Técnicas e aplicações da Aprendizagem Automática e da Inteligência Artificial na Educação: uma revisão sistemática da literatura.
DOI:
https://doi.org/10.5944/ried.27.1.37491Palavras-chave:
machine learning, inteligência artificial, tecnologia educativa, tecnologia emergente, educação digitalResumo
A Aprendizagem Automática (AM) é um domínio da inteligência artificial (IA) que, ultimamente, está a ter impacto em todas as áreas do conhecimento. As áreas das ciências sociais, em especial a educação, não lhe são inerentes, pelo que se procedeu a uma revisão sistemática da literatura sobre as técnicas e aplicações da Aprendizagem Automática e da inteligência artificial na Educação. As questões de pesquisa foram: (1) Em quais níveis educacionais foram realizados estudos de AM ou IA na educação; (2) Em quais países foram realizadas pesquisas de AM ou IA na educação e quais têm maior influência; (3) Quais são as palavras-chave dos estudos; (4) Quais técnicas de AM foram utilizadas na pesquisa; e (5) Quais foram os resultados da implementação de AM ou IA como uma tecnologia emergente na educação? As bases de dados utilizadas para a pesquisa bibliográfica foram Web of Science e Scopus, a metodologia aplicada é baseada na declaração PRISMA para a coleta e análise de 55 artigos publicados em periódicos de alto impacto entre os anos de 2021-2023. Os resultados mostraram que os estudos abordaram um total de 33 técnicas de AM e IA e múltiplas aplicações que foram implementadas em contextos educacionais nos níveis de ensino primário, secundário e superior em 38 países. As conclusões mostraram o forte impacto da utilização do AM e da IA em diferentes contextos no setor da educação e as diferentes implicações que daí advêm.
Downloads
Referências
Ahajjam, T., Moutaib, M., Aissa, H., Azrour, M., Farhaoui, Y., & Fattah, M. (2022). Predicting Students’ Final Performance Using Artificial Neural Networks. Big Data Mining and Analytics, 5(4), 294-301. https://doi.org/10.26599/BDMA.2021.9020030
Ahmed, A., Aziz, S., Qidwai, U., Farooq, F., Shan, J., Subramanian, M., Chouchane, L., EINatour, R., Abd-Alrazaq, A., Pandas, S., & Sheikh, J. (2023). Wearable Artificial Intelligence for Assessing Physical Activity in High School Children. Sustainability (Switzerland), 15(1), 1-12. https://doi.org/10.3390/su15010638
Ali, S., DiPaola, D., Lee, I., Sindato, V., Kim, G., Blumofe, R., & Breazeal, C. (2021). Children as creators, thinkers and citizens in an AI-driven future. Computers and Education: Artificial Intelligence, 2, 100040. https://doi.org/10.1016/j.caeai.2021.100040
Ali, S., DiPaola, D., Lee, I., Hong, J., & Breazeal, C. (2021). Exploring generative models with middle school students. Conference on Human Factors in Computing Systems - Proceedings. https://doi.org/10.1145/3411764.3445226
Aljabri, M., Chrouf, S. M. B., Alzahrani, N. A., Alghamdi, L., Alfehaid, R., Alqarawi, R., Alhuthayfi, J., & Alduhailan, N. (2021). Sentiment analysis of arabic tweets regarding distance learning in saudi arabia during the covid-19 pandemic. Sensors, 21(16). https://doi.org/10.3390/s21165431
Almeida Pereira Abar, C. A., Dos Santos Dos Santos, J. M., & de Almeida, M. V. (2021). Computational Thinking in Elementary School in the Age of Artificial Intelligence: Where is the Teacher? Revista de Ensino de Ciencias y Matemática, 23(6), 270-299. https://doi.org/10.17648/acta.scientiae.6869
