The learner-interface interaction´s future, a vision from EdTech
DOI:
https://doi.org/10.32870/Ap.v12n2.1910Keywords:
Human-computer interaction, learner-interface interaction, EdTech, AI, intelligent agents, tech design, Covid-19Abstract
From Moore’s three interactions of distance education, Hillman, Willis & Gunawardena (1994) proposed a technological interaction in the instructional domain, the learner-interface interaction. Twenty-six years after their proposal, due to the high degree of technologization of educational technology due to its link with artificial intelligence, this interaction is more valid than ever. This article develops the past, present and future of this fourth interaction. The three most important technological areas related to education are presented, for the future development of the learner-interface interaction: Evaluation of technological interactions in the learning domain (Usability and user experience), Capabilities of intelligent educational agents (Artificial intelligence and natural language processing) and Scope of predictive algorithms in education (Deep learning and big data), as fundamental elements, although not the only ones, for the design of the next generation of 2&3D intelligent multimedia interactive interfaces for educational purposes. The need for a unified interactional model, based on the Anderson-Moore triangular interactional model, is raised, and the Anderson equivalence theorem is taken to hypothesize a possible future scenario in the short, medium and long term for highly technological education.
References
Akyol, Z. & Garrison, D. R. (2013). Educational communities of inquiry: Theoretical framework.
Research and practice. Information sciences reference, IGI Global.
Alexandru, A.; Tîrziu, E.; Tudora, E. & Bica, O. (2015). Enhanced education by using intelligent agents in multi-agent adaptive e-learning systems. Studies in Informatics and Control, 24(1), 13-22. Recuperado de: http://doi.org/10.24846/v24i1y201502
Alsharif, N. Z. & Roche, V. F. (2010). Promoting key interactions in a distance medicinal chemistry course.
Currents in Pharmacy Teaching and Learning, 2(2), 114-125. Recuperado de: http://doi.org/10.1016/j.cptl.2010.01.003
Anderson, T. (2003). Getting the mix right again: An updated and theoretical rationale for interaction. The International Review of Research in Open and Distributed Learning, 4(2).
Anderson, T. & Garrison, D. R. (1998). Learning in a networked world: New roles and responsibilities,
en Distance Learners in Higher Education: Institutional responses for quality outcomes. Madison, Wi.: Atwood.
Baykal, S. I.; Bulut, D. & Sahingoz, O. K. (2018, April). Comparing deep learning performance on BigData by using CPUs and GPUs, en 2018 Electric Electronics, Computer Science, Biomedical Engineerings’ Meeting (EBBT) (pp. 1-6). IEEE.
Bray, E.; Aoki, K. & Dlugosh, L. (2008). Predictors of learning satisfaction in Japanese online distance learners. International Review of Research in Open and Distance Learning, 9(3).
Bringula, R. P.; Álvarez, J. N.; Evangelista, M. A. & So, R. B. (2017). Learner-interface interactions with mobile-assisted learning in mathematics: Effects on and relationship with mathematics performance, en K-12 STEM Education: Breakthroughs in Research and Practice (pp. 305-321). IGI Global.
Bringula, R. P.; Basa, R. S.; De la Cruz, C. & Rodrigo, M. M. T. (2016). Effects of prior knowledge in mathematics on learner-interface interactions in a learning-by-teaching intelligent tutoring system. Journal of Educational Computing Research, 54(4), 462-482.
Chang, Y. (2009). An action research of integrating computer technology in academic writing for undergraduates. World Academy of Science, Engineering and Technology, 37, 981-984.
Cho, M. H. & Cho, Y. (2017). Self-regulation in three types of online interaction: A scale development. Distance Education, 38(1), 70-83.
Cho, T. (2011). The impact of types of interaction on student satisfaction in online courses. International Journal on E-Learning: Corporate, Government, Healthcare, and Higher Education, 10(2), 109-125.
Chou, C.; Peng, H. & Chang, C. (2010). The technical framework of interactive functions for course-management systems: Students’ perceptions, uses, and evaluations. Computers and Education, 55(3), 1004-1017. http://doi.org/10.1016/j.compedu.2010.04.011
Clark, R. C. (2020). Evidence-based training methods: A guide for training professionals. American Society for Training and Development.
Cope, B.; Kalantzis, M. & Searsmith, D. (2020). Artificial intelligence for education: Knowledge
and its assessment in AI-enabled learning ecologies. Educational Philosophy and Theory. http://doi.org/10.1080/00131857.2020.1728732
Danesh, A.; Bailey, A. & Whisenand, T. (2015). Technology and instructor-interface interaction in distance education. International Journal of Business and Social Science, 6(2).
