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The role of different levels of multichannel multimodal learning experience delivery in student engagement

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Abstract

This study aimed to explore the pedagogical dimension of multichannel multimodal learning (MML) and its association with student engagement. Forty-three students from Shanghai Open University participated in the study and their behavioural, emotional, cognitive learning engagement were investigated. Although teaching in the same space, the three instructors developed different MML-integrated pedagogies and delivered different levels of MML experience (i.e., different numbers of modes and channels used and incorporated). A one-factor-three-level ANOVA with a Tukey HSD test was conducted to examine whether students’ behavioural, emotional, or cognitive learning engagement on three different levels of MML experience were different. The results showed behavioural and emotional engagement was significantly positively correlated (p = .020 and .026 respectively) to different MML-integrated pedagogies, and students’ perception of levels of MML experience was significantly correlated (p = .048) to the levels the present study identified. The present study pointed out the importance of instructors’ integrating pedagogies into MML environments. More modes and channels used and incorporated by instructors can better teaching quality by increasing learners’ behavioural and emotional engagement.

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References

  • Al-Awni, A. (2016). Mood extraction using facial features to improve learning curves of students in e-learning systems. International journal of advanced computer science and applications, 7(11), 444–453. https://doi.org/10.14569/IJACSA.2016.071157

  • Aslan, S., Alyuz, N., Tanriover, C., Mete, S. E., Okur, E., D'Mello, S. K., & Arslan Esme, A. (2019). Investigating the impact of a real-time, multimodal student engagement analytics technology in authentic classrooms. In Proceedings of the 2019 chi conference on human factors in computing systems, 304, 1–12. https://doi.org/10.1145/3290605.3300534

  • Azevedo, R., & Gašević, D. (2019). Analyzing multimodal multichannel data about self-regulated learning with advanced learning technologies: Issues and challenges. Computers in Human Behavior, 96, 207–210. https://doi.org/10.1016/j.chb.2019.03.025

    Article  Google Scholar 

  • Barbieri, T., Bianchi, A., Sbattella, L., Carella, F., & Ferra, M. (2005). MultiAbile: A multimodal learning environment for the inclusion of impaired e-learners using tactile feedbacks, voice, gesturing, and text simplification. Assist Technology: From Virtuality to Real, 16(1), 406–410.

    Google Scholar 

  • Berland, M., Baker, R. S., & Blikstein, P. (2014). Educational data mining and learning analytics: Applications to constructionist research. Technology, Knowledge and Learning, 19(1–2), 205–220. https://doi.org/10.1007/s10758-014-9223-7

    Article  Google Scholar 

  • Blikstein, P., & Worsley, M. (2016). Multimodal learning analytics and education data mining: using computational technologies to measure complex learning tasks. Journal of Learning Analytics, 3(2), 220–238. https://doi.org/10.18608/jla.2016.32.11

  • Chambers, M. (1999). The efficacy and ethics of using digital multimedia for educational purposes. In A. Tait & R. Mills (Eds.), The Convergence of Distance and Conventional Education: Patterns of Flexibility for the Individual Learner (pp. 5–16). Routledge.

  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates, Publishers. https://doi.org/10.4324/9780203771587

  • Delfino, A. P. (2019). Student engagement and academic performance of students of Partido State University. Asian Journal of University Education, 15(1), 42–55. https://eric.ed.gov/?id=EJ1222588

  • Derakhshan, A. (2021). The predictability of Turkman students’ academic engagement through Persian language teachers’ nonverbal immediacy and credibility. Journal of Teaching Persian to Speakers of Other Languages, 10(21), 3–26. https://doi.org/10.30479/jtpsol.2021.14654.1506

  • Dogan, U. (2014).Validity and reliability of student engagement scale. Bartin University Journal of Faculty of Education, 3(2), 309–403. https://doi.org/10.14686/buefad.201428190

  • Dogan, U. (2015). Student engagement, academic self-efficacy, and academic motivation as predictors of academic performance. The Anthropologist, 20(3), 553–561.

