholar 

  • Dillenbourg, P. (2011). Trends in orchestration. Second research and technology scouting report. STELLAR NoE Deliverable, D1.5  Available online at http://goo.gl/6G5g3E. Accessed 04 .04. 2020.

  • Dimitriadis, Y. A. (2012). Supporting teachers in orchestrating CSCL classrooms. In Research on E-Learning and ICT in Education (pp. 71–82). Springer.

  • Dillenbourg, P., Järvelä, S., & Fischer, F. (2009). The evolution of research on computer-supported collaborative learning. In Technology-enhanced learning (pp. 3-19). Springer, Dordrecht.

  • Dyehouse, M., Weber, N., Fang, J., Harris, C., David, R., Hua, I., & Strobel, J. (2017). Examining the relationship between resistance to change and undergraduate engineering students’ environmental knowledge and attitudes. Studies in Higher Education, 42(2), 390–409.

    Article  Google Scholar 

  • Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.

  • Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics.

  • Godin, J. J., & Leader, L. F. (2013). Factors influencing the acceptance of collaboration technology within the context of virtual teamwork training. Paper presented at the International Conference on Educational Technologies, Malaysia. Retrieved from https://files.eric.ed.gov/fulltext/ED557177.pdf.

  • Hair, J. F., Anderson Jr, R. E., Tatham, R. L., & Black, W. C. (2010). Multivariate data analysis (7th ed.). Global Edition.

  • Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2014). A primer on partial least squares structural equation modeling (PLS-SEM). Thousand Oaks: Sage.

  • Henseler, J. (2017a). Bridging design and behavioral research with variance-based structural equation modeling. Journal of Advertising, 46(1), 178–192.

    Article  Google Scholar 

  • Henseler, J. (2017b). Adanco 2.0. 1-User manual. Kleve: Composite Modeling GmbH and Co.

  • Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: Updated guidelines. Industrial Management and Data Systems.

  • Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the academy of marketing science, 43(1), 115–135.

  • Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. Advances in International Marketing, 20, 277–320.

  • Jeong, H., Hmelo-Silver, C. E., & Jo, K. (2019). Ten years of computer-supported collaborative learning: A meta-analysis of CSCL in STEM education during 2005–2014. Educational Research Review, 28, 100284.

    Article  Google Scholar 

  • Johnson, D. W., & Johnson, R. T. (2009). An educational psychology success story: Social interdependence theory and cooperative learning. Educational researcher, 38(5), 365–379.

    Article  Google Scholar 

  • Karaoğlan Yılmaz, F. G., Olpak, Y. Z., & Yılmaz, R. (2018). The effect of the metacognitive support via pedagogical agent on self-regulation skills. Journal of Educational Computing Research, 56(2), 159–180.

    Article  Google Scholar 

  • Kaye, A. R. (2012). December). Computer supported collaborative learning. Computer Supported Collaborative Learning, 128, 125–144.

    Google Scholar 

  • Khan, M. A., Al Raja, M. N., & Al-Shanfari, S. S. A. (2019). The effect of effort expectancy, ubiquity, and context on intention to use online applications. In 2019 International Conference on Digitization (ICD) (pp. 123–128). IEEE.

  • Kim, Y., Thayne, J., & Wei, Q. (2017). An embodied agent helps anxious students in mathematics learning. Educational Technology Research and Development, 65(1), 219–235.

    Article  Google Scholar 

  • Kimmerle, J., & Cress, U. (2008). Group awareness and self-presentation in computer-supported information exchange. International Journal of Computer-Supported Collaborative Learning, 3(1), 85–97.

    Article  Google Scholar 

  • Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration (ijec), 11(4), 1–10.

    Article  Google Scholar 

  • Koomson, W. K. (2019). Ontology of ubiquitous learning: WhatsApp messenger competes successfully with learning management systems (LMS). In Science and information conference (pp. 107–117). Springer.

  • Korkmaz, Ö. (2012). A validity and reliability study of the Online Cooperative Learning Attitude Scale (OCLAS). Computers and Education, 59(4), 1162–1169.

    Article  Google Scholar 

  • Korkmaz, Ö. (2013). CEIT teacher candidates’ attitude toward online collaborative learning and their opinions. İlköğretim Online, 12(1), 283–294.

    Google Scholar 

  • Korkmaz, Ö., & Yesil, R. (2011). Evaluation of achievement, attitudes towards technology using and opinions about group work among students working in gender based groups. Gazi University Journal of Gazi Education Faculty, 31(1), 201–229.

    Google Scholar 

  • Kuhn, D. (2005). Education for thinking. Harvard University Press.

    Google Scholar 

  • Lakhal, S., Khechine, H., & Pascot, D. (2013). Student behavioural intentions to use desktop video conferencing in a distance course: Integration of autonomy to the UTAUT model. Journal of Computing in Higher Education, 25(2), 93–121.