Dewey, J. (1916). Democracy and education: An introduction to the philosophy of education. Macmillan.
Doleck, T.; Lemay, D. J.; Basnet, R. B. & Bazelais, P. (2019). Predictive analytics in education: A comparison
of deep learning frameworks. Education and Information Technologies, 1-13. http://doi.org/10.1007/s10639-019-10068-4
Dwivedi, Y. K.; Hughes, L.; Ismagilova, E.; Aarts, G.; Coombs, C.; Crick, T. & Galanos, V. (2019). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management,101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Garg, T. (2020). Artificial intelligence in medical education. American Journal of Medicine, 133(2), e68. http://doi.org/10.1016/j.amjmed.2019.08.017
Gunawardena, C. (1999). The challenge of designing and evaluating ‘interaction’ in web-based distance education, en WebNet World Conference on the WWW and Internet (pp. 451-456). Association for the Advancement of Computing in Education (AACE).
Gunesekera, A. I.; Bao, Y. & Kibelloh, M. (2019). The role of usability on e-learning user interactions and satisfaction: A literature review. Journal of Systems and Information Technology, 21(3), 368-394
Hillman, D. C.; Willis, D. J. & Gunawardena, C. N. (1994). Learner-interface interaction in distance
education: An extension of contemporary models and strategies for practitioners. American Journal of Distance Education, 8(2), 30-42.
Hsiao, E. L. & Huang, X. (2014). Promoting interactivity in a distance course for nontraditional students. RealLife Distance Education: Case Studies in Practice, 235.
Jancheski, M. (2017). Interaction in distance education, en 11th International Conference on Technology, Education and Development (INTED). Location: Valencia, SPAIN: INTED2017: 11th international technology, education and development conference Book Series: INTED Proceedings.
Jia, J. (2020). Educational stages and interactive learning: From kindergarten to workplace training. Information sciences reference, 437. IGI Global.
Kennepohl, D. (2020). Editorial. International Review of Research in Open and Distributed Learning, 21(2), i-iii. Recuperado de: https://doi.org/10.19173/irrodl.v21i2.4812
Koola, P. M.; Ramachandran, S. & Vadakkeveedu, K. (2016). How do we train a stone to think? A review of machine intelligence and its implications. Theoretical Issues in Ergonomics Science, 17(2), 211-238.
Kumar, S. G. & Reddy, G. H. (2019). Prediction of education performance using deep learning. International Journal of Innovative Technology and Exploring Engineering, 8(7),361-364
Lee, M. K. (2015). Effects of mobile phone-based app learning compared to computer-based web learning on nursing students: Pilot randomized controlled trial. Healthcare Informatics Research, 21(2), 125-133. http://doi.org/10.4258/hir.2015.21.2.125
Longo, L. (2020). Empowering qualitative research methods in education with artificial intelligence. http://doi.org/10.1007/978-3-030-31787-4_1
Luckin, R. & Cukurova, M. (2019). Designing educational technologies in the age of AI: A learning sciences-driven approach. British Journal of Educational Technology, 50(6), 2824-2838.
Luo, H. & Lei, J. (2012). Emerging technologies for interactive learning in the ICT age. Educational stages and interactive learning: From kindergarten to workplace training (pp. 73-91). http://doi.org/10.4018/978-1-4666-0137-6.ch005
Martin, F.; Parker, M. A. & Deale, D. F. (2012). Examining interactivity in synchronous virtual classrooms. International Review of Research in Open and Distance Learning, 13(3), 228-261.
Mayer, R. E. (ed.) (2014). The Cambridge handbook of multimedia learning. Cambridge University Press.
Mladenova, M. & Kirkova, D. (2014). Role of student interaction interface in web-based distance learning, en ACHI 2014-7th International Conference on Advances in Computer-Human Interactions (pp. 307-312).
Mohan, S.; Ramea, K.; Price, B.; Shreve, M.; Eldardiry, H. & Nelson, L. (2019). Building Jarvis-A learner-aware conversational trainer. IUI Workshops on User-Aware Conversational Agents in conjunction with ACM Intelligent User Interfaces 2019, Los Angeles, California.
Moore, M. G. (1989). Editorial: Three types of interaction. The American Journal of Distance Education, 3(2).
