Surveying the Future of Computer and Data Science Education - Prospects and Pitfalls of Generative AI on Pedagogical Approaches
Resumo
This study investigates the role of generative Artificial Intelligence (AI), like ChatGPT and other Large Language Models (LLMs), on learning strategies among computer and data science students at the Center for Informatics, University of Paraíba (CI/UFPB), Brazil. Analyzing 178 responses, the research highlights a significant engagement with LLMs and discovers a moderate correlation between students' LLM knowledge and their use of metacognitive learning strategies. Additionally, findings suggest a decrease in dysfunctional learning strategies with academic progression. The study reveals AI's potential to improve personalized learning while emphasizing the need for educational adjustments to avoid overreliance on AI.
Referências
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Chiu, T. K. F., Moorhouse, B. L., Chai, C. S. and Ismailov, M. (2023). “Teacher support and student motivation to learn with Artificial Intelligence (AI) based chatbot”, Interactive Learning Environments. DOI: 10.1080/10494820.2023.2172044
Choi, J. H., Hickman, K. E., Monahan, A. and Schwarcs, D. B. (2023). “ChatGPT Goes to Law School.”, 71 Journal of Legal Education, v. 387, p. 1–16. DOI: 10.2139/ssrn.4335905
Damasio, B. (2012). “Uses of Exploratory Factorial Analysis in Psychology.”, Avaliação Psicológica, v. 11(2), p. 213–228. DOI: DOI: S1677-04712012000200007
Dunn, T. J., Baguley, T. and Brunsden, V. (2013). “From alpha to omega: A practical solution to the pervasive problem of internal consistency estimation.”, British Journal of Psychology, v. 105(3), p. 399–412. DOI: 10.1111/bjop.12046
Dwivedi, Y. K., Kshetri, N., Huges, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M.; Albanna, H., Albashrawi, M. A., Al-Busaidi, A., Balakrishnan, J., Barlette, Y.; Basu, S., Bose, I., (...) and Wright, R. (2023). “Opinion Paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy”, International Journal of Information Management, v. 71. DOI: 10.1016/j.ijinfomgt.2023.102642
Gao, S., and Gao, A. K. (2023). “On the Origin of LLMs: An Evolutionary Tree and Graph for 15,821 Large Language Models”, ArXiv preprints, p. 1–14. DOI: 10.48550/arXiv.2307.09793
Goretzko, D., Pham, T.T.H. and Bühner, M. (2021). “Exploratory factor analysis: Current use, methodological developments and recommendations for good practice”, Current Psychology, v. 40, p. 3510–3521. DOI: 10.1007/s12144-019-00300-2
Hadi, M. U., Al-Tashi, G.; Qureshi, R., Shah, A., Muneer, A., Shaikh, M. B., Al-Garadi, M. A., Wu, J. and Mirjalili, S. (2023). “Large Language Models: A Comprehensive Survey of Applications, Challenges, Limitations, and Future Prospects”, TechRxiv preprints, p. 1–44. DOI: 10.36227/techrxiv.23589741.v4
Hanum S. F., Hasmayni, B. and Lubis, A. H. (2023). “The Analysis of Chat GPT Usage Impact on Learning Motivation among Scout Students”, International Journal of Research and Review, v. 10(7), p. 632-–638. DOI: 10.52403/ijrr.20230774
Hung, J. and Chen, J. (2023). “The Benefits, Risks and Regulation of Using ChatGPT in Chinese Academia: A Content Analysis”, Social Sciences; 12(9), pp. 380. DOI: 10.3390/socsci12070380
Kasneci E., Sessler K., Küchemann S., Bannert M., Dementieva, D., Fischer F., Gasser, U, Groh G., Günnemann S., Hüllermeier E., Krusche S., Kutyniok G., Michaeli T., Nerdel C., Pfeffer J., Poquet O., Sailer M., Schmidt A., Seidel T., Stadler M., Weller J., Kuhn J. and Kasneci G. (2023). “ChatGPT for good? On opportunities and challenges of large language models for education”, Learning and Individual Differences, v. 103. DOI: 10.1016/j.lindif.2023.102274
Oliveira, A. F.; Caliatto, S. G. (2018). “Exploratory Factor Analysis of a Scale of Learning Strategies. Educação: Teoria e Prática”, v. 18(59), p. 548-565. DOI: 10.18675/1981-8106.vol28.n59.p548-565
Okonkwo, C. W. and Ade-Ibijola, A. (2021). “Chatbots applications in education: A systematic review. SComputers and Education: Artificial Intelligence”, v. 2, p. 380. DOI: 10.1016/j.caeai.2021.100033
Pereira, C. P. da S., Santos, A. A. A. and Ferraz, A. S. (2020). “Learning Strategies Assessment Scale for Vocational Education: Adaptation and psychometric studies”, Revista Portuguesa de Educação, v. 33(1), p. 75–93. DOI: 10.21814/rpe.19127
Ramos, A. S. M. (2023). “Generative Artificial Intelligence based on large language models - tools for use in academic research”, Scielo preprints. DOI: 10.1590/SciELOPreprints.6105
Rahman, M., Terano, H. J. R., Rahman, N. Salamzadeh, A. and Rahaman, S. (2023). “ChatGPT and Academic Research: A Review and Recommendations Based on Practical Examples”, Journal of Education, Management and Development Studies, v. 3(1), p. 1–12. DOI: 10.52631/jemds.v3i1.175
Shanahan, M. (2022). “Talking About Large Language Models”, ArXiv preprint, v. 5, p. 1–13. DOI: 10.48550/arXiv.2212.03551
Taherdoost, H., Sahibuddin, S. and Jalaliyoon, N. (2022). “Exploratory Factor Analysis: concepts and theory”, In: Advances in Applied and Pure Mathematics, organized by Jerzy Balicki, p.375–382. Available from: [link]
Theobald, M. (2021). “Self-regulated learning training programs enhance university students’ academic performance, self-regulated learning strategies, and motivation: A meta-analysis”, Contemporary Educational Psychology, v. 66. DOI: 10.1016/j.cedpsych.2021.101976
Tu, X., Zou, J. Su, W. J. and Zhang, L. (2023). “What Should Data Science Education Do with Large Language Models?”, arXiv preprints, p. 1–18. DOI: 10.48550/arXiv.2307.02792
Urban, M., Děchtěrenko, F., Lukavsky, J., Hrabalová, V., Švácha, F.; Brom, C. and Urban, K. (2024). “ChatGPT Improves Creative Problem-Solving Performance in University Students: An Experimental Study”, Computers & Education, v. 215. DOI: 10.1016/j.compedu.2024.105031
Yin, J., Goh, T., Yang, B. and Xiaobin, Y. (2023). “Conversation Technology With Micro-Learning: The Impact of Chatbot-Based Learning on Students’ Learning Motivation and Performance”, Journal of Educational Computing Research, v. 59(1). DOI: 10.1177/0735633120952067
Zhai, X. (2023). “ChatGPT User Experience: Implications for Education”, SSRN Eletronic Journal. DOI: 10.2139/ssrn.4312418
Chiu, T. K. F., Moorhouse, B. L., Chai, C. S. and Ismailov, M. (2023). “Teacher support and student motivation to learn with Artificial Intelligence (AI) based chatbot”, Interactive Learning Environments. DOI: 10.1080/10494820.2023.2172044
Choi, J. H., Hickman, K. E., Monahan, A. and Schwarcs, D. B. (2023). “ChatGPT Goes to Law School.”, 71 Journal of Legal Education, v. 387, p. 1–16. DOI: 10.2139/ssrn.4335905
Damasio, B. (2012). “Uses of Exploratory Factorial Analysis in Psychology.”, Avaliação Psicológica, v. 11(2), p. 213–228. DOI: DOI: S1677-04712012000200007
Dunn, T. J., Baguley, T. and Brunsden, V. (2013). “From alpha to omega: A practical solution to the pervasive problem of internal consistency estimation.”, British Journal of Psychology, v. 105(3), p. 399–412. DOI: 10.1111/bjop.12046
Dwivedi, Y. K., Kshetri, N., Huges, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M.; Albanna, H., Albashrawi, M. A., Al-Busaidi, A., Balakrishnan, J., Barlette, Y.; Basu, S., Bose, I., (...) and Wright, R. (2023). “Opinion Paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy”, International Journal of Information Management, v. 71. DOI: 10.1016/j.ijinfomgt.2023.102642
Gao, S., and Gao, A. K. (2023). “On the Origin of LLMs: An Evolutionary Tree and Graph for 15,821 Large Language Models”, ArXiv preprints, p. 1–14. DOI: 10.48550/arXiv.2307.09793
Goretzko, D., Pham, T.T.H. and Bühner, M. (2021). “Exploratory factor analysis: Current use, methodological developments and recommendations for good practice”, Current Psychology, v. 40, p. 3510–3521. DOI: 10.1007/s12144-019-00300-2
Hadi, M. U., Al-Tashi, G.; Qureshi, R., Shah, A., Muneer, A., Shaikh, M. B., Al-Garadi, M. A., Wu, J. and Mirjalili, S. (2023). “Large Language Models: A Comprehensive Survey of Applications, Challenges, Limitations, and Future Prospects”, TechRxiv preprints, p. 1–44. DOI: 10.36227/techrxiv.23589741.v4
Hanum S. F., Hasmayni, B. and Lubis, A. H. (2023). “The Analysis of Chat GPT Usage Impact on Learning Motivation among Scout Students”, International Journal of Research and Review, v. 10(7), p. 632-–638. DOI: 10.52403/ijrr.20230774
Hung, J. and Chen, J. (2023). “The Benefits, Risks and Regulation of Using ChatGPT in Chinese Academia: A Content Analysis”, Social Sciences; 12(9), pp. 380. DOI: 10.3390/socsci12070380
Kasneci E., Sessler K., Küchemann S., Bannert M., Dementieva, D., Fischer F., Gasser, U, Groh G., Günnemann S., Hüllermeier E., Krusche S., Kutyniok G., Michaeli T., Nerdel C., Pfeffer J., Poquet O., Sailer M., Schmidt A., Seidel T., Stadler M., Weller J., Kuhn J. and Kasneci G. (2023). “ChatGPT for good? On opportunities and challenges of large language models for education”, Learning and Individual Differences, v. 103. DOI: 10.1016/j.lindif.2023.102274
Oliveira, A. F.; Caliatto, S. G. (2018). “Exploratory Factor Analysis of a Scale of Learning Strategies. Educação: Teoria e Prática”, v. 18(59), p. 548-565. DOI: 10.18675/1981-8106.vol28.n59.p548-565
Okonkwo, C. W. and Ade-Ibijola, A. (2021). “Chatbots applications in education: A systematic review. SComputers and Education: Artificial Intelligence”, v. 2, p. 380. DOI: 10.1016/j.caeai.2021.100033
Pereira, C. P. da S., Santos, A. A. A. and Ferraz, A. S. (2020). “Learning Strategies Assessment Scale for Vocational Education: Adaptation and psychometric studies”, Revista Portuguesa de Educação, v. 33(1), p. 75–93. DOI: 10.21814/rpe.19127
Ramos, A. S. M. (2023). “Generative Artificial Intelligence based on large language models - tools for use in academic research”, Scielo preprints. DOI: 10.1590/SciELOPreprints.6105
Rahman, M., Terano, H. J. R., Rahman, N. Salamzadeh, A. and Rahaman, S. (2023). “ChatGPT and Academic Research: A Review and Recommendations Based on Practical Examples”, Journal of Education, Management and Development Studies, v. 3(1), p. 1–12. DOI: 10.52631/jemds.v3i1.175
Shanahan, M. (2022). “Talking About Large Language Models”, ArXiv preprint, v. 5, p. 1–13. DOI: 10.48550/arXiv.2212.03551
Taherdoost, H., Sahibuddin, S. and Jalaliyoon, N. (2022). “Exploratory Factor Analysis: concepts and theory”, In: Advances in Applied and Pure Mathematics, organized by Jerzy Balicki, p.375–382. Available from: [link]
Theobald, M. (2021). “Self-regulated learning training programs enhance university students’ academic performance, self-regulated learning strategies, and motivation: A meta-analysis”, Contemporary Educational Psychology, v. 66. DOI: 10.1016/j.cedpsych.2021.101976
Tu, X., Zou, J. Su, W. J. and Zhang, L. (2023). “What Should Data Science Education Do with Large Language Models?”, arXiv preprints, p. 1–18. DOI: 10.48550/arXiv.2307.02792
Urban, M., Děchtěrenko, F., Lukavsky, J., Hrabalová, V., Švácha, F.; Brom, C. and Urban, K. (2024). “ChatGPT Improves Creative Problem-Solving Performance in University Students: An Experimental Study”, Computers & Education, v. 215. DOI: 10.1016/j.compedu.2024.105031
Yin, J., Goh, T., Yang, B. and Xiaobin, Y. (2023). “Conversation Technology With Micro-Learning: The Impact of Chatbot-Based Learning on Students’ Learning Motivation and Performance”, Journal of Educational Computing Research, v. 59(1). DOI: 10.1177/0735633120952067
Zhai, X. (2023). “ChatGPT User Experience: Implications for Education”, SSRN Eletronic Journal. DOI: 10.2139/ssrn.4312418
Publicado
21/07/2024
Como Citar
ARAÚJO, Vitor Meneghetti Ugulino de; PINTO, Pedro Henrique Ramos; FERREIRA JUNIOR, Cleydson de Souza; MARQUES, Maria Jullyanna Ferreira; GOULART, Lutero Lima; AGUIAR, Gabriel Silva; LIRA, Paloma Duarte de; MENDES, Samuel José Fernandes.
Surveying the Future of Computer and Data Science Education - Prospects and Pitfalls of Generative AI on Pedagogical Approaches. In: WORKSHOP SOBRE EDUCAÇÃO EM COMPUTAÇÃO (WEI), 32. , 2024, Brasília/DF.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 501-512.
ISSN 2595-6175.
DOI: https://doi.org/10.5753/wei.2024.2103.
