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Does the use of a web-based collaborative platform reduce cognitive load and influence project-based student engagement?

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Abstract

The web-based supported collaborative learning is increasingly used to support student social activities in higher institutions. However, little is known about the factors of collaborative learning in a web-based supported learning environment. Therefore, this study examines the use of a web-based supported collaborative platform to enhance project-based student engagement. This research aims to determine the factors that determine collaborative learning and subsequent student satisfaction. Moreover, this research determines students’ cognitive load understanding, social influence, and learner’s motivation towards collaborative learning and the resultant impact of the web-based supported collaborative platform on student satisfaction. The data was collected from university post-graduate students who used the TRELLO platform. A total of 115 post-graduate students participated in this study, and the resulting data were analyzed based on partial least squares structural equation modelling statistical approach. The study results suggest that students’ social influence and motivation positively influence collaborative learning; directly and indirectly, students are satisfied using a web-based supported collaborative learning platform to support project-based student engagement.

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Data Availability

Instruments and databases are available. The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  • Abuhassna, H., Al-Rahmi, W. M., Yahya, N., Zakaria, M. A. Z. M., Kosnin, A. B. M., & Darwish, M. (2020). Development of a new model on utilizing online learning platforms to improve students’ academic achievements and satisfaction. International Journal of Educational Technology in Higher Education, 17(1), 1–23.

    Article  Google Scholar 

  • Alhinty, M. (2014). Young language learners' collaborative learning and social interaction as a motivational aspect of the iPad. In 2014 international conference on interactive Mobile communication technologies and learning (IMCL2014) (pp. 64-69). IEEE.

  • Almulla, M. A. (2020). The effectiveness of the project-based learning (PBL) approach as a way to engage students in learning. SAGE Open, 10(3), 2158244020938702.

    Article  Google Scholar 

  • Al-Rahmi, W. M., & Othman, M. S. (2013). Evaluating student’s satisfaction of using social media through collaborative learning in higher education. International Journal of advances in engineering & technology, 6(4), 1541–1551.

    Google Scholar 

  • Al-Rahmi, W., Othman, M. S., & Yusuf, L. M. (2015). The role of social media for collaborative learning to improve academic performance of students and researchers in Malaysian higher education. The International Review of Research in Open and Distributed Learning, 16(4).

  • Al-Samarraie, H., & Saeed, N. (2018). A systematic review of cloud computing tools for collaborative learning: Opportunities and challenges to the blended-learning environment. Computers & Education, 124, 77–91.

    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.

  • Ayres, P., & Paas, F. (2012). Cognitive load theory: New directions and challenges. Applied Cognitive Psychology, 26(6), 827–832.

    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), 607–627.

    Article  Google Scholar 

  • Balakrishnan, V., Liew, T. K., & Pourgholaminejad, S. (2015). Fun learning with Edooware–a social media enabled tool. Computers & Education, 80, 39–47.

    Article  Google Scholar 

  • Borge, M., & White, B. (2016). Toward the development of socio-metacognitive expertise: An approach to developing collaborative competence. Cognition and Instruction, 34(4), 323–360.

    Article  Google Scholar 

  • Borge, M., Aldemir, T., & Xia, Y. (2019). Unpacking socio-metacognitive sense-making patterns to support collaborative discourse.

  • Borge, M., Ong, Y. S., & Rosé, C. P. (2018). Learning to monitor and regulate collective thinking processes. International Journal of Computer-Supported Collaborative Learning, 13(1), 61–92.

    Article  Google Scholar 

  • Carrabba, C., & Farmer, A. (2018). The impact of project-based learning and direct instruction on the motivation and engagement of middle school students. Online Submission, 1(2), 163–174.

    Google Scholar 

  • Chan, F. T., Chong, A. Y. L., & Zhou, L. (2012). An empirical investigation of factors affecting e-collaboration diffusion in SMEs. International Journal of Production Economics, 138(2), 329–344.

    Article  Google Scholar 

  • Chan, K., Cheung, G., Brown, I., & Luk, G. (2015). Synthesizing technology adoption and learners' approaches towards active learning in higher education. In international conference on e-learning (p. 66). Academic conferences international limited.