Morgan, H. (2020). Best practices for implementing remote learning during a pandemic. The Clearing House: A Journal of Educational Strategies, Issues and Ideas, 93(3), 135-141. Recuperado de: https://doi.org/10.1080/00098655.2020.1751480
Nacu, D.; Martin, C. K. & Pinkard, N. (2018). Designing for 21st century learning online: A heuristic method to enable educator learning support roles. Educational Technology Research and Development, 66(4), 1029-1049. http://doi.org/10.1007/s11423-018-9603-0
Nahar, L.; Sulaiman, R. & Jaafar, A. (2020). An interactive math braille learning application to assist blind students in Bangladesh. Assistive Technology (en prensa). https://doi.org/10.1080/10400435.2020.1734112
Nappi, C. & Cuocolo, A. (2020). The machine learning approach: Artificial intelligence is coming to support critical clinical thinking. Journal of Nuclear Cardiology, 27(1), 156-158. http://doi.org/10.1007/s12350-018-1344-2
Nathoo, A.; Bekaroo, G.; Gangabissoon, T. & Santokhee, A. (2020). Using tangible user interfaces for teaching concepts of internet of things. Interactive Technology and Smart Education (en prensa). https://doi.org/10.1108/ITSE-09-2019-0061
Piaget, J. (1971). Genetic epistemology. Norton & Co Inc. Raees, M. & Ullah, S. (2020). THE-3DI: Tracing head and eyes for 3D interactions. Multimedia Tools and Applications, 79(1-2), 1311-1337.
Rampton, V.; Mittelman, M. & Goldhahn, J. (2020). Implications of artificial intelligence for medical education. The Lancet Digital Health, 2(3), e111-e112. http://doi.org/10.1016/S2589-7500(20)30023-6
Rautopuro, J.; Pöntinen, S. & Kukkonen, J. (2006). Towards the information society - the case of Finnish teacher education. Informatics in Education, 5(2), 285-300.
Ricco, J.; Guetarni, F. & Kolh, P. (2020). Learning from artificial intelligence and big data in health care. European Journal of Vascular and Endovascular Surgery. http://doi.org/10.1016/j.ejvs.2020.01.019
Schlegelmilch, J. & Douglas, C. (2020). Initial Covid-19 closure strategies adopted by a convenience sample of US school districts: Directions for future research. Disaster Medicine and Public Health Preparedness, 1-4. http://doi.org/10.1017/dmp.2020.147
Scolari, C. A. (2018). Las leyes de la interfaz: diseño, ecología evolución, tecnología (vol. 136). Editorial: Gedisa.
Şimşek, Ö.; Atman, N.; Inceoğlu, M. M. & Arikan, Y. D. (2010). Diagnosis of learning styles based on active/reflective dimension of Felder and Silverman’s learning style model in a learning management system, en International Conference on Computational Science and Its Applications (pp. 544-555). Springer, Berlin, Heidelberg. http://doi.org/10.1007/978-3-642-12165-4-43
Tidwell, J.; Brewer, C. & Valencia-Brooks, A. (2020). Designing interfaces: Patterns for effective interaction design. O’Reilly Media, Inc.
UNESCO. (2020). Covid-19 Impact on Education. Recuperado de: https://en.unesco.org/covid19/educationresponse
Villegas-Ch, W.; Arias-Navarrete, A. & Palacios-Pacheco, X. (2020). Proposal of architecture for the integration of a chatbot with artificial intelligence in a smart campus for the improvement of learning. Sustainability (Switzerland), 12(4). http://doi.org/10.3390/su12041500
Vygotsky, L. S. (1980). Mind in society: The development of higher psychological processes. Harvard University Press.
Walsh, K. (2020). Artificial intelligence and healthcare professional education: Superhuman resources for health? Postgraduate Medical Journal, 96(1133), 121-122. http://doi.org/10.1136/postgradmedj-2019-137132
Wang, C. & Tsai, W. (2016). Elucidating how interface design and cognitive function affect learning performance in the enterprise resource planning (ERP) software system. Journal of Testing and Evaluation, 44(1), 31-46. http://doi.org/10.1520/JTE20140044
Wang, H.; Rush, B. R.; Wilkerson, M. & Van Der Merwe, D. (2014). Exploring the use of tablet pcs in veterinary medical education: Opportunity or obstacle? Journal of Veterinary Medical Education, 41(2), 122-131. http://doi.org/10.3138/jvme.1013-145R1
WBIS (2018). Proceedings -2017 international workshop on big data and information security, en Proceedings – WBIS 2017: 2017 International Workshop on Big Data and Information Security.
Wong, G. K. W.; Ma, X.; Dillenbourg, P. & Huen, J. (2020). Broadening artificial intelligence education in K-12: Where to start? ACM Inroads, 11(1), 20-29. http://doi.org/10.1145/3381884
Xiao, J. (2017). Learner-content interaction in distance education: The weakest link in interaction research. Distance Education, 38(1), 123-135.
Yang, S. & Bai, H. (2020). The integration design of artificial intelligence and normal students’ education. the Journal of Physics: Conference Series, 1453(1). http://doi.org/10.1088/1742-6596/1453/1/012090
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