    Article  Google Scholar 

  • Lin, J. W., & Lai, Y. C. (2019). User acceptance model of computer-based assessment: moderating effect and intention-behavior effect. Australasian Journal of Educational Technology, 35(1). 163–176

  • Lin, J. W., & Lin, H. C. K. (2019). User acceptance in a computer-supported collaborative learning (CSCL) environment with social network awareness (SNA) support. Australasian Journal of Educational Technology, 35(1), 100–115.

  • Lin, Y. T., Chang, C. H., Hou, H. T., & Wu, K. C. (2016). Exploring the effects of employing google docs in collaborative concept mapping on achievement, concept representation, and attitudes. Interactive Learning Environments, 24(7), 1552–1573.

    Article  Google Scholar 

  • Liu, Y. C., & Huang, Y. M. (2015). Using the UTAUT model to examine the acceptance behavior of synchronous collaboration to support peer translation. JALT CALL Journal, 11(1), 77–91. Retrieved from https://files.eric.ed.gov/fulltext/EJ1107989.pdf.

  • Lu, H. P., & Yang, Y. W. (2014). Toward an understanding of the behavioural intention to use a social networking site: An extension of task-technology fit to social-technology fit. Computers in Human Behavior, 34, 323–332.

    Article  Google Scholar 

  • Nam, C. W., & Zellner, R. D. (2011). The relative effects of positive interdependence and group processing on student achievement and attitude in online cooperative learning. Computers and Education, 56(3), 680–688.

    Article  Google Scholar 

  • O’Donnell, A. M., & King, A. (2014). Cognitive perspectives on peer learning. Oxford: Routledge.

  • OECD, O. (2015). Students, computers & learning: Making the connection. Organisation for Economic Co-operation and Development (OECD) Programme for International Student Assessments.

  • Oluwajana, D., Nat, M., & Fadiya, S. (2019). An investigation of students’ interactivity in the classroom and within learning management system to improve learning outcomes. Croatian Journal of Education: HrvatskiČasopisZaOdgoj I Obrazovanje, 21(1), 77–102.

    Article  Google Scholar 

  • Park, S. Y. (2009). An analysis of the technology acceptance model in understanding university students’ behavioural intention to use e-learning. Journal of Educational Technology and Society, 12(3), 150–162.

    Google Scholar 

  • Park, S. Y., Nam, M. W., & Cha, S. B. (2012). University students’ behavioural intention to use mobile learning: Evaluating the technology acceptance model. British Journal of Educational Technology, 43(4), 592–605.

    Article  Google Scholar 

  • Pellegrino, J. W., Chudowsky, N., & Glaser, R. (2001). Knowing what students know: The science and design of educational assessment. National Academy Press.

  • Peñarroja, V., Sánchez, J., Gamero, N., Orengo, V., & Zornoza, A. M. (2019). The influence of organisational facilitating conditions and technology acceptance factors on the effectiveness of virtual communities of practice. Behaviour and Information Technology, 38(8), 845–857.

    Article  Google Scholar 

  • Peng, S., Yang, A., Cao, L., Yu, S., & Xie, D. (2017). Social influence behaviour using information theory in mobile social networks. Information Sciences, 379, 146–159.

    Article  Google Scholar 

  • Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of applied psychology, 88(5), 879.

    Article  Google Scholar 

  • Roschelle, J., Dimitriadis, Y., & Hoppe, U. (2013). Classroom orchestration: Synthesis. Computers and Education, 69, 523–526.

    Article  Google Scholar 

  • Rowsell, J., & Walsh, M. (2011). Rethinking literacy education in new times: Multimodality, multiliteracies and new literacies.

  • Schwarz, B. B., Prusak, N., Swidan, O., Livny, A., Gal, K., & Segal, A. (2018). Orchestrating the emergence of conceptual learning: A case study in a geometry class. International Journal of Computer-Supported Collaborative Learning, 13(2), 189–211.

    Article  Google Scholar 

  • Setyahadi, A. R., & Dewi, C. K. (2019). The Influence of Performance Expectancy, Effort Expectancy, Social Influence And Perceived Risk On Mobile Banking Usage Intention In Indonesia Millenial Generation. eProceedings of Management, 6(2), 4590–4606

  • Shute, V. J. (2008). Focus on formative feedback. Review of Educational Research, 78(1), 153–189.

    Article  Google Scholar 

  • Slavin, R. E. (1983). When does cooperative learning increase student achievement? Psychological bulletin, 94(3), 429.

    Article  Google Scholar 

  • Soffer, T., Kahan, T., & Livne, E. (2017). E-assessment of online academic courses via students’ activities and perceptions. Studies in Educational Evaluation, 54, 83–93.

    Article  Google Scholar 

  • Stahl, G., Koschmann, T., & Suthers, D. (2006). Computer-supported collaborative learning: An historical perspective. In R. K. Sawyer (Ed.), Cambridge handbook of the learning sciences (pp. 409–426). Cambridge University Press. Available at http://GerryStahl.net/cscl/CSCL_English.pdf.

