Inteligencia Artificial Offline para la Educación: un camino hacia un campo más inclusivo
DOI:
https://doi.org/10.5753/rbie.2023.3156Keywords:
análisis de datos educativos, equidad digital, desconectadaAbstract
El campo de la Inteligencia Artificial (IA) tiene el potencial de mejorar la enseñanza y el aprendizaje, por ejemplo, mediante el análisis de datos producidos en entornos educativos. Además, también puede empeorar la desigualdad, ya que requiere que los estudiantes e instructores tengan acceso a la infraestructura (smartphones o computadoras) requerida por la mayoría de esas herramientas para generar y analizar datos. Sin embargo, el acceso a dicha infraestructura no es una realidad para muchos estudiantes de todo el mundo. Para arrojar luz sobre este problema, este documento investiga, a través de un Estudio de Mapeo Sistemático (SMS), iniciativas que permitan un análisis de datos más inclusivo utilizando IA en la educación, especialmente en escenarios con pocos recursos de conectividad. Identificamos que estas iniciativas son escasas y están enfocadas en la primera fase de la tarea de análisis de datos: la recolección de datos. Con base en los resultados de SMS, proponemos un conjunto de recomendaciones para que los investigadores ofrezcan direcciones hacia un análisis más inclusivo de datos educativos usando IA.
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Cruzes, D.S., Dyba, T.: Recommended steps for thematic synthesis in software engineering. In: 2011 International Symposium on Empirical Software Engineering and Measurement. pp. 275–284 (2011). 10.1109/ESEM.2011.36 [GS Search]
Devedžić, V.: Introduction to web-based education. Semantic Web and Education pp. 1–28 (2006). 10.1007/978-0-387-35417-0_1 [GS Search]
van Dijk, J.: The Digital Divide. Polity Press (2020)
Eradze, M., Väljataga, T., Laanpere, M.: Observing the use of e-textbooks in the classroom: Towards "offline" learning analytics. In: Cao, Y., V¨aljataga, T., Tang, J.K., Leung, H., Laanpere, M. (eds.) New Horizons in Web Based Learning. pp. 254–263. Springer International Publishing, Cham (2014). 10.1007/978-3-319-13296-9_28, [GS Search]
Ferguson, R.: Learning analytics: Drivers, developments and challenges. Int. J. Technol. Enhanc. Learn. 4(5/6), 304–317 (jan 2012). 10.1504/IJTEL.2012.051816, [GS Search]
Fincher, S., Robins, A.: The Cambridge Handbook of Computing Education Research. Cambridge Handbooks in Psychology, Cambridge University Press (2019)
Gašević, D.: Include us all! directions for adoption of learning analytics in the global south. Lim, C. P., Tinio, V. L. (Eds.). (2018). Learning analytics for the global south. Quezon City, Philippines: Foundation for Information Technology Education and Development. pp. 1–22 (2018), available at [Link]
Guan, C., Mou, J., Jiang, Z.: Artificial intelligence innovation in education: A twenty-year data-driven historical analysis. International Journal of Innovation Studies 4(4), 134–147 (2020). 10.1016/j.ijis.2020.09.001, [GS Search]
Hillier, M.: Bridging the digital divide with off-line e-learning. Distance Education 39(1), 110–121 (2018). 10.1080/01587919.2017.1418627, [GS Search]
Holstein, K., Doroudi, S.: Equity and artificial intelligence in education: Will "aied" amplify or alleviate inequities in education? (2021). 10.48550/arXiv.2104.12920, [GS Search]
Katz, V., Rideout, V.: Learning at home while under-connected (2021), available at [Link]
Kinshuk, Han, B., Hong, H., Patel, A.: Student adaptivity in tile: a client-server approach. In: Proceedings IEEE International Conference on Advanced Learning Technologies. pp. 297–300 (2001). 10.1109/ICALT.2001.943927, [GS Search]
Kitchenham, B., Budgen, D., Brereton, P.: Evidence-based software engineering and systematic reviews (1st ed.) (2015). 10.1201/b19467, [GS Search]
Konomi, S., Hu, X., Gu, C., Mushi, D.: Designing a distributed cooperative data substrate for learners without internet access. In: Streitz, N.A., Konomi, S. (eds.) Distributed, Ambient and Pervasive Interactions. Smart Living, Learning, Well-being and Health, Art and Creativity. pp. 137–147. Springer International Publishing, Cham (2022). 10.1007/978-3-031-05431-0_10, [GS Search]
Konomi, S., Gao, L., Mushi, D.: An intelligent platform for offline learners based on modeldriven crowdsensing over intermittent networks. In: International Conference on Human- Computer Interaction. pp. 300–314. Springer (2020). 10.1007/978-3-030-49913-6_26, [GS Search]
Labba, C., Ben Atitallah, R., Boyer, A.: Combining artificial intelligence and edge computing to reshape distance education (case study: K-12 learners). In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds.) Artificial Intelligence in Education. pp. 218–230. Springer International Publishing, Cham (2022). 10.1007/978-3-031-11644-5_18, [GS Search]
Livingston, J., Steele, R.: A crowdsensing algorithm for imputing zika outbreak location data. In: 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON). pp. 334–340 (2017). 10.1109/UEMCON.2017.8249065, [GS Search]
Nunn, S.G., Avella, J.T., Kanai, T., Kebritchi, M.: Learning analytics methods, benefits, and challenges in higher education: A systematic literature review. Online Learning 20(2), 13–29 (2016). 10.24059/olj.v20i2.790, [GS Search]
Patel, N., Thakkar, M., Rabadiya, B., Patel, D., Malvi, S., Sharma, A., Lomas, D.: Equitable access to intelligent tutoring systems through paper-digital integration. In: Crossley, S., Popescu, E. (eds.) Intelligent Tutoring Systems. pp. 255–263. Springer International Publishing, Cham (2022). 10.1007/978-3-031-09680-8_24, [GS Search]
Prinsloo, P., Kaliisa, R.: Learning analytics on the african continent: An emerging research focus and practice. Journal of Learning Analytics 9(2), 218–235 (Jun 2022). 10.18608/jla.2022.7539, [GS Search]
Puigjaner, R.: Progressing toward digital equity. In: Mata, F.J., Pont, A. (eds.) ICT for Promoting Human Development and Protecting the Environment. pp. 109–120. Springer International Publishing, Cham (2016). 10.1007/978-3-319-44447-5_11, [GS Search]
Thompson, G.: Two thirds of the world’s school-age children have no internet access at home, new unicef-itu report says (2020), available at [Link]
United Nations: The sustainable development goals report 2022 (2022), available at [Link]
Verma, S., Gros, A., Dluhos, M.: An architectural design for learning analytics in remote education environments. In: Twenty-fourth Americas Conference on Information Systems AMCIS, New Orleans (2018), available at [Link]
Wang, Y., Zhang, T., Yu, X.: A component-detection-based approach for interpreting off-line handwritten chemical cyclic compound structures. In: 2021 IEEE International Conference on Engineering, Technology Education (TALE). pp. 785–791 (2021). 10.1109/TALE52509.2021.9678874, [GS Search]
World Bank: Harnessing artificial intelligence for development in the post-covid-19 era: A review of national ai strategies and policies. World Bank Group p. 47 (2021), available at [Link]
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Derechos de autor 2023 Elyda Laisa Soares Xavier Freitas, Ig Ibert Bittencourt, Seiji Isotani, Leonardo Marques, Diego Dermeval, Alan Silva, Rafael Ferreira Mello
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