  • Chen, C. H., & Chiu, C. H. (2016). Employing intergroup competition in multitouch design-based learning to foster student engagement, learning achievement, and creativity. Computers & Education, 103, 99–113.

    Article  Google Scholar 

  • Chen, C. M., & Kuo, C. H. (2019). An optimized group formation scheme to promote collaborative problem-based learning. Computers & Education, 133, 94–115.

    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.

    Article  Google Scholar 

  • Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern methods for business research, 295(2), 295–336.

  • Ciampa, K. (2014). Learning in a mobile age: An investigation of student motivation. Journal of Computer Assisted Learning, 30(1), 82–96.

    Article  Google Scholar 

  • Curşeu, P. L., Chappin, M. M., & Jansen, R. J. (2018). Gender diversity and motivation in collaborative learning groups: The mediating role of group discussion quality. Social Psychology of Education, 21(2), 289–302.

    Article  Google Scholar 

  • De Freitas, S. I., Morgan, J., & Gibson, D. (2015). Will MOOCs transform learning and teaching in higher education? Engagement and course retention in online learning provision. British Journal of Educational Technology, 46(3), 455–471.

    Article  Google Scholar 

  • Deci, E. L., & Ryan, R. M. (1985). The general causality orientations scale: Self-determination in personality. Journal of Research in Personality, 19(2), 109–134.

    Article  Google Scholar 

  • DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten year update. Journal of Management Information Systems, 19(4), 9–30.

    Article  Google Scholar 

  • Donlan, L. (2014). Exploring the views of students on the use of Facebook in university teaching and learning. Journal of Further and Higher Education, 38(4), 572–588.

    Article  Google Scholar 

  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

    Article  Google Scholar 

  • Giesbers, B., Rienties, B., Tempelaar, D., & Gijselaers, W. (2013). Investigating the relations between motivation, tool use, participation, and performance in an e-learning course using web-videoconferencing. Computers in Human Behavior, 29(1), 285–292.

    Article  Google Scholar 

  • Godin, J., & Goette, T. (2013). A pilot study of virtual teamwork training. Communications of the IIMA, 13(2), 3.

  • Gomez, E. A., Wu, D., & Passerini, K. (2010). Computer-supported team-based learning: The impact of motivation, enjoyment and team contributions on learning outcomes. Computers & Education, 55(1), 378–390.

    Article  Google Scholar 

  • Gómez-Rey, P., Barbera, E., & Fernández-Navarro, F. (2018). Students' perceptions about online teaching effectiveness: A bottom-up approach for identifying online instructors’ roles. Australasian Journal of Educational Technology, 34(1).

  • Gress, C. L., Fior, M., Hadwin, A. F., & Winne, P. H. (2010). Measurement and assessment in computer-supported collaborative learning. Computers in Human Behavior, 26(5), 806–814.

    Article  Google Scholar 

  • Hadwin, A. F., Oshige, M., Gress, C. L. Z., & Winne, P. H. (2010). Innovative ways for using gStudy to orchestrate and research social aspects of self-regulated learning. Computers in Human Behavior, 26, 794–805.

    Article  Google Scholar 

  • Hair, J. F., Anderson, R. E., Babin, B. J., & Black, W. C. (2010). Multivariate data analysis: A global perspective (Vol. 7): Pearson Upper Saddle River.

  • Harvey, C. M., & Koubek, R. J. (2000). Cognitive, social, and environmental attributes of distributed engineering collaboration: A review and proposed model of collaboration. Human Factors and Ergonomics in Manufacturing & Service Industries, 10(4), 369–393.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: Updated guidelines. Industrial management & data systems, 116(1), 2–20.

  • Hernández-Sellés, N., Muñoz-Carril, P. C., & González-Sanmamed, M. (2019). Computer-supported collaborative learning: An analysis of the relationship between interaction, emotional support and online collaborative tools. Computers & Education, 138, 1–12.

    Article  Google Scholar 

  • Horváth, I. (2019). MaxWhere 3D capabilities contributing to the enhanced efficiency of the Trello 2D management software. Acta Polytechnica Hungarica, 16(6), 55–71.

    Google Scholar 

  • Hynd, Jodi Holschuh, & Sherrie Nist, C. (2000). Learning complex scientific information: Motivation theory and its relation to student perceptions. Reading & Writing Quarterly, 16(1), 23–57.

