AI learning modules for elementary students
Keywords:
artificial intelligence (AI), elementary students, module, AI learning, documentary researchAbstract
The article is a documentary research. The results were brought for assessment and trial, with the objectives to synthesize AI learning components for elementary students and to create the prototype AI learning modules for elementary students. The results revealed as follows. 1) There were 5 AI learning components for elementary students, i.e., (1) perception (of the computer world by using sensors), (2) representation & reasoning (3) machine learning (ML), (4) natural interaction, and (5) societal impacts. These 5 dimensions covered the principles of teaching educational concepts and AI. 2) There were 5 prototype AI learning modules for elementary students, i.e., (1) Perception Module, (2) Representation & Reasoning Module, (3) Machine Learning Module, (4) Natural Interaction Module, and (5) Societal Impact Module; with practice through teaching media in accordance with the contents. There was an intelligent decision support system (IDSS) to assess AI learning competency for students. To clarify, the system predicted each dimensions of their AI ability. There was also an intelligent advice management system (IAMS) for teachers to reinforce learning management. The prototype modules were assessed by 5 experts. It was found that the suitability of most modules was highest, followed by the high ones.
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Alberola, J. M., Del Val, E., Sanchez-Anguix, V., Palomares, A., & Teruel, M. D. (2016). An artificial intelligence tool for heterogeneous team formation in the classroom. Knowledge- Based Systems, 101, 1-14. https://doi.org/10.1016/ j.knosys.2016.02.010
Ali, S., Payne, B. H., Williams, R., Park, H. W., & Breazeal, C. (2019, June). Constructionism, ethics, and creativity: Developing primary and middle school artificial intelligence education. In International Workshop on Education in Artificial Intelligence K-12 (EDUAI'19) (pp. 1-4). http://robotic.media.mit.edu/wpcontent/uploads/sites/7/2019/08/Constructionism__Ethics__and_Creativity.pdf
Chai, C. S., Lin, P. Y., Jong, M. S. Y., Dai, Y., Chiu, T. K., & Huang, B. (2020, August). Factors Influencing Students' Behavioral Intention to Continue Artificial Intelligence Learning. In 2020 International Symposium on Educational Technology (ISET) (pp. 147-150). IEEE.
Chai, C. S., Wang, X., & Xu, C. (2020). An extended theory of planned behavior for the modelling of Chinese secondary school students’ intention to learn Artificial Intelligence. Mathematics, 8(11), 2089. https://doi.org/10.3390/math8112089
Dzikovska, M., Steinhauser, N., Farrow, E., Moore, J., & Campbell, G. (2014). BEETLE II: Deep natural language understanding and automatic feedback generation for intelligent tutoring in basic electricity and electronics. International Journal of Artificial Intelligence in Education, 24(3), 284-332. https://doi.org/10.1007/s40593-014-0017-9
Goldstein, I., & Papert, S. (1977). Artificial intelligence, language, and the study of knowledge. Cognitive science, 1(1), 84-123. https://doi.org/10.1016/S0364-0213(77)80006-2
Heinze, C. A., Haase, J., & Higgins, H. (2010, July). An action research report from a multi-year approach to teaching artificial intelligence at the k-6 level. In First AAAI Symposium on Educational Advances in Artificial Intelligence. https://www.aaai.org/ocs/index.php/EAAI/EAAI10/paper/viewPaper/1746
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education. Boston: Center for Curriculum Redesign. https://curriculumredesign.org/wp-content/uploads/AIED-Book-Excerpt-CCR.pdf
https://doi.org/10.1109/ISET49818.2020.00040
Kandlhofer, M., Steinbauer, G., Hirschmugl-Gaisch, S., & Huber, P. (2016, October). Artificial intelligence and computer science in education: From kindergarten to university. In 2016 IEEE Frontiers in Education Conference (FIE) (pp. 1-9). IEEE. https://doi.org/10.1109/FIE.2016.7757570
Khachatryan, G. A., Romashov, A. V., Khachatryan, A. R., Gaudino, S. J., Khachatryan, J. M., Guarian, K. R., & Yufa, N. V. (2014). Reasoning Mind Genie 2: An intelligent tutoring system as a vehicle for international transfer of instructional methods in mathematics. International Journal of Artificial Intelligence in Education, 24(3), 333-382. https://doi.org/10.1007/s40593-014-0019-7