Almoqbil, A., O’Connor, B. C., Anderson, R., Shittu, J., & McLeod, P. (2021). Modeling deception: A case study of email phishing. Proceedings from the Document Academy, 8(2). https://doi.org/10.35492/docam/8/2/8
Alshaikh, K., Bahurmuz, N., Torabah, O., Alzahrani, S., Alshingiti, Z., & Meccawy, M. (2021). Using Recommender Systems for Matching Students with Suitable Specialization: An Exploratory Study at King Abdulaziz University. International Journal of Emerging Technologies in Learning, 16(3), 316-324. https://doi.org/10.3991/ijet.v16i03.17829
Alvarado Uribe, J., Mejía Almada, P., Masetto Herrera, A. L., Molontay, R., Hilliger, I., Hegde, V., Montemayor Gallegos, J. E., Ramírez Díaz, R. A., & Ceballos, H. G. (2022). Student Dataset from Tecnologico de Monterrey in Mexico to Predict Dropout in Higher Education. Data, 7(9). https://doi.org/10.3390/data7090119
An, X., Chai, C. S., Li, Y., Zhou, Y., Shen, X., Zheng, C., & Chen, M. (2022). Modeling English teachers’ behavioral intention to use artificial intelligence in middle schools. Education and Information Technologies. https://doi.org/10.1007/s10639-022-11286-z
Angara, P. P., Stege, U., MacLean, A., Muller, H. A., & Markham, T. (2022). Teaching Quantum Computing to High-School-Aged Youth: A Hands-On Approach. IEEE Transactions on Quantum Engineering, 3. https://doi.org/10.1109/TQE.2021.3127503
Araya, R., & Sossa-Rivera, J. (2021). Automatic Detection of Gaze and Body Orientation in Elementary School Classrooms. Frontiers in Robotics and AI, 8(September), 1-11. https://doi.org/10.3389/frobt.2021.729832
Ayanwale, M. A., Sanusi, I. T., Adelana, O. P., Aruleba, K. D., & Oyelere, S. S. (2022). Teachers’ readiness and intention to teach artificial intelligence in schools. Computers and Education: Artificial Intelligence, 3(August), 100099. https://doi.org/10.1016/j.caeai.2022.100099
Baashar, Y., Hamed, Y., Alkawsi, G., Fernando Capretz, L., Alhussian, H., Alwadain, A., & Al-amri, R. (2022). Evaluation of postgraduate academic performance using artificial intelligence models. Alexandria Engineering Journal, 61(12), 9867-9878. https://doi.org/10.1016/j.aej.2022.03.021
Bakker, T., Krabbendam, L., Bhulai, S., Meeter, M., & Begeer, S. (2023). Predicting academic success of autistic students in higher education. Autism. https://doi.org/10.1177/13623613221146439
Ban, H., & Ning, J. (2021). Online English Teaching Based on Artificial Intelligence Internet Technology Embedded System. Mobile Information Systems, 2021. https://doi.org/10.1155/2021/2593656
Bellas, F., Guerreiro-Santalla, S., Naya, M., & Duro, R. J. (2022). AI Curriculum for European High Schools: An Embedded Intelligence Approach. International Journal of Artificial Intelligence in Education, 0123456789. https://doi.org/10.1007/s40593-022-00315-0
Bhavana, S., & Vijayalakshmi, V. (2022). AI-Based Metaverse Technologies Advancement Impact on Higher Education Learners. WSEAS Transactions on Systems, 21, 178-184. https://doi.org/10.37394/23202.2022.21.19
Blease, C., Kharko, A., Annoni, M., Gaab, J., & Locher, C. (2021). Machine Learning in Clinical Psychology and Psychotherapy Education: A Mixed Methods Pilot Survey of Postgraduate Students at a Swiss University. Frontiers in Public Health, 9(April). https://doi.org/10.3389/fpubh.2021.623088