    Article  Google Scholar 

  • Janssen, J., Kirschner, F., Erkens, G., Kirschner, P. A., & Paas, F. (2010). Making the black box of collaborative learning transparent: Combining process-oriented and cognitive load approaches. Educational Psychology Review, 22(2), 139–154.

    Article  Google Scholar 

  • Järvelä, S., Kirschner, P. A., Panadero, E., Malmberg, J., Phielix, C., Jaspers, J., Koivuniemi, M., & Järvenoja, H. (2015). Enhancing socially shared regulation in collaborative learning groups: Designing for CSCL regulation tools. Educational Technology Research and Development, 63(1), 125–142.

    Article  Google Scholar 

  • Jiménez-Zarco, A. I., González-González, I., Saigí-Rubió, F., & Torrent-Sellens, J. (2015). The co-learning process in healthcare professionals: Assessing student' satisfaction in virtual communities of practice. Computers in Human Behavior, 51, 1303–1313.

    Article  Google Scholar 

  • Ke, F., & Kwak, D. (2013). Online learning across ethnicity and age: A study on learning interaction participation, perception, and learning satisfaction. Computers & Education, 61, 43–51.

    Article  Google Scholar 

  • Kirschner, F., Paas, F., & Kirschner, P. A. (2009). A cognitive load approach to collaborative learning: United brains for complex tasks. Educational Psychology Review, 21(1), 31–42.

    Article  Google Scholar 

  • Kirschner, F., Paas, F., Kirschner, P. A., & Janssen, J. (2011). Differential effects of problem-solving demands on individual and collaborative learning outcomes. Learning and Instruction, 21(4), 587–599.

    Article  Google Scholar 

  • Kizilcec, R. F. (2013). Collaborative learning in geographically distributed and in-person groups. In AIED 2013 Workshops Proceedings, 67, 73–79.

  • Kwon, K., Hong, R. Y., & Laffey, J. M. (2013). The educational impact of metacognitive group coordination in computer-supported collaborative learning. Computers in Human Behavior, 29(4), 1271–1281.

    Article  Google Scholar 

  • Lai, H. M., & Chen, T. T. (2014). Knowledge sharing in interest online communities: A comparison of posters and lurkers. Computers in Human Behavior, 35, 295–306.

    Article  Google Scholar 

  • Lee, M. C. (2010). Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation–confirmation model. Computers & Education, 54, 506–516.

    Article  Google Scholar 

  • Leppink, J., Paas, F., Van Gog, T., van Der Vleuten, C. P., & Van Merrienboer, J. J. (2014). Effects of pairs of problems and examples on task performance and different types of cognitive load. Learning and Instruction, 30, 32–42.

    Article  Google Scholar 

  • Lin, C. H., Zhang, Y., & Zheng, B. (2017). The roles of learning strategies and motivation in online language learning: A structural equation modeling analysis. Computers & Education, 113, 75–85.

    Article  Google Scholar 

  • Lin, G. Y. (2020). Scripts and mastery goal orientation in face-to-face versus computer-mediated collaborative learning: Influence on performance, affective and motivational outcomes, and social ability. Computers & Education, 143, 103691.

    Article  Google Scholar 

  • Lwoga, E. (2014). Critical success factors for adoption of web-based learning management systems in Tanzania. International Journal of Education and Development using ICT, 10(1).

  • Magen-Nagar, N., & Shonfeld, M. (2018). The impact of an online collaborative learning program on students’ attitude towards technology. Interactive Learning Environments, 26(5), 621–637.

    Article  Google Scholar 

  • Martínez-Núñez, M., Fidalgo-Blanco, Á., & Borrás-Gené, O. (2015). New challenges for the motivation and learning in engineering education using gamification in MOOC.

  • Miyake, N., & Kirschner, P. A. (2014). The social and interactive dimensions of collaborative learning. The Cambridge Handbook of the Learning Sciences, 418–438. https://doi.org/10.1017/cbo9781139519526.026.

  • Mohammadi, H. (2015). Investigating users’ perspectives on e-learning: An integration of TAM and IS success model. Computers in Human Behavior, 45, 359–374.