    Article  MathSciNet  Google Scholar 

  • Fadel, C. (2008). Multimodal Learning Through Media: What the Research Says. Cisco Systems. https://doi.org/10.1007/978-1-4419-1428-6_922

  • Fauth, B., Decristan, J., Rieser, S., Klieme, E., & Büttner, G. (2014). Student ratings of teaching quality in primary school: Dimensions and prediction of student outcomes. Learning and Instruction, 29, 1–9. https://doi.org/10.1016/j.learninstruc.2013.07.001

    Article  Google Scholar 

  • Ferdig, R., Cavanaugh, C., & Freidhoff, J. (2012). Lessons learned from blended programs: Experiences and recommendations from the field. iNACOL.

  • Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74(1), 59–109. https://doi.org/10.3102/00346543074001059

    Article  Google Scholar 

  • Fredricks, J. (2013). Behavioral engagement in learning. In J. Hattie & E. M. Anderman (Eds.), International guide to student achievement (pp. 42–44). Routledge/Taylor & Francis Group.

  • Furrer, C., & Skinner, E. (2003). Sense of relatedness as a factor in children’s academic engagement and performance. Journal of Educational Psychology, 95(1), 148. https://doi.org/10.1037/0022-0663.95.1.148

    Article  Google Scholar 

  • Ganapathy, M., & Seetharam, S. A. (2016). The effects of using multimodal approaches in meaning - Making of 21st century literacy texts among ESL students in a private school in Malaysia. Advances in Language and Literary Studies, 7(2), 143–155. https://doi.org/10.7575/aiac.alls.v.7n.2p.143

  • Grafsgaard, J. F. (2014). Multimodal analysis and modelling of nonverbal behaviors during tutoring. In Proceedings of the 16th International Conference on Multimodal Interaction (pp. 404–408). https://doi.org/10.1145/2663204.2667611

  • Graham, C., Allen, S., & Ure, D. (2005). Benefits and challenges of blended learning environments. https://doi.org/10.4018/978-1-59140-553-5.ch047

  • Greene, T. G., Marti, C. N., & McClenney, K. (2008). The effort—outcome gap: Differences for African American and Hispanic community college students in student engagement and academic achievement. The Journal of Higher Education, 79(5), 513–539. https://doi.org/10.1353/jhe.0.0018

    Article  Google Scholar 

  • Gunuc, S., & Kuzu, A. (2015). Student engagement scale: Development, reliability and validity. Assessment & Evaluation in Higher Education, 40(4), 587–610. https://doi.org/10.1080/02602938.2014.938019

    Article  Google Scholar 

  • Hudley, C., Daoud, A., Polanco, T., Wright-Castro, R., & Hershberg, R. (2003). Student engagement, school climate, and future expectations in high school. Paper presented at the 2003 Biennial Meeting of the Society for Research in Child Development, Tampa, FL.

  • Jing, S., Tang, Y., Liu, X., & Gong, X. (2020). A learner model integrating cognitive and metacognitive and its application on scratch programming projects. IFAC-PapersOnLine, 53(5), 644–649. https://doi.org/10.1016/j.ifacol.2021.04.154

  • Junco, R. (2012). The relationship between frequency of Facebook use, participation in Facebook activities, and student engagement. Computers & Education, 58(1), 162–171. https://doi.org/10.1016/j.compedu.2011.08.004.

    Article  Google Scholar 

  • Jung, Y., & Lee, J. (2018). Learning engagement and persistence in massive open online courses (MOOCS). Computers & Education, 122, 9–22. https://doi.org/10.1016/j.compedu.2018.02.013.

    Article  Google Scholar 

  • Kerkiri, T., Konetas, D., Paleologou, A. M., & Mavridis, I. (2011). Semantic web technologies anchored in learning styles as catalysts towards personalising the learning process. International Journal of Learning and Intellectual Capital, 8(2), 179–205.

    Article  Google Scholar 

  • Kline, T. J. (1999). The team player inventory: Reliability and validity of a measure of predisposition toward organizational team-working environments. Journal for Specialists in Group Work, 24(1), 102–112. https://doi.org/10.1080/01933929908411422

    Article  Google Scholar 

  • Kuh, G. D. (2009). What student affairs professionals need to know about student engagement. Journal of College Student Development, 50(6), 683–706. https://doi.org/10.1353/csd.0.0099

    Article  Google Scholar 

  • Kuh, G. D., Kinzie, J., Schuh, J. H., & Whitt, E. J. (2005). Never let it rest lessons about student success from high-performing colleges and universities. Change: The Magazine of Higher Learning, 37(4), 44–51. https://doi.org/10.3200/chng.37.4.44-51