Bogina, V., Hartman, A., Kuflik, T., & Shulner-Tal, A. (2022). Educating Software and AI Stakeholders About Algorithmic Fairness, Accountability, Transparency and Ethics. International Journal of Artificial Intelligence in Education, 32(3), 808-833. https://doi.org/10.1007/s40593-021-00248-0
Bosch, N. (2021). Identifying supportive student factors for mindset interventions: A two-model machine learning approach. Computers and Education, 167(March), 104190. https://doi.org/10.1016/j.compedu.2021.104190
Bruno, G. di D. (2021). Erwhi Hedgehog: A New Learning Platform for Mobile Robotics. In Lecture Notes in Networks and Systems (Vol. 240). Springer International Publishing. https://doi.org/10.1007/978-3-030-77040-2_32
Burgess, S., Metcalfe, R., & Sadoff, S. (2021). Understanding the response to financial and non-financial incentives in education: Field experimental evidence using high-stakes assessments. Economics of Education Review, 85(July), 102195. https://doi.org/10.1016/j.econedurev.2021.102195
Byun, A., & Kim, H. (2022). The Effect of Design Classes Using Artificial Intelligence in the Era of COVID-19 on Social Responsibility of High School Students. Archives of Design Research, 35(4), 251-266. https://doi.org/10.15187/adr.2022.11.35.4.251
Ceha, J., Law, E., Kulić, D., Oudeyer, P. Y., & Roy, D. (2022). Identifying Functions and Behaviours of Social Robots for In-Class Learning Activities: Teachers’ Perspective. International Journal of Social Robotics, 14(3), 747-761. https://doi.org/10.1007/s12369-021-00820-7
Chen, B., Chen, H., & Li, M. (2021). Improvement and Optimization of Feature Selection Algorithm in Swarm Intelligence Algorithm Based on Complexity. Complexity, 2021. https://doi.org/10.1155/2021/9985185
Cheng, J., Chae, M. H. C., & Feng, R. (2021). Stem education-career pathway for emerging forensic analytics: Innovative professional development in multimodal environments. Journal of Higher Education Theory and Practice, 21(8), 115-130. https://doi.org/10.33423/jhetp.v21i8.4509
Chrysafiadi, K., Virvou, M., Tsihrintzis, G. A., & Hatzilygeroudis, I. (2022). Evaluating the user’s experience, adaptivity and learning outcomes of a fuzzy-based intelligent tutoring system for computer programming for academic students in Greece. In Education and Information Technologies (Issue 0123456789). Springer US. https://doi.org/10.1007/s10639-022-11444-3
Costa Mendes, R., Oliveira, T., Castelli, M., & Cruz Jesus, F. (2021). A machine learning approximation of the 2015 Portuguese high school student grades: A hybrid approach. Education and Information Technologies, 26(2), 1527-1547. https://doi.org/10.1007/s10639-020-10316-y
Dai, Y., Liu, A., Qin, J., Guo, Y., Jong, M. S. Y., Chai, C. S., & Lin, Z. (2022). Collaborative construction of artificial intelligence curriculum in primary schools. Journal of Engineering Education, October 2022, 23-42. https://doi.org/10.1002/jee.20503
Demchenko, M. V., Gulieva, M. E., Larina, T. V., & Simaeva, E. P. (2021). Digital Transformation of Legal Education: Problems, Risks and Prospects. European Journal of Contemporary Education, 10(2), 297-307. https://doi.org/10.13187/ejced.2021.2.297
Demir, K., & Güraksın, G. E. (2022). Determining middle school students’ perceptions of the concept of artificial intelligence: A metaphor analysis. Participatory Educational Research, 9(2), 297-312. https://doi.org/10.17275/per.22.41.9.2