    Article  Google Scholar 

  • Molinillo, S., Aguilar-Illescas, R., Anaya-Sánchez, R., & Vallespín-Arán, M. (2018). Exploring the impacts of interactions, social presence and emotional engagement on active collaborative learning in a social web-based environment. Computers & Education, 123, 41–52.

    Article  Google Scholar 

  • Nunnally, J. C. (1978). Psychometric theory. McGraw-Hill.

  • Oluwajana, D., & Adeshola, I. (2021). Does the student’s perspective on multi-modal literacy influence their behavioural intention to use collaborative computer-based learning?. Education and information technologies, 1–23.

  • 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: Hrvatski časopis za odgoj i obrazovanje, 21(1), 77–102.

    Article  Google Scholar 

  • O'Sullivan, D., Krewer, F., & Frankl, G. (2017). Technology-enhanced collaborative learning using a project-based learning management system. International Journal of Technology Enhanced Learning, 9(1), 14–36.

    Article  Google Scholar 

  • Paas, F., Tuovinen, J. E., Tabbers, H., & Van Gerven, P. W. (2003). Cognitive load measurement as a means to advance cognitive load theory. Educational Psychologist, 38(1), 63–71.

    Article  Google Scholar 

  • Popov, V., Biemans, H. J., Brinkman, D., Kuznetsov, A. N., & Mulder, M. (2013). Facilitation of computer-supported collaborative learning in mixed-versus same-culture dyads: Does a collaboration script help? The Internet and Higher Education, 19, 36–48.

    Article  Google Scholar 

  • Ringle, Christian M., Wende, Sven, & Becker, Jan-Michael. (2015). SmartPLS 3. SmartPLS. Retrieved from http://www.smartpls.com

  • Rosé, C. P., & Ferschke, O. (2016). Technology support for discussion based learning: From computer supported collaborative learning to the future of massive open online courses. International Journal of Artificial Intelligence in Education, 26(2), 660–678.

    Article  Google Scholar 

  • Salam, F., Mailok, R., Ubaidullah, N., & Ahmad, U. (2016). The effect of project-based learning against students’ engagement. International Journal of Development Research., 6(2), 6891–6895.

    Google Scholar 

  • Salam, M., & Farooq, M. S. (2020). Does sociability quality of web-based collaborative learning information system influence students’ satisfaction and system usage? International Journal of Educational Technology in Higher Education, 17, 1–39.

    Article  Google Scholar 

  • Sanz-Martínez, L., Er, E., Martínez-Monés, A., Dimitriadis, Y., & Bote-Lorenzo, M. L. (2019). Creating collaborative groups in a MOOC: A homogeneous engagement grouping approach. Behaviour & Information Technology, 38(11), 1107–1121.

    Article  Google Scholar 

  • Schoor, C., & Bannert, M. (2011). Motivation in a computer-supported collaborative learning scenario and its impact on learning activities and knowledge acquisition. Learning and Instruction, 21(4), 560–573.

    Article  Google Scholar 

  • Serrano-Cámara, L. M., Paredes-Velasco, M., Alcover, C. M., & Velazquez-Iturbide, J. Á. (2014). An evaluation of students’ motivation in computer-supported collaborative learning of programming concepts. Computers in Human Behavior, 31, 499–508.

    Article  Google Scholar 

  • So, H. J., & Brush, T. A. (2008). Student perceptions of collaborative learning, social presence and satisfaction in a blended learning environment: Relationships and critical factors. Computers & Education, 51(1), 318–336.

    Article  Google Scholar 

  • Su, A. Y., Yang, S. J., Hwang, W. Y., & Zhang, J. (2010). A web 2.0-based collaborative annotation system for enhancing knowledge sharing in collaborative learning environments. Computers & Education, 55(2), 752–766.

    Article  Google Scholar 

  • Su, C. H. (2016). The effects of students' motivation, cognitive load and learning anxiety in gamification software engineering education: A structural equation modeling study. Multimedia Tools and Applications, 75(16), 10013–10036.

    Article  Google Scholar 

  • Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285.

    Article  Google Scholar 

  • Taki, S., & Fardafshari, E. (2012). Weblog-based collaborative learning: Iranian EFL learners’ writing skill and motivation. International Journal of Linguistics, 4(2), 412–429.