  • Kyei-Blankson, L., Ntuli, E., & Donnelly, H. (2016). Establishing the importance of interaction and presence to student learning in online environments. World Journal of Educational Research,3(1), 48–65. https://doi.org/10.22158/wjer.v3n1p48

  • Leach, L. (2016). Enhancing student engagement in one institution. Journal of Further and Higher Education, 40(1), 23–47. https://doi.org/10.1080/0309877X.2013.869565

    Article  Google Scholar 

  • Lee, J. S. (2014). The relationship between student engagement and academic performance: Is it a myth or reality?. The Journal of Educational Research, 107(3), 177–185.https://doi.org/10.1080/00220671.2013.807491

  • Lloyd, M. (2013). Something’s coming, something good: Identifying TPACK competence in preservice teachers’ analyses of learning objects. Australian Educational Computing, 28(1). http://journal.acce.edu.au/index.php/AEC/article/view/12/PDF

  • Malczyk, B. R. (2018). Multimodal Instruction: The New Hybrid. Journal of Non-profit Education and Leadership,8(1), 16–31. https://doi.org/10.18666/jnel-2018-v8-i1-8347

  • Mo, Y., & Singh, K. (2008). Parents’ relationships and involvement: Effects on students’ school engagement and performance. RMLE Online, 31(10), 1–11. https://doi.org/10.1080/19404476.2008.11462053

    Article  Google Scholar 

  • Magana, A. J., Serrano, M. I., & Rebello, N. S. (2019). A sequenced multimodal learning approach to support students’ development of conceptual learning. Journal of Computer Assisted Learning, 35(4), 516–528. https://doi.org/10.1111/jcal.12356

  • Moreno, R., & Mayer, R. (2007). Interactive multimodal learning environments. Educational Psychology Review, 19(3), 309–326. https://doi.org/10.1007/s10648-007-9047-2

    Article  Google Scholar 

  • Mudrick, N. V. (2018). Designing Adaptive Intelligent Tutoring Systems: Fostering Cognitive, Affective, and Metacognitive Self-Regulated Learning Processes Using Multimodal, Multichannel Process Data [Doctoral dissertation, North Carolina State University]. NC State University Libraries.

  • Mukhopadhyay, M., & Parhar, M. (2001). Instructional design in multi-channel learning system. British Journal of Educational Technology, 32(5), 543–556. https://doi.org/10.1111/1467-8535.00224

    Article  Google Scholar 

  • Noroozi, O., Pijeira-Díaz, H. J., Sobocinski, M., Dindar, M., Järvelä, S., & Kirschner, P. A. (2020). Multimodal data indicators for capturing cognitive, motivational, and emotional learning processes: A systematic literature review. Education and Information Technologies, 25, 5499–5547. https://doi.org/10.1007/s10639-020-10229-w

  • Pascarella, E. T., & Terenzini, P. T. (2005). How college affects students: A third decade of research. San Francisco: Jossey-Bass. https://doi.org/10.14426/jsaa.v2i2.70

  • Peng, W. (2017). Research on model of student engagement in online learning. Eurasia Journal of Mathematics, Science and Technology Education, 13(7). https://doi.org/10.12973/eurasia.2017.00723a

  • Picciano, A. G. (2009). Blending with purpose: The multimodal model. Journal of asynchronous learning networks, 13(1), 7–18. https://doi.org/10.24059/olj.v13i1.1673

  • Pintrich, P. R., & De Groot, E. V. (1990). Motivational and self-regulated learning components of classroom academic performance. Journal of Educational Psychology, 82(1), 33–40. https://doi.org/10.1037/0022-0663.82.1.33

  • Psaltis, A., Apostolakis, K. C., Dimitropoulos, K., & Daras, P. (2017). Multimodal student engagement recognition in prosocial games. IEEE Transactions on Games, 10(3), 292–303.

  • Redfern, S., & Naughton, N. (2002). Collaborative virtual environments to support communication and community in Internet-based distance education.Journal of Information Technology Education, 1(3), 201–211.

  • Rieser, S., Naumann, A., Decristan, J., Fauth, B., Klieme, E., & Büttner, G. (2016). The connection between teaching and learning: Linking teaching quality and metacognitive strategy use in primary school. British Journal of Educational Psychology, 86(4), 526–545. https://doi.org/10.1111/bjep.12121

    Article  Google Scholar 

  • Russell, V. J., Ainley, M., & Frydenberg, E. (2005). Student motivation and engagement. Schooling Issues Digest, 2, 1–11.

    Google Scholar 

  • Sagayadevan, V., & Jeyaraj, S. (2012). The role of emotional engagement in lecturer-student interaction and the impact on academic outcomes of student achievement and learning. Journal of the Scholarship of Teaching and Learning, 12(3), 1–30. https://scholarworks.iu.edu/journals/index.php/josotl/article/view/2152

  • Sankey, M., Birch, D., & Gardiner, M. (2010). Engaging students through multimodal learning environments: The journey continues. In Proceedings of ASCILITE Australian Society for Computers in Learning in Tertiary Education Annual Conference 2010 (pp. 852–863). http://www.editlib.org/p/45485

  • Sinatra, G. M., Heddy, B. C., & Lombardi, D. (2015). The challenges of defining and measuring student engagement in science. Educational Psychologist, 50(1), 1–13. https://doi.org/10.1080/00461520.2014.1002924

  • Sprague, E. W., & Dahl, D. W. (2009). Learning to click: An evaluation of the personal response system clicker technology in introductory marketing courses. Journal of Marketing Education, 32(1), 93–103. https://doi.org/10.1177/0273475309344806

    Article  Google Scholar 

  • Tai, D. W., Tai, V., & Wang, R. (2014). Assessment criteria of vocational college students’ learning engagement. International Journal of Technology and Engineering Education, 11(1), 31–38.

  • Thiessen, V., & Blasius, J. (2008). Mathematics achievement and mathematics learning strategies: Cognitive competencies and construct differentiation. International Journal of Educational Research, 47(6), 362–371. https://doi.org/10.1016/j.ijer.2008.12.002

    Article  Google Scholar 

  • Van de Grift, W. J., Chun, S., Maulana, R., Lee, O., & Helms-Lorenz, M. (2017). Measuring teaching quality and student engagement in South Korea and The Netherlands. School Effectiveness and School Improvement, 28(3), 337–349. https://doi.org/10.1080/09243453.2016.1263215

    Article  Google Scholar 

  • Walker, C. O., & Greene, B. A. (2009). The relations between student motivational beliefs and cognitive engagement in high school. The Journal of Educational Research, 102(6), 463–472. https://doi.org/10.3200/joer.102.6.463-472

    Article  Google Scholar 

  • Wara, E., Aloka, P. J., & Odongo, B. C. (2018). Relationship between emotional engagement and academic achievement among Kenyan secondary school students. Academic Journal of Interdisciplinary Studies, 7(1), 108–118. https://doi.org/10.2478/ajis-2018-0011

    Article  Google Scholar 

  • Yazzie-Mintz, E., & McCormick, K. (2012). Finding the humanity in the data: Understanding, measuring, and strengthening student engagement. Handbook of research on student engagement (pp. 743–761). Springer. https://doi.org/10.1007/978-1-4614-2018-7_36

  • Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory into Practice, 41(2), 64–70. https://doi.org/10.1207/s15430421tip4102_2

    Article  Google Scholar 

  • Zhao, Y., & Ko, J. (2020). How do teaching quality and pedagogical practice enhance vocational student engagement? A mixed-method classroom observation approach. International Journal of Educational Management, 34(6), 987–1000. https://doi.org/10.1108/IJEM-11-2019-0393

    Article  Google Scholar 

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Funding

This study was supported both by Shanghai Science and technology innovation action plan international cooperation project "Research on international multi language online learning platform and key technologies(No.20510780100) "and Science and Technology Commission of Shanghai Municipality research project “Shanghai Engineering Research Centre of Open Distance Education(No.13DZ2252200).

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  1. Shanghai Engineering Research Center of Open Distance Education, Shanghai Open University, No.288 Guoshun Road, Shanghai, 80305, CO, China

    Jun Xiao

  2. Wisdom Garden Educational Research Institute, Hong Kong, China

    Tzu-Han Lin & Hong-Zheng Sun-Lin

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Correspondence to Jun Xiao.

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Xiao, J., Lin, TH. & Sun-Lin, HZ. The role of different levels of multichannel multimodal learning experience delivery in student engagement. Educ Inf Technol 27, 2939–2954 (2022). https://doi.org/10.1007/s10639-021-10731-9

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