The Study of Designing Interactive Learning Experiences: Improving Education through Computer-Human Interaction
DOI: 10.23977/aetp.2023.071813 | Downloads: 28 | Views: 498
Author(s)
Yiding Xia 1,2
Affiliation(s)
1 The University of Art London, London, United Kingdom
2 KUNLUNXIN (Beijing) Technology CO., LTD, Beijing, China
Corresponding Author
Yiding XiaABSTRACT
Utilizing computer-human interactions, this study investigates the creation of collaborative learning experiences. Encouragement of engagement, memory retention improvement, and the implementation of personalized learning strategies are its main objectives. In order to build interesting learning environments, a number of approaches are being researched, including "gamified learning, virtual simulations, and individualized learning modules". The study uses a deductive research strategy, a positivist research mindset, and a secondary data collection technique. Results reflect favorable comments and high levels of participant satisfaction with interactive activities. The study comes to the conclusion that driven by technology interactive learning can revolutionize education by encouraging participation, greater comprehension, and personalized learning opportunities. The implications point to the possibility of a dynamic and successful instructional paradigm for students as well as teachers.
KEYWORDS
Interactive learning, Computer-human interaction, Artificial intelligence, Education through AICITE THIS PAPER
Yiding Xia, The Study of Designing Interactive Learning Experiences: Improving Education through Computer-Human Interaction. Advances in Educational Technology and Psychology (2023) Vol. 7: 84-90. DOI: http://dx.doi.org/10.23977/aetp.2023.071813.
REFERENCES
[1] Ahma A, Mozelius, P. January. (2019) Critical factors for human computer interaction of ehealth for older adults. In Proceedings of the 5th International Conference on e-Society, e-Learning and e-Technologies, 58-62.
[2] Ahumada-Newhart V, Olson J S. (2019) Going to school on a robot: Robot and user interface design features that matter. ACM Transactions on Computer-Human Interaction (TOCHI), 26(4): 1-28.
[3] Fujita N. (2020) Transforming online teaching and learning: towards learning design informed by information science and learning sciences. Information and Learning Sciences, 121(7/8): 503-511.
[4] Gauthier A, Porayska-Pomsta K, Dumontheil I, et al. (2022) Manipulating interface design features affects children's stop-and-think behaviours in a counterintuitive-problem game. ACM Transactions on Computer-Human Interaction, 29(2): 1-21.
[5] Carmichael D, MacEachen C, Archibald J. (2022) Gamification in a learning resource for the study of Human Computer Interaction. In Intelligent Computing: Proceedings of the 2021 Computing Conference, Volume 1, 697-717, Springer International Publishing.
[6] Liu Y, Lin Y, Shi R. et al. (2021) Relicvr: A virtual reality game for active exploration of archaeological relics. In Extended Abstracts of the 2021 Annual Symposium on Computer-Human Interaction in Play, 326-332.
[7] Lv Z, Poiesi F, Dong Q. et al. (2022) Deep learning for intelligent human–computer interaction [J]. Applied Sciences, 12(22): 11457.
[8] Menges R, Kumar C, Staab S. (2019). Oving user experience of eye tracking-based interaction: Introspecting and adapting interfaces. ACM Transactions on Computer-Human Interaction (TOCHI), 26(6): 1-46.
[9] Ortega M. (2021) Computer-human interaction and collaboration: challenges and prospects. Electronics, 10(5): 616.
[10] Siddique S, Chow J C. (2021) Machine learning in healthcare communication. Encyclopedia, 1(1): 220-239.
[11] Tuli N, Mantri A. (2020) Experience Fleming’s rule in electromagnetism using augmented reality: Analyzing impact on students learning. Procedia Computer Science. 172: 660-668.
[12] Maloney D, Freeman G, Robb A. (2020) virtual space for all: Exploring children's experience in social virtual reality. In Proceedings of the Annual Symposium on Computer-Human Interaction in Play, 472-483.
[13] Padi S, Sadjadi S O, Manocha D. et al. (2022) Multimodal emotion recognition using transfer learning from speaker recognition and bert-based models. arXiv preprint arXiv:2202.08974.
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