Comprendiendo el potencial y los desafíos del Big Data en las escuelas y la educación
Palabras clave:
toma de decisiones, procesamiento electrónico de datos, ayuda para enseñar, Administración educacional
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial 4.0.
Resumen
En los últimos años, el mundo ha experimentado una gran revolución centrada en la recopilación y aplicación de Big Data en varios campos. Esto ha afectado muchos aspectos de nuestra vida diaria, incluidos el gobierno, la manufactura, el comercio, la salud, la comunicación, el entretenimiento y muchos más. Hasta ahora, la educación se ha beneficiado muy poco de la revolución del Big Data. En este manuscrito, revisamos el potencial de los grandes datos en el contexto de los sistemas educativos. Dichos datos pueden incluir archivos de registro extraídos de entornos de aprendizaje en línea, mensajes en foros de discusión en línea, respuestas a preguntas abiertas, calificaciones en diversas tareas, información demográfica y administrativa, discurso, notas escritas a mano, ilustraciones, gestos y movimientos, señales neurofisiológicas, ojo movimientos, y muchos más. Al analizar estos datos, es posible calcular una amplia gama de mediciones del proceso de aprendizaje y apoyar a diversos interesados educativos con una toma de decisiones informada. Ofrecemos un marco para una mejor comprensión de cómo se pueden utilizar los grandes datos en la educación. El marco comprende varios elementos que deben abordarse en este contexto: definición de los datos; formulación de aparatos de recolección y almacenamiento de datos; análisis de datos y la aplicación de productos de análisis. Además, revisamos algunas oportunidades clave y algunos desafíos importantes del uso de Big Data en la educación.
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Adelman, M., Haimovich, F., Ham, A., & Vazquez, E. (2018). Predicting school dropout with administrative data: New evidence from Guatemala and Honduras. Education Economics, 26(4), 356–372. doi: doi.org/10.1080/09645292.2018.1433127.
Alexandron, G., Keinan, G., Levy, B., & Hershkovitz, S. (2018). Evaluating the effectiveness of animated cartoons in an intelligent math tutoring system using educational data mining. In EdMedia + Innovate Learning (Vol. 2018, pp. 719–730). Waynesville, NC.
Alexandron, G., Ruipérez-Valiente, J. A., Chen, Z., Muñoz-Merino, P. J., & Pritchard, D. E. (2017). Copying@scale: Using harvesting accounts for collecting correct answers in a MOOC. Computers and Education, 108, 96–114. doi: doi.org/10.1016/j.compedu.2017.01.015.
Almanac 2017. (2017). Cumulative growth in number of MOOCs 2011–2017. The Chronicle of Higher Education.
Berland, M., Baker, R. S., & Blikstein, P. (2014). Educational data mining and learning analytics: Applications to constructionist research. Technology, Knowledge and Learning, 19(1–2), 205–220. doi: doi.org/10.1007/s10758-014-9223-7.
Blikstein, P., & Worsley, M. (2016). Multimodal Learning Analytics and Education Data Mining: Using Computational Technologies to Measure Complex Learning Tasks. Journal of Learning Analytics, 3(2), 220–238. doi: doi.org/10.18608/jla.2016.32.11.
Chrysafiadi, K., & Virvou, M. (2013). Student modeling approaches: A literature review for the last decade. Expert Systems with Applications, 40(11), 4715–4729. doi: doi.org/10.1016/J.ESWA.2013.02.007.
Cocea, M., Hershkovitz, A., & Baker, R. S. J. D. (2009). The impact of off-task and gaming behaviors on learning: Immediate or aggregate, The International Conference on Artificial Intelligence and Applications. doi: doi.org/10.3233/978-1-60750-028-5-507.
Collins, A., & Halverson, R. (2018). Rethinking education in the age of technology: The digital revolution and schooling in America (second edition). New York, NY: Teachers College Press.
Corbett, A. T., & Anderson, J. R. (1995). Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modelling and User-Adapted Interaction, 4(4), 253–278. doi: doi.org/10.1007/BF01099821.
del Blanco, A., Serrano, A., Freire, M., Martínez-Ortiz, I., & Fernández-Manjón, B. (2013). E-Learning Standards and Learning Analytics. IEEE Global Engineering Education Conference, 1255–1261. doi: doi.org/10.1109/EduCon.2013.6530268.
Fancsali, S. E., & Ritter, S. (2018). Assessments that care about student learning. In N. Guin & A. Kumar (Eds.), 14th International Conference on Intelligent Tutoring: Workshop Proceedings (pp. 135–140). Montreal, QC.
Ferguson, R., Brasher, A., Clow, D., Cooper, A., Hillaire, G., Mittelmeier, J., … Vuorikari, R. (2016). Research evidence on the use of learning analytics: Implications for education policy. Seville, Spain: Publ. Off. of the Europ. Union.
Fincham, E., Whitelock-Wainwright, A., Kovanović, V., Joksimović, S., van Staalduinen, J. P., & Gašević, D. (2019). Counting clicks is not enough: Validating a theorized model of engagement in learning analytics. In the 9th International Learning Analytics & Knowledge Conference (pp. 501–510). Tempe, AZ. doi: doi.org/10.1145/3303772.3303775.
Gal, T., & Hershkovitz, A. (2019). Different response-based feedback in mathematics: The case of symbolic and textual messages. In The 9th International Learning Analytics & Knowledge Conference. doi: doi.org/10.1145/3303772.3303815.
Glaser, R. (1977). Adaptive education: Individual diversity and learning. Holt, Rinehart and Winston.
Graesser, A. C. (2016). Conversations with AutoTutor help students learn. International Journal of Artificial Intelligence in Education, 26(1), 124–132. doi: doi.org/10.1007/s40593-015-0086-4.
Heffernan, N. T., & Heffernan, C. L. (2014). The ASSISTments ecosystem: Building a platform that brings scientists and teachers together for minimally invasive research on human learning and Teaching. International Journal of Artificial Intelligence in Education, 24(4), 470–497. doi: doi.org/10.1007/s40593-014-0024-x.
Herder, E., Sosnovsky, S., & Dimitrova, V. (2017). Adaptive intelligent learning environments. In D. E., S. M., & S. R. (Eds.), Technology Enhanced Learning (pp. 109-114). Cham, Switzerland: Springer. doi: doi.org/10.1007/978-3-319-02600-8_10.
Hershkovitz, A., de Baker, R. S. J. R. S. J., Gobert, J., Wixon, M., & Pedro, M. S. M. S. (2013). Discovery with models: A case study on carelessness in computer-based science inquiry. American Behavioral Scientist, 57(10), 1480–1499. doi: doi.org/10.1177/0002764213479365.
Ho, A. (2017). Workshop on big data in education: Balancing the benefits of educational research and student privacy. Washington, DC.
Holmes, N. (2018). Engaging with assessment: Increasing student engagement through continuous assessment. Active Learning in Higher Education, 19(1), 23–34. doi: doi.org/10.1177/1469787417723230.
Kahan, T., Soffer, T., & Nachmias, R. (2017). Types of participant behavior in a massive open online course. International Review of Research in Open and Distance Learning, 18(6), 1–18. doi: doi.org/10.19173/irrodl.v18i6.3087.
Klein, C., & Hess, R. M. (2018). Using learning analytics to improve student learning outcomes assessment: Benefits, constraints, & possibilities. In A. Lester, C. Klein, A. Johri, & H. Rangwala (Eds.), Learning Analytics in Higher Education (pp. 140–159). New York, NY: Routledge. doi: doi.org/10.4324/9780203731864-7.
Knowles, J. (2015). Building an accurate statewide dropout early warning system in Wisconsin. Journal of Educational Data Mining, 7(3), 18–67.
Lang, C., Siemens, G., Wise, A., & Gasevic, D. (Eds.). (2017). Handbook of Learning Analytics. Society for Learning Analytics Research (SoLAR). doi: doi.org/10.18608/hla17.
Manrique, R., Nunes, B. P., Marino, O., Casanova, M. A., & Nurmikko-Fuller, T. (2019). An analysis of student representation, representative features and classification algorithms to predict degree dropout. In Proceedings of the 9th International Conference on Learning Analytics & Knowledge - LAK19 (pp. 401–410). doi: doi.org/10.1145/3303772.3303800.
Nkambou, R. (2010). Modeling the domain: An introduction to the expert module. In N. R., B. J., & M. R. (Eds.), Advances in Intelligent Tutoring Systems (pp. 15–32). Berlin, Germany: Springer. doi: doi.org/10.1007/978-3-642-14363-2_2.
Packer, M. J., & Maddox, C. (2016). Mapping the territory of the learning sciences. In M. A. Evans, M. J. Packer, & R. K. Sawyer (Eds.), Reflections on the Learning Sciences (pp. 126–154). New York, NY: Cambridge University Press. doi: doi.org/10.1017/cbo9781107707221.007.
Papamitsiou, Z., & Economides, A. A. (2014). Learning analytics and educational data mining in practice: A systematic literature review of empirical evidence. Educational Technology & Society (Vol. 17).
Paquette, L., Baker, R. S. J. D., Sao Pedro, M. A., Gobert, J. D., Rossi, L., Nakama, A., & Kauffman-Rogoff, Z. (2014). Sensor-free affect detection for a simulation-based science inquiry learning environment. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8474 LNCS, pp. 1–10). Springer, Cham. doi: doi.org/10.1007/978-3-319-07221-0_1.
Paquette, L., Biswas, G., Baker, R. S., Rajendran, R., Munshi, A., & Ocumpaugh, J. (2018). Modeling learners’ cognitive and affective states to scaffold SRL in open-ended learning environments. In Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization (pp. 131–138). Singapore. doi: doi.org/10.1145/3209219.3209241.
Pardos, Z. A., & Xu, Y. (2016). Improving efficacy attribution in a self-directed learning environment using prior knowledge individualization, 435–439. doi: doi.org/10.1145/2883851.2883949.
Richards, G., & DeVries, I. (2012). Revisiting formative evaluation. Proceedings of the 1st International Conference on Learning Analytics and Knowledge – LAK ’11, 157. doi: doi.org/10.1145/2090116.2090141.
Romero, C., Ventura, S., Pechenizkiy, M., & Baker, R. S. J. d. (Eds.). (2010). Handbook of Educational Data Mining - CRC Press Book. Boca Raton, FL: CRC Press. doi: doi.org/10.1201/b10274.
San Pedro, M. O. Z., Baker, R. S. J. d., Bowers, A. J., & Heffernan, N. T. (2013). Predicting college enrollment from student interaction with an intelligent tutoring system in middle school. In The Sixth International Conference on Educational Data Mining (pp. 177–184). Memphis, TN.
Shah, D. (2016). By the numbers: MOOCS in 2016. Retrieved from https://www.classcentral.com/report/mooc-stats-2016/.
Simon, J. (2017). A priori knowledge in learning analytics. In A. Peña-Ayala (Ed.), Learning Analytics: Fundaments, Applications, and Trends (Vol. 94, pp. 199–227). Cham, Switzerland: Springer. doi: doi.org/10.1007/978-3-319-52977-6.
Syed, M., Anggara, T., Lanski, A., Duan, X., Ambrose, G. A., & Chawla, N. V. (2019). Integrated closed-loop learning analytics scheme in a First Year Experience course. In Proceedings of the 9th International Conference on Learning Analytics & Knowledge (pp. 521–530). Tempe, AZ. doi: doi.org/10.1145/3303772.3303803.
Tsai, Y.-S., Moreno-Marcos, P. M., Tammets, K., Kollom, K., & Gašević, D. (2018). SHEILA policy framework. In Proceedings of the 8th International Conference on Learning Analytics and Knowledge – LAK ’18 (pp. 320–329). New York, New York, USA: ACM Press. doi: doi.org/10.1145/3170358.3170367.
Verbert, K., Duval, E., Klerkx, J., Govaerts, S., & Santos, J. L. (2013). Learning analytics dashboard applications. American Behavioral Scientist, 57(10), 1500–1509. doi: doi.org/10.1177/0002764213479363.
Ye, C., Biswas, G., & Biswas, G. (2016). Early prediction of student dropout and performance in MOOCs using higher granularity temporal information. Journal of Learning Analytics, 1(3), 169–172. doi: doi.org/10.18608/jla.2014.13.14.
Yoo, Y., Lee, H., Jo, I.-H., & Park, Y. (2015). Educational dashboards for smart learning: Review of case studies. In Emerging Issues in Smart Learning (pp. 145–155). Berlin: Springer-Verlag. doi: doi.org/10.1007/978-3-662-44188-6_21.