  • Suki, N. M., & Suki, N. M. (2017). Determining students’ behavioural intention to use animation and storytelling applying the UTAUT model: The moderating roles of gender and experience level. The International Journal of Management Education, 15(3), 528–538.

    Article  Google Scholar 

  • Suthers, D. D., & Seel, N. M. (2012). Computer-supported collaborative learning. Encyclopedia of the sciences of learning, 719–722.

  • Talib, S. (2018). Social media pedagogy: Applying an interdisciplinary approach to teach multi-modal critical digital literacy. E-learning and digital media, 15(2), 55–66.

    Article  Google Scholar 

  • Tan, E., & Lau, J. L. (2016). Behavioural intention to adopt mobile banking among the millennial generation. Young Consumers, 17(1) 18–31.

  • Teo, T., & Lee, C. B. (2010). Explaining the intention to use technology among student teachers. Campus-Wide Information Systems, 27(2), 60–67.

  • Tissenbaum, M., & Slotta, J. D. (2015). Scripting and orchestration of learning across contexts: A role for intelligent agents and data mining. In Seamless learning in the age of mobile connectivity (pp. 223–257). Springer.

  • Tondeur, J., Van Braak, J., Ertmer, P. A., & Ottenbreit-Leftwich, A. (2017). Understanding the relationship between teachers’ pedagogical beliefs and technology use in education: A systematic review of qualitative evidence. Educational Technology Research and Development, 65(3), 555–575.

    Article  Google Scholar 

  • Van der Meij, H., van der Meij, J., & Harmsen, R. (2015). Animated pedagogical agents effects on enhancing student motivation and learning in a science inquiry learning environment. Educational technology research and development, 63(3), 381–403.

    Article  Google Scholar 

  • Van Wyngaard, C., Strachan, J., & Hülsmann, T. (2016). Whatsapp: Going Where The Conversation Is. In 9th European Distance and E-Learning Network Research Workshop, Forging new pathways of research and innovation in open and distance learning: Reaching from the roots. Retrieved from http://vbn.aau.dk/files/253577592/RW_2016_Oldenburg_Proceedings.Pdf#page=118.

  • Varma, V. (2020). Understanding Resistance to Change: An Experiential Exercise. Management Teaching Review, 5(3), 246–258

  • Venkatesh, V. &, Morris, M. G. (2000). Why don’t men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS Quarterly, 24(1), 115–139.

  • 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

  • Venkatesh, V. Y. L. Thong, J., & Xu, X. (2012). Consumer acceptance and use of information Technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178.

  • Walsh, M. (2010). Multi-modal literacy: What does it mean for classroom practice? The Australian Journal of Language and Literacy, 33(3), 211.

    Article  Google Scholar 

  • Yilmaz, F. G. K., & Yilmaz, R. (2019). Impact of pedagogic agent-mediated metacognitive support towards increasing task and group awareness in CSCL. Computers and Education, 134, 1–14.

    Article  Google Scholar 

  • Yilmaz, F. G. K., & Yilmaz, R. (2020). Student opinions about personalized recommendation and feedback based on learning analytics. Technology, Knowledge and Learning, 25(4), 753–768.

  • Yilmaz, R., Karaoglan Yilmaz, F. G., & Kilic Cakmak, E. (2017). The impact of transactive memory system and interaction platform in collaborative knowledge construction on social presence and self-regulation. Interactive Learning Environments, 25(8), 949–969.

    Article  Google Scholar 

  • Yueh, H. -P., Huang, R. -Y., & Chang, C. (2015). Exploring factors affecting students' continued Wiki use for individual and collaborative learning from the perspective of the unified theory of acceptance and use of technology. Australasian Journal of Educational Technology, 31(1), 16–31.

  • Zhou, L. L., Owusu-Marfo, J., Antwi, H. A., Antwi, M. O., Kachie, A. D. T., & Ampon-Wireko, S. (2019). Assessment of the social influence and facilitating conditions that support nurses’ adoption of hospital electronic information management systems (HEIMS) in Ghana using the unified theory of acceptance and use of technology (UTAUT) model. BMC medical Informatics and Decision Making, 19(1), 230.

    Article  Google Scholar 

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  1. Department of Information Technology, Eastern Mediterranean University, Famagusta, North Cyprus

    Dokun Oluwajana & Ibrahim Adeshola

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  1. Dokun Oluwajana
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Corresponding author handled questionnaire development and data analysis while second author handled research design, structuring and editing. All authors read and approved the final manuscript.

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Correspondence to Dokun Oluwajana.

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Table 7 Design of the study
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Table 8 The demographic profile of respondents
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Oluwajana, D., Adeshola, I. Does the student's perspective on multimodal literacy influence their behavioural intention to use collaborative computer-based learning?. Educ Inf Technol 26, 5613–5635 (2021). https://doi.org/10.1007/s10639-021-10526-y

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