Abstract
This chapter analyses the learning principles governing the learning theories of blended learning, personalized learning, adaptive learning, collaborative assisted learning and game-based learning towards capturing requirements of these theories that can be successfully met and aspects that can be significantly facilitated by technological solutions. We also present a generic learning process structure that can model the above learning theories along with a prototype implementation. The end goal is to showcase the beneficial use of technological solutions in pedagogy.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Baker, R. S., D’Mello, S. K., Rodrigo, M. T., & Graesser, A. C. (2010). Better to be frustrated than bored: The incidence, persistence, and impact of learners’ cognitive–affective states during interactions with three different computer-based learning environments. International Journal of Human-Computer Studies, 68(4), 223–241.
Basawapatna, A. R., Repenning, R., Koh, K. H., & Nickerson, H. (2013). The zones of proximal flow: Guiding students through a space of computational thinking skills and challenges. Proceedings of the ninth annual international ACM conference on International computing education research.
Beaumont, C. J. (1999). Dilemmas of peer assistance in a bilingual full inclusion classroom. The Elementary School Journal, 99(3), 233–254.
Berghel, H. (1997). Cyberspace 2000: Dealing with information overload. Communications of the ACM, 40(2), 19–24.
Blumenfeld, P. C., Marx, R. W., Soloway, E., & Krajcik, J. (1996). Learning with peers: From small group cooperation to collaborative communities. Educational Researcher, 25(8), 37–39.
Borchers, A., Herlocker, J., & Riedl, J. (1998). Ganging up on information overload. Computer, 31(4), 106–108.
Boyle, B., Arnedillo-Sanchez, I., & Zahid, A. (2015). AutBlocks: Using collaborative learning to develop joint attention skills for children with autism spectrum disorder. International Society of the Learning Sciences, Inc. [ISLS].
Brush, T. A. (1998). Embedding cooperative learning into the design of integrated learning systems: Rationale and guidelines. Educational Technology Research and Development, 46(3), 5–18.
Carpenter, B., Egerton, J., Cockbill, B., Bloom, T., Fotheringham, J., Rawson, H., et al. (2015). Engaging learners with complex learning difficulties and disabilities: A resource book for teachers and teaching assistants. London: Routledge.
Chaiklin, S. (2003). The zone of proximal development in Vygotsky’s analysis of learning and instruction. In Vygotsky’s educational theory in cultural context (Vol. 1, pp. 39–64), Cambridge University Press.
Chen, C. M., Lee, H. M., & Chen, Y. H. (2005). Personalized e-learning system using item response theory. Computers and Education, 44(3), 237–255.
D4.2 MaTHiSiS. (2017). Retrieved from D4.2 MaTHiSiS sensorial component: http://mathisis-project.eu/en/deliverables
D8.8 MaTHiSis. (2017). Retrieved from D8.8. Report on mainstream education case pilots: http://mathisis-project.eu/en/deliverables
Damon, W., & Phelps, E. (1989). Critical distinctions among three approaches to peer education. International Journal of Educational Research, 13(1), 9–19.
Dillenbourg, P., & Hong, F. (2008). The mechanics of CSCL macro scripts. International Journal of Computer-Supported Collaborative Learning, 3(1), 5–23.
D’Mello, S., Picard, R. W., & Graesser, A. (2007). Toward an affect-sensitive AutoTutor. IEEE Intelligent Systems, 22(4).
Dolan, R. P., & Hall, T. E. (2001). Universal design for learning: Implications for large-scale assessment. IDA perspectives, 27(4), 22–25.
Ekman, P., & Friesen, W. (1978). Facial action coding system: A technique for the measurement of facial movement. Palo Alto: Consulting Psychologists PressConsulting Psychologists Press.
Ford, J. H., Robinson, J. M., & Wise, M. E. (2016). Adaptation of the Grasha Riechman student learning style survey and teaching style inventory to assess individual teaching and learning styles in a quality improvement collaborative. BMC Medical Education, 16(1), 252.
Garrison, D. R., Anderson, T., & Archer, W. (2001). Critical thinking and computer conferencing: A model and tool to access cognitive presence. American Journal of Distance Education, 15(1), 7–23.
Garrisson, R. D., & Heather, K. (2004). Blended learning: Uncovering its transformative potential in higher education. The Internet and Higher Education, 7(2), 95–105.
Ghou, C. Y., Chan, T. W., & Lin, C. J. (2003). Redefining the learning companion: The past, present, and future of educational agents. Computers & Education, 40(3), 255–269.
Graham, C. R., Allen, S., & Ure, D. (2003). Blended learning environments: A review of the research literature. Unpublished manuscript, Provo, UT.
Hargreaves, D. H. (2006). A new shape for schooling. London: Specialist Schools and Academies Trust.
Hooper, S., & Hannafin, M. J. (1991). The effects of group composition on achievement, interaction, and learning efficiency during computer-based cooperative instruction. Educational Technology Research and Development, 39(3), 27–40.
Iovannone, R., Dunlap, G., Huber, H., & Kincaid, D. (2003). Effective educational practices for students with autism spectrum disorders. Focus on Autism and Other Developmental Disabilities, 18(3), 150–165.
Johnson, D. W., & Johnson, R. T. (1989). Cooperation and learning: Theory and research. Edina, MN: Interaction Book Company.
Johnson, D. W., & Johnson, R. T. (1999). Learning together and alone: Cooperative, competitive, and individualistic learning (5th ed.). Boston, MA: Allyn & Bacon.
Johnson, R. T., Johnson, D. W., & Stanne, M. B. (1985). Effects of cooperative, competitive, and individualistic goal structures on computer-assisted instruction. Journal of Educational Psychology, 77(6), 668–677.