Dietz, G., Chen, J. K., Beason, J., Tarrow, M., Hilliard, A., & Shapiro, R. B. (2022). ARtonomous: Introducing Middle School Students to Reinforcement Learning Through Virtual Robotics. Proceedings of Interaction Design and Children, IDC 2022, 430-441. https://doi.org/10.1145/3501712.3529736
Dogadina, E. P., Smirnov, M. V., Osipov, A. V., & Suvorov, S. V. (2021). Formation of the optimal load of high school students using a genetic algorithm and a neural network. Applied Sciences (Switzerland), 11(11). https://doi.org/10.3390/app11115263
Duncan, D., Garner, R., Bennett, A., Sinclair, M., Ramirez-De La Cruz, G., & Pasik-Duncan, B. (2022). Interdisciplinary K-12 Control Education in Biomedical and Public Health Applications. IFAC-PapersOnLine, 55(17), 242-248. https://doi.org/10.1016/j.ifacol.2022.09.286
Duzhin, F., & Gustafsson, A. (2018). Machine learning-based app for self-evaluation of teacher-specific instructional style and tools. Education Sciences, 8(1), 1-15. https://doi.org/10.3390/educsci8010007
Eegdeman, I., Cornelisz, I., van Klaveren, C., & Meeter, M. (2022). Computer or teacher: Who predicts dropout best? Frontiers in Education, 7(November), 1-10. https://doi.org/10.3389/feduc.2022.976922
Eguchi, A., Okada, H., & Muto, Y. (2021). Contextualizing AI Education for K-12 Students to Enhance Their Learning of AI Literacy Through Culturally Responsive Approaches. KI - Kunstliche Intelligenz, 35(2), 153-161. https://doi.org/10.1007/s13218-021-00737-3
Fernández-Martínez, C., Hernán-Losada, I., & Fernández, A. (2021). Early Introduction of AI in Spanish Middle Schools. A Motivational Study. KI - Kunstliche Intelligenz, 35(2), 163-170. https://doi.org/10.1007/s13218-021-00735-5
Forero Corba, W., & Negre Bennasar, F. (2022). Revisión sistemática de la aplicación del Machine Learning en la Educación. EDUTEC Educación transformadora en un mundo digital: Conectando paisajes de aprendizaje, 416-419.
http://dspace.uib.es/xmlui/handle/11201/160593
Gerlache, H. A. M., Ger, P. M., & Valentín, L. de la F. (2022). Towards the Grade’s Prediction. A Study of Different Machine Learning Approaches to Predict Grades from Student Interaction Data. International Journal of Interactive Multimedia and Artificial Intelligence, 7(4), 196-204. https://doi.org/10.9781/ijimai.2021.11.007
Giam, N. M., Nam, N. T. H., & Giang, N. T. H. (2022). Situation and Proposals for Implementing Artificial Intelligence-based Instructional Technology in Vietnamese Secondary Schools. International Journal of Emerging Technologies in Learning, 17(18), 53-75. https://doi.org/10.3991/ijet.v17i18.31503
Horanai, H., Maejima, Y., & Ding, L. (2022). An Education Tool at Supports Junior Learners in Studying Machine Learning. Frontiers in Artificial Intelligence and Applications, 360, 111-116. https://doi.org/10.3233/FAIA220432
Houngue, P., Hountondji, M., & Dagba, T. (2022). An Effective Decision-Making Support for Student Academic Path Selection using Machine Learning. International Journal of Advanced Computer Science and Applications, 13(11), 727-734. https://doi.org/10.14569/IJACSA.2022.0131184
Liu, Y., Chen, L., & Yao, Z. (2022). The application of artificial intelligence assistant to deep learning in teachers’ teaching and students’ learning processes. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.929175
Luan, H., & Tsai, C. C. (2021). A Review of Using Machine Learning Approaches for Precision Education. Educational Technology and Society, 24(1), 250–266.
Luo, F., Jiang, L., Tian, X., Xiao, M., Ma, Y., & Zhang, S. (2021). Shyness prediction and language style model construction of elementary school students. Acta Psychologica Sinica, 53(2), 155-169. https://doi.org/10.3724/SP.J.1041.2021.00155
Marín, V. I. (2022). The systematic review in Educational Technology research: observations and advice. RiiTE Revista Interuniversitaria de Investigación En Tecnología Educativa, 13, 62-79. https://doi.org/10.6018/riite.533231
Mittal, S., Mahendra, S., Sanap, V., & Churi, P. (2022). International Journal of Information Management Data Insights How can machine learning be used in stress management : A systematic literature review of applications in workplaces and education. International Journal of Information Management Data Insights, 2(2), 100110. https://doi.org/10.1016/j.jjimei.2022.100110
Nafea, I. T. (2018). Machine Learning in Educational Technology. Machine Learning - Advanced Techniques and Emerging Applications. https://doi.org/10.5772/intechopen.72906
Oskotsky, T., Bajaj, R., Burchard, J., Cavazos, T., Chen, I., Connell, W., Eaneff, S., Grant, T., Kanungo, I., Lindquist, K., Myers-Turnbull, D., Naing, Z. Z. C., Tang, A., Vora, B., Wang, J., Karim, I., Swadling, C., Yang, J., Lindstaedt, B., & Sirota, M. (2022). Nurturing diversity and inclusion in AI in Biomedicine through a virtual summer program for high school students. PLoS Computational Biology, 18(1), 1-12. https://doi.org/10.1371/journal.pcbi.1009719
Pimentel, J. S., Ospina, R., & Ara, A. (2021). Learning Time Acceleration in Support Vector Regression: A Case Study in Educational Data Mining. Stats, 4(3), 682-700. https://doi.org/10.3390/stats4030041
Salas-Pilco, S. Z., & Yang, Y. (2022). Artificial intelligence applications in Latin American higher education: a systematic review. International Journal of Educational Technology in Higher Education, 19(1). https://doi.org/10.1186/s41239-022-00326-w
Salas Rueda, R. A., De la cruz Martínez, G., Eslava Cervantes, A. L., Castañeda Martínez, R., & Ramírez Ortega, J. (2022). Teachers’ opinion about collaborative virtual walls and massive open online course during the COVID-19 pandemic. Online Journal of Communication and Media Technologies, 12(1), 1-13. https://doi.org/10.30935/ojcmt/11305
Santos García, F., Valdivieso, K. D., Rienow, A., & Gairín, J. (2021). Urban–Rural Gradients Predict Educational Gaps: Evidence from a Machine Learning Approach Involving Academic Performance and Impervious Surfaces in Ecuador. ISPRS International Journal of Geo-Information, 10(12). https://doi.org/10.3390/ijgi10120830
Sanusi, I. T., Oyelere, S. S., & Omidiora, J. O. (2022). Exploring teachers’ preconceptions of teaching machine learning in high school: A preliminary insight from Africa. Computers and Education Open, 3 (November 2021), 100072. https://doi.org/10.1016/j.caeo.2021.100072
Sasmita, F., & Mulyanti, B. (2020). Development of machine learning implementation in engineering education: A literature review. IOP Conference Series: Materials Science and Engineering, 830(3). https://doi.org/10.1088/1757-899X/830/3/032061
Segura, M., Mello, J., & Herná, A. (2022). Machine Learning Prediction of University Student Dropout: Does Preference Play a Key Role? 1-20. https://doi.org/10.3390/math10183359
Su, J., Zhong, Y., Tsz, D., & Ng, K. (2022). Computers and Education : Artificial Intelligence A meta-review of literature on educational approaches for teaching AI at the K-12 levels in the Asia-Pacific region. Computers and Education: Artificial Intelligence, 3(March), 100065. https://doi.org/10.1016/j.caeai.2022.100065
Suzuki, H., Hong, M., Ober, T., & Cheng, Y. (2022). Prediction of differential performance between advanced placement exam scores and class grades using machine learning. Frontiers in Education, 7(December). https://doi.org/10.3389/feduc.2022.1007779
Taha, S. A., Shihab, R. A., & Sadik, M. C. (2018). Studying of Educational Data Mining Techniques. International Journal of Advanced Research in Science, Engineering and Technology, 5(5), 5742-5750. http://www.ijarset.com/upload/2018/may/9-IJARSET-SAJATAHA.pdf