    Article  Google Scholar 

  • Tarhini, A., Al-Busaidi, K. A., Mohammed, A. B., & Maqableh, M. (2017). Factors influencing students’ adoption of e-learning: A structural equation modeling approach. Journal of International Education in Business, 10(2), 164–182.

  • Teng, F., & Reynolds, B. L. (2019). Effects of individual and group metacognitive prompts on EFL reading comprehension and incidental vocabulary learning. PLoS One, 14(5), e0215902.

    Article  PubMed  PubMed Central  Google Scholar 

  • Testers, L., Gegenfurtner, A., & Brand-Gruwel, S. (2020). Taking Affective Learning in Digital Education One Step Further: Trainees’ Affective Characteristics Predicting Multicontextual Pre-training Transfer Intention. Frontiers in Psychology, 11. pp. 2189.

  • Troussas, C., Virvou, M., & Alepis, E. (2014). Collaborative learning: Group interaction in an intelligent mobile-assisted multiple language learning system. Informatics in Education, 13(2), 279–292.

    Article  Google Scholar 

  • 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, X. M., & Hwang, G. J. (2017). A problem posing-based practicing strategy for facilitating students’ computer programming skills in the team-based learning mode. Educational Technology Research and Development, 65(6), 1655–1671.

    Article  Google Scholar 

  • Xie, K. (2013). What do the numbers say? The influence of motivation and peer feedback on students’ behaviour in online discussions. British Journal of Educational Technology, 44(2), 288–301.

    Article  Google Scholar 

  • Xiong, Y., Li, H., Kornhaber, M. L., Suen, H. K., Pursel, B., & Goins, D. D. (2015). Examining the relations among student motivation, engagement, and retention in a MOOC: A structural equation modeling approach. Global Education Review, 2(3), 23–33.

    Google Scholar 

  • Yadegaridehkordi, E., Nilashi, M., Shuib, L., & Samad, S. (2020). A behavioral intention model for SaaS-based collaboration services in higher education. Education and Information Technologies, 25(2), 791–816.

    Article  Google Scholar 

  • Yang, Q. F., Chang, S. C., Hwang, G. J., & Zou, D. (2020). Balancing cognitive complexity and gaming level: Effects of a cognitive complexity-based competition game on EFL students' English vocabulary learning performance, anxiety and behaviors. Computers & Education, 148, 103808.

    Article  Google Scholar 

  • Yueh, H. P., Huang, J. Y., & Chang, C. (2015). Exploring factors affecting students’ continued wiki use for individual and collaborative learning: An extended UTAUT perspective. Australasian Journal of Educational Technology, 31(1).

  • Yusop, F. D., & Basar, S. M. M. A. (2017). Resistance towards wiki: Implications for designing successful wiki-supported collaborative learning experiences. Universal Access in the Information Society, 16(2), 349–360.

    Article  Google Scholar 

  • Zambrano, J., Kirschner, F., Sweller, J., & Kirschner, P. A. (2019). Effects of group experience and information distribution on collaborative learning. Instructional Science, 47(5), 531–550.

    Article  Google Scholar 

  • Zhang, X., Meng, Y., de Pablos, P. O., & Sun, Y. (2019). Learning analytics in collaborative learning supported by slack: From the perspective of engagement. Computers in Human Behavior, 92, 625–633.

    Article  Google Scholar 

  • Zheng, L., Zhang, X., & Gyasi, J. F. (2019). A literature review of features and trends of technology-supported collaborative learning in informal learning settings from 2007 to 2018. Journal of Computers in Education, 6(4), 529–561.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Management Information Systems, Cyprus International University, Nicosia, North Cyprus, Turkey

    Dokun Oluwajana

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

    Ibrahim Adeshola

  3. Department of Accounting and Finance, University of Bedfordshire, Luton, UK

    Seyefar Clement

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  1. Dokun Oluwajana
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  2. Ibrahim Adeshola
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  3. Seyefar Clement
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Correspondence to Dokun Oluwajana.

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Oluwajana, D., Adeshola, I. & Clement, S. Does the use of a web-based collaborative platform reduce cognitive load and influence project-based student engagement?. Curr Psychol 42, 8265–8278 (2023). https://doi.org/10.1007/s12144-021-02145-0

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