Jones, V., & Jo, J. H. (2004). Ubiquitous learning environment: An adaptive teaching system using ubiquitous technology. In Beyond the comfort zone: Proceedings of the 21st ASCILITE conference (pp. 474).
Kerawalla, L., Pearce, D., Yuill, N., Luckin, R., & Harris, A. (2008). “I’m keeping those there, are you?” the role of a new user interface paradigm–separate control of shared space (SCOSS)–in the collaborative decision-making process. Computers & Education, 50(1), 193–206.
Killi, K. (2005). Digital game-based learning: Towards an experiential gaming model. The Internet and Higher Education, 8(1), 13–24.
Kreijns, K., Kirschner, P. A., & Jochems, W. (2003). Identifying the pitfalls for social interaction in computer-supported collaborative learning environments: A review of the research. Computers in Human Behavior, 19(3), 335–353.
Lee, M. G. (2001). Profiling students’ adaptation styles in web-based learning. Computers and Education, 36, 121–132.
Linnenbrink, E. A., & Pintrich, P. R. (2002). Achievement goal theory and affect: An asymmetrical bidirectional model. Educational Psychologist, 37(2), 69–78.
Lucey, P., Cohn, J. F., Kanade, T., Saragih, J., Ambadar, Z., & Matthews, I. (2010, June). The extended cohn-kanade dataset (ck+): A complete dataset for action unit and emotion-specified expression. In Computer Vision and Pattern Recognition Workshops (CVPRW), 2010 IEEE Computer Society Conference on (pp. 94–101). IEEE.
Luckin, R. (2010). Re-designing learning contexts: Technology-rich, learner-centred ecologies. New York, NY: Routledge.
Martin, B. L., & Reigeluth, C. M. (1999). Affective education and the affective domain: Implications for instructional-design theories and models. In Instructional-design theories and models: A new paradigm of instructional theory (Vol. II). Hillsdale, NJ: Lawrence Erlbaum Associates.
MaTHiSiS. (2018). Retrieved from H2020 MaTHiSiS project: http://mathisis-project.eu/en
Norman, D. A. (1993). Things that make us smart: Defending human attributes in the age of the machine. New York, NY: Addison-Wesley.
O’Connor, R. E., & Jenkins, J. R. (1996). Cooperative learning as an inclusion strategy: A closer look. Exceptionality, 6(1), 29–51.
Oray, M. (2010). Emerging perspectives on learning, teaching and technology. CreateSpace: North Charleston.
Papanikolaou, K. A., & Grigoriadou, M. (2002). Towards new forms of knowledge communication: The adaptive dimension of a webbased learning environment. Computers and Education, 33, 333–360.
Paramythis, A., & Loidl-Reisinger, S. (2004, March). Adaptive learning environments and e-learning standards. In Second European conference on e-learning (pp. 369–379).
Pekrun, R., & Linnenbrink-Garcia, L. (2014). International handbook of emotions in education. London, UK/New York, NY: Routledge.
Rollings, A., & Adams, E. (2003). Andrew Rollings and Ernest Adams on game design. Berkeley, CA: New Riders.
Sebba, J., Brown, N., Steward, S., Galton, M., & James, M. (2007). An investigation of personalised learning approaches used by schools. Nottingham, UK: DfES Publications.
Shernoff, D. J., Csikszentmihalyi, M., Schneider, B., & Shernoff, E. S. (2014). Student engagement in high school classrooms from the perspective of flow theory. In Applications of flow in human development and education (pp. 475–494). Springer: Dordrecht.
Soller, A. L., Lesgold, A., Linton, F., & Goodman, B. (1999). What makes peer interaction effective? Modeling effective communication in an intelligent CSCL. In Proceedings of the 1999 AAAI fall symposium: Psychological models of communication in collaborative systems (pp. 116–123). Cape-Cod, MA.
Steed, C. (2002, September). Why personalized is the way ahead for learning. IT Training.
Tang, T. Y., & Mccalla, G. (2003). Smart recommendation for evolving e-learning system. In 11th international conference on artificial intelligence in education, Workshop on technologies for electronic documents for supporting learning (pp. 699–710). Syndey, Australia.
Tobias, S., Fletcher, J. D., & Wind, A. P. (2014). Game-based learning. In Handbook of research on educational communications and technology (pp. 485–503). New York, NY: Springer.
Tsastou, D., Vretos, N., & Daras, P. (2017). Modelling learning experiences in adaptive multi. In 9th international conference on virtual worlds (pp. 193–200).
Weber, G., & Specht, M. (1997). User modeling and adaptive navigation support in WWW-based tutoring systems. In Proceedings of the sixth international conference on user modeling (pp. 289–300). Vienna, Austria/New York, NY: Springer.
Xiong, X., & De la Torre, F. (2013). Supervised descent method and its applications to face alignment. In Computer Vision and Pattern Recognition (CVPR). IEEE conference on computer vision and pattern recognition (pp. 532–539).
Acknowledgements
The work presented in this document was partially funded through H2020 – MaTHiSiS project. This project has received funding from the European Union’s Horizon 2020 Programme (H2020-ICT-2015) under Grant Agreement No. 687772.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Karkazis, P. et al. (2019). Technologies Facilitating Smart Pedagogy. In: Daniela, L. (eds) Didactics of Smart Pedagogy. Springer, Cham. https://doi.org/10.1007/978-3-030-01551-0_22
Download citation
DOI: https://doi.org/10.1007/978-3-030-01551-0_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01550-3
Online ISBN: 978-3-030-01551-0
eBook Packages: EducationEducation (R0)