Tarik, A., Aissa, H., & Yousef, F. (2021). Artificial intelligence and machine learning to predict student performance during the COVID-19. Procedia Computer Science, 184, 835-840. https://doi.org/10.1016/j.procs.2021.03.104
Temitayo, I., Sunday, S., & Olamide, J. (2022). Exploring teachers ’ preconceptions of teaching machine learning in high school : A preliminary insight from Africa. Computers and Education Open, 3(November 2021), 100072. https://doi.org/10.1016/j.caeo.2021.100072
Van Brummelen, J., Tabunshchyk, V., & Heng, T. (2021). “Alexa, Can I Program You?”: Student Perceptions of Conversational Artificial Intelligence before and after Programming Alexa. Proceedings of Interaction Design and Children, IDC 2021, 305-313. https://doi.org/10.1145/3459990.3460730
Yamamoto, S. H., & Alverson, C. Y. (2022). From high school to postsecondary education, training, and employment: Predicting outcomes for young adults with autism spectrum disorder. Autism and Developmental Language Impairments, 7. https://doi.org/10.1177/23969415221095019
Yepes-Nuñez, J. J., Urrútia, G., Romero-García, M., & Alonso-Fernández, S. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Revista Espanola de Cardiologia, 74(9), 790-799. https://doi.org/10.1016/j.recesp.2021.06.016
Yu, Y., Fan, J., Xian, Y., & Wang, Z. (2022). Graph Neural Network for Senior High Student’s Grade Prediction. Applied Sciences (Switzerland), 12(8). https://doi.org/10.3390/app12083881
Zafari, M., Sadeghi-Niaraki, A., Choi, S. M., & Esmaeily, A. (2021). A practical model for the evaluation of high school student performance based on machine learning. Applied Sciences (Switzerland), 11(23). https://doi.org/10.3390/app112311534
Zawacki-Richter, O., Kerres, M., Bedenlier, S., & Buntins, K. (2020). Systematic Reviews in Educational Research. In Systematic Reviews in Educational Research. https://doi.org/10.1007/978-3-658-27602-7
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education, 16(1). https://doi.org/10.1186/s41239-019-0171-0
Zhai, X., Chu, X., Chai, C. S., Jong, M. S. Y., Istenic, A., Spector, M., Liu, J. B., Yuan, J., & Li, Y. (2021). A Review of Artificial Intelligence (AI) in Education from 2010 to 2020. Complexity, 2021. https://doi.org/10.1155/2021/8812542
Zhu, J., & Liu, W. (2020). A tale of two databases: the use of Web of Science and Scopus in academic papers. Scientometrics, 123(1), 321-335. https://doi.org/10.1007/s11192-020-03387-8
Downloads
Publicado
Como Citar
Edição
Secção
Licença
Direitos de Autor (c) 2023 Wiston Forero-Corba, Francisca Negre Bennasar
Este trabalho encontra-se publicado com a Licença Internacional Creative Commons Atribuição 4.0.
As obras que são publicadas neste revista estão sujeitas ao seguintes termos:
1. Os autores cedem de forma não exclusiva os direitos de exploração dos trabalhos aceitos para sua publicação a "RIED. Revista Iberoamericana de Educação a Distância", garantem à revista o direito de ser a primeira publicação do trabalho e permitem que a revista distribua os trabalhos publicados sob a licença de indicada no ponto 2.
2. As obras são publicadas na edição eletrônica da revista sob uma licença Creative Commons Atribuição 4.0 Internacional (CC BY 4.0). Podem copiar e redistribuir o material em qualquer suporte ou formato, adaptar, remixar, transformar, e criar a partir do material para qualquer fim, mesmo que comercial. Você deve atribuir o devido crédito, fornecer um link para a licença, e indicar se foram feitas alterações. Você pode fazê-lo de qualquer forma razoável, mas não de uma forma que sugira que o licenciante o apoia ou aprova o seu uso.
3. Condições de auto-arquivo. Permite-se e incentava-se aos autores difundir eletronicamente a versõ OnlineFirst (versão avaliada e aceita para publicação) de su obra antes de sua publicação, sempre com referência a sua publicação na RIED, já que favorece sua circulação e difusão mais cedo e com isso um possível aumento em sua citação e alcance entre a comunidade acadêmica. Color RoMEO: verde.
