ChatGPT: A new study tool shaping the future for high school students
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DOI:
https://doi.org/10.59287/ijanser.562Keywords:
ChatGPT, High School Students, Study Tool, Academic Performance, Digital ResourcesAbstract
With the rapid progression of technology and the growing presence of natural language processing applications in everyday life, it is essential to explore how high school students engage with these tools and how they foresee their futures in light of these advancements. The goal of this study is to analyse the usage patterns and future value perceptions of ChatGPT among 70 high school students through a survey-based approach. A key finding highlights that technology has become an integral element of contemporary life, underscoring the historical relevance of Natural Language Processing (NLP) and the eagerness of the younger generation to adopt such emerging technologies. High school students utilise ChatGPT for various purposes, including academic support, social communication, and personal management, across both educational and social contexts. Moreover, the participants conveyed a positive outlook on the potential of ChatGPT to significantly impact their lives in the coming years while acknowledging possible hurdles. Based on the findings of this study, it is clear that NLP tools like ChatGPT have a crucial role in moulding the experiences and anticipations of high school students. This paper, therefore, sets the stage for additional research and development in this area.
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K. Czakóová and V. Stoffová. (2019) Training teachers of computer science for teaching algorithmization and programming. In: IMSCI´20 proceedings, The 14th International Multi-conference on Society, Cybernetics and Informatics : Proceedings (Post-Conference Edition). Winter Garden: International Institute of Informatics and Systemics, 2020, p. 231-235. ISBN 978-1-950492-40-4.
M. Baygin, H. Yetis, M. Karakose and E. Akin. (2016) An Effect Analysis of Industry 4.0 to Higher Education. 15th International Conference on Information Technology Based Higher Education and Training (ITHET), 1-4. https://doi.org/10.1109/ITHET. 2016.7760744.
F. Llorens-Largo. (2015). Dicen por ahí…que la nueva alfabetización pasa por la programacion. ReVisión, 8(2), 11-14.
A. Benesova, M. Hirman, F. Steiner and J. Tupa. (2019) Requirements for Education 4.0 and study programs within Industry 4.0. Proceedings of the International Conference on Industrial Engineering and Operations Management, Pilsen, Czech Republic, July 23-26. IEOM Society International.
J. Udvaros, Á. Gubán and M. Gubán. (2019) Methods of artificial intelligence in economical and logistical education. eLearning and Software for Education Conference, pp. 414–421. http://dx.doi.org/10.12753/2066-026x-19-055
J. M. Wing. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35. https://doi.org/10.1145/1118178.1118215.
J. Udvaros and M. Gubán. (2016). Demonstration the class, objects and inheritance concepts by software. Acta Didactica Napocensia, 9 (1). pp. 23-34. ISSN 2065-1430
J. Udvaros and L. Végh. (2020). New teaching methods by using microcontrollers in teaching programming. eLearning and Software for Education 2020. Bukarest 1 pp. 630-637. Paper: 82 , 8 p. http://doi.org/10.12753
/2066-026X-20-119
M. T. Fülöp, J. Udvaros, Á. Gubán, and Á. Sándor. (2022) Development of Computational Thinking Using Microcontrollers Integrated into OOP (Object-Oriented Programming). Sustainability, 14 12, 7218, https://doi.org/10.3390/su14127218
R. Barna and V. Honfi. (2022) Kütyüzni jó!: Bevezetés az Arduino programozásába, Magyar Agrár- és Élettudományi Egyetem Kaposvári Campus, MATER Press - Egyetemi Kiadványok, Kaposvár, Hungary, ISBN: 9786155599934
J. Udvaros and O. Takáč. (2022) Technical IT solutions in teaching, INTED2022 Proceedings, pp. 4047-4052. https://doi.org/10.21125/inted.2022.1107
G. S. Sharath, N. Hiremath and G. Manjunatha. (2020) Design and analysis of Gantry robot for pick and place mechanism with Arduino Mega 2560 microcontroller and processed using Pythons. https://doi.org/10.1016/
j.matpr.2020.11.965
A. J. Magana and G. S. Coutinho. (2017) Modeling and simulation practices for a computational thinking‐enabled engineering workforce. Computer Applications in Engineering Education, 25 (1), pp. 62-78, https://doi.org/10.1002/cae.21779
J. Udvaros and O. Takáč. (2020) Developing Computational Thinking By Microcontrollers. In: L., Gómez Chova; A., López Martínez; I., Candel Torres. ICERI2020 Proceedings 13th International Conference of Education, Research and Innovation. Valencia, IATED Academy, pp. 6877-6882., 6 p. https://doi.org/ 10.21125/iceri.2020.1474
J. Udvaros and K. Czakóová. (2021) Developing of computational thinking using microcontrollers and simulations, EDULEARN21 Proceedings, pp. 7945-7951. https://doi.org/10.21125/edulearn.2021.1619
J. Udvaros and K. Czakóová. (2021) Developing of computational thinking using microcontrollers and simulations, EDULEARN21 Proceedings, pp. 7945-7951. https://doi.org/10.21125/edulearn.2021.1619
J. Udvaros and K. Czakóová. (2021) Using teaching methods based on visualizing by TinkerCad in teaching programming, ICERI2021 Proceedings, pp. 5913-5917. https://doi.org/10.21125/iceri.2021.1333
J. Udvaros and N. Forman. (2023) Artificial Intelligence and Education 4.0, INTED2023 Proceedings, pp. 6309-6317. https://doi.org/10.21125/inted.2023.1670
Sweller, J. (1988). Cognitive load during problem-solving: Effects on learning. Cognitive Science, vol. 12, no. 2, pp. 257-285. [DOI: 10.1207/s15516709cog1202_4]
van Merriënboer, J. J., & Sweller, J. (2010). Cognitive load theory in health professional education: Design principles and strategies. Medical Education, vol. 44, no. 1, pp. 85-93. [DOI: 10.1111/j.1365-2923.2009.03498.x]
McGeoch, J. A. (1932). Forgetting and the law of disuse. Psychological Review, vol. 39, no. 4, pp. 352-370. [DOI: 10.1037/h0069819]
Anderson, J. R., & Neely, J. H. (1996). Interference and inhibition in memory retrieval. Memory, pp. 237-313.
Cepeda, N. J., Vul, E., Rohrer, D., Wixted, J. T., & Pashler, H. (2006). Spacing effects in learning: A temporal ridgeline of optimal retention. Psychological Science, vol. 17, no. 11, pp. 1095-1100. [DOI: 10.1111/j.1467-9280.2006.01824.x]
Kang, S. H. K. (2016). Spaced repetition promotes efficient and effective learning: Policy implications for instruction. Policy Insights from the Behavioral and Brain Sciences, vol. 3, no. 1, pp. 12-19. [DOI: 10.1177/2372732215624708]
Craik, F. I., & Lockhart, R. S. (1972). Levels of processing: A framework for memory research. Journal of Verbal Learning and Verbal Behavior, vol. 11, no. 6, pp. 671-684. [DOI: 10.1016/S0022-5371(72)80001-X]
Weinstein, Y., Nunes, L. D., & Karpicke, J. D. (2018). On the placement of practice questions during study. Journal of Experimental Psychology: Applied, vol. 24, no. 1, pp. 97-108. [DOI: 10.1037/xap0000147]
Xie, H., Chu, H. C., Hwang, G. J., & Wang, C. C. (2020). Trends and development in technology-enhanced adaptive/personalized learning: A systematic review of journal publications from 2007 to 2017. Computers & Education, vol. 140, article 103599. [DOI: 10.1016/j.compedu.2019.103599]
Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving language understanding by generative pre-training.
Prensky, M. (2001). Digital natives, digital immigrants. On the Horizon, vol. 9, no. 5, NCB University Press.
Selwyn, N. (2016). Education and technology: Key issues and debates. Bloomsbury Publishing.
Greenhow, C., Robelia, B., & Hughes, J. E. (2009). Learning, teaching, and scholarship in a digital age: Web 2.0 and classroom research: What path should we take now? Educational Researcher, vol. 38, no. 4, pp. 246-259. [DOI: 10.3102/0013189X09336671]
Lenhart, A., Purcell, K., Smith, A., & Zickuhr, K. (2010). Social media & mobile internet use among teens and young adults. Pew Internet & American Life Project.
Loader, B. D., Vromen, A., & Xenos, M. A. (2014). The networked young citizen: Social media, political participation and civic engagement. Routledge.
Perks, L. G. (2015). Media marathoning: Immersions in morality. Lexington Books.
Hamari, J., & Sjöblom, M. (2017). What is eSports and why do people watch it? Internet Research, vol. 27, no. 2, pp. 211-232. [DOI: 10.1108/IntR-04-2016-0085]
boyd, d. (2014). It's complicated: The social lives of networked teens. Yale University Press.
Twenge, J. M., Joiner, T. E., Rogers, M. L., & Martin, G. N. (2018). Increases in depressive symptoms, suicide-related outcomes, and suicide rates among U.S. adolescents after 2010 and links to increased new media screen time. Clinical Psychological Science, vol. 6, no. 1, pp. 3-17. [DOI: 10.1177/2167702617723376]
Nabi, R. L., Prestin, A., & So, J. (2013). Facebook friends with (health) benefits? Exploring social network site use and perceptions of social support, stress, and well-being. Cyberpsychology, Behavior, and Social Networking, vol. 16, no. 10, pp. 721-727. [DOI: 10.1089/cyber.2012.0521]
Gurrapadi, N., Zhang, Y., & Bowman, S. R. (2020). Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Artificial Intelligence Research, vol. 67, no. 1, pp. 1-31.
Feldman, A., Ogan, A., & Ramachandran, D. (2021). The impact of AI-driven conversational agents on student learning and emotions in higher education. Computers & Education, vol. 168, no. 1, pp. 104146. [DOI: 10.1016/j.compedu.2021.104146]
Xia, H., Zhang, X., & Zhao, R. (2021). AI-based writing evaluation and feedback: A review of the literature. Frontiers in Artificial Intelligence, vol. 4, no. 1, pp. 1-12. [DOI: 10.3389/frai.2021.682212]
Zmigrod, L., Mielke, S., Wallis, T., & Flanagan, R. (2020). Addressing harmful bias in contextualized embeddings: An evaluation on name-based gender inference. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 5303-5314. [DOI: 10.18653/v1/2020.emnlp-main.427]