Klassner, F., & Anderson, S. D. (2003). Lego MindStorms: Not just for K-12 anymore. IEEE robotics & automation magazine, 10(2), 12-18. https://doi.org/10.1109 /MRA.2003.1213611
Langley, P. (2019, July). An integrative framework for artificial intelligence education. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 33, No. 01, pp. 9670-9677). https://doi.org/10.1609/aaai.v33i01.33019670
Lenat, D. B., & Durlach, P. J. (2014). Reinforcing math knowledge by immersing students in a simulated learning-by-teaching experience. International Journal of Artificial Intelligence in Education, 24(3), 216-250. https://doi.org/10.1007/s40593-014-0016-x
Malik, G., Tayal, D. K., & Vij, S. (2019). An analysis of the role of artificial intelligence in education and teaching. In Recent Findings in Intelligent Computing Techniques (pp. 407-417). Springer, Singapore. https://doi.org/10.1007/978-981-10-8639-7_42
McCalla, G. (2000). The fragmentation of culture, learning, teaching and technology: implications for the artificial intelligence in education research agenda in 2010. International Journal of Artificial Intelligence in Education, 11(2), 177-196. https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.100.7762&rep=rep1&typ=pdf
Ner, R. H. (2020). Evolution and Revolution in Artificial Intelligence. Evolution, 11(2). https://doi.org/10.48175/IJARSCT-612
Ouariachi, T., Li, C. Y., & Elving, W. J. (2020). Gamification approaches for education and engagement on pro-environmental behaviors: Searching for best practices. Sustainability, 12(11), 4565. https://doi.org/10.3390/su12114565
Pareto, L. (2014). A teachable agent game engaging primary school children to learn arithmetic concepts and reasoning. International Journal of Artificial Intelligence in Education, 24(3), 251-283. https://doi.org/10.1007/s40593-014-0018-8
Pérez, A. V. P., Borges, C. G. R. B., & Rodríguez, J. A. P. R. (2019). Photovoltaic system proposal for a house. International Journal of Physical Sciences and Engineering, 3(2), 34–43. https://doi.org/10.29332/ijpse.v3n2.330
Putra, I. G. W., Warpala, I. W. S., Sudatha, I. G. W., & Utami, N. P. R. S. (2022). Developing a Gagne theory-based learning video for thematic subject in elementary school. International Research Journal of Management, IT and Social Sciences, 9(4), 666-675. https://doi.org/10.21744/irjmis.v9n4.2149
Rau, M. A., Aleven, V., Rummel, N., & Pardos, Z. (2014). How should intelligent tutoring systems sequence multiple graphical representations of fractions? A multi-methods study. International Journal of Artificial Intelligence in Education, 24(2), 125-161. https://doi.org/10.1007/s40593-013-0011-7
Saragih, J. M., Lucey, S., & Cohn, J. F. (2011). Deformable model fitting by regularized landmark mean-shift. International journal of computer vision, 91(2), 200-215. https://doi.org/10.1007/s11263-010-0380-4
Timms, M. J. (2016). Letting artificial intelligence in education out of the box: educational cobots and smart classrooms. International Journal of Artificial Intelligence in Education, 26(2), 701-712. https://doi.org/10.1007/s40593-016-0095-y
Toivonen, T., Jormanainen, I., Kahila, J., Tedre, M., Valtonen, T., & Vartiainen, H. (2020, July). Co-designing machine learning apps in k–12 with primary school children. In 2020 IEEE 20th International Conference on Advanced Learning Technologies (ICALT) (pp. 308-310). IEEE. https://doi.org/10.1109/ICALT49669.2020.00099
Touretzky, D., Gardner-McCune, C., Martin, F., & Seehorn, D. (2019, July). Envisioning AI for K-12: What should every child know about AI?. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 33, No. 01, pp. 9795-9799). https://doi.org/10.1609/aaai.v33i01.33019795
Widana, I.K., Sumetri, N.W., Sutapa, I.K., Suryasa, W. (2021). Anthropometric measures for better cardiovascular and musculoskeletal health. Computer Applications in Engineering Education, 29(3), 550–561. https://doi.org/10.1002/cae.22202
Xiao, Y., & Hu, J. (2019). Assessment of optimal pedagogical factors for Canadian ESL learners’ reading literacy through artificial intelligence algorithms. International Journal of English Linguistics, 9(4), 1-14. https://doi.org/10.5539/ijel.v9n4p1
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