Analítica de aprendizaje en MOOC mediante métricas dinámicas en tiempo real

  1. León Urrutia, Manuel 2
  2. Vázquez Cano, Esteban 3
  3. López Meneses, Eloy 1
  1. 1 Universidad Pablo de Olavide
    info

    Universidad Pablo de Olavide

    Sevilla, España

    ROR https://ror.org/02z749649

  2. 2 University of Southampton
    info

    University of Southampton

    Southampton, Reino Unido

    ROR https://ror.org/01ryk1543

  3. 3 Universidad Nacional de Educación a Distancia
    info

    Universidad Nacional de Educación a Distancia

    Madrid, España

    ROR https://ror.org/02msb5n36

Aldizkaria:
@tic. revista d'innovació educativa

ISSN: 1989-3477

Argitalpen urtea: 2017

Zenbakien izenburua: Spring (January-June)

Zenbakia: 18

Orrialdeak: 38

Mota: Artikulua

DOI: 10.7203/ATTIC.18.10022 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Beste argitalpen batzuk: @tic. revista d'innovació educativa

Laburpena

Este artículo presenta el diseño y funcionamiento de una experiencia de analítica mediante una plataforma que ofrece métricas dinámicas en tiempo real denominada “MOOC Dashboard”. La experiencia se ha desarrollado por la Universidad de Southampton y la Universidad Autónoma de Madrid y se ha aplicado al análisis del funcionamiento en los cursos MOOC de la plataforma FutureLearn. El avance de la enseñanza en entornos masivos requiere, entre otras iniciativas, del conocimiento del desempeño del estudiante con respecto al diseño más o menos interactivo que ofrecen estos cursos. La visualización de métricas de aprendizaje y de la huella del estudiante en los cursos permite dinamizar y mejorar los entornos de cursos los MOOC. A través de un enfoque descriptivo-exploratorio se analiza el curso MOOC: “Digital Marketing: Challenges and Insights” ofrecido por la plataforma FutureLearn” y se presentan los resultados de la aplicación de métricas analíticas dinámicas en tiempo real al desempeño académico de los estudiantes.

Erreferentzia bibliografikoak

  • Adams Becker, S., Cummins, M., Davis, A., Freeman, A., Hall Giesinger, C., & Ananthanarayanan, V. (2017). NMC Horizon Report: 2017 Higher Education Edition. Austin, Texas: The New Media Consortium.
  • Aguaded, I., Vázquez-Cano, E., & López Meneses, E. (2016). “El impacto bibliométrico del movimiento MOOC en la Comunidad Científica Española” en Educación XX1, 19(2), pp. 77-104. doi: 10.5944/educXX1.19.2
  • Aguaded, I., Vázquez-Cano, E., & Sevillano, M.ª L. (2013). MOOCs, ¿turbocapitalismo de redes o altruismo educativo? Hacia un modelo más sostenible. Informe SCOPEO No. 2. MOOC: Estado de la situación actual, posibilidades, retos y futuro. Universidad de Salamanca.
  • Baker, R., & Siemens, G. (2013). Educational data mining and learning analytics. En Sawyer, K. (ed.), Cambridge Handbook of the Learning Sciences. Recuperado de http://www.columbia.edu/~rsb2162/BakerSiemensHandbook2013.pdf
  • Beheshti, B., Desmarais, M. C., & Naceur, R. (2012). Methods to find the number of latent skills. En Proceedings of the 5th International Conference on Educational Data Mining (pp. 81-86). ERIC.
  • Ben-Naim, D., Bain, M., & Marcus, N. (2009). A User-Driven and Data-Driven Approach for Supporting Teachers in Reflection and Adaptation of Adaptive Tutorials. En Romero et al. Proceedings of the Second International Conference on Educational Data Mining-EDM09 (pp. 21-30). Recuperado de http://www.educationaldatamining.org/EDM2009/uploads/proceedings/edm-proceedings-2009.pdf.
  • Brinton, C.G., Chiang, M., Jain, S., Lam, H., Liu, Z., & Wong, F.M.F. (2014). Learning about social learning in MOOCs: From statistical analysis to generative model en IEEE transactions on Learning Technologies, 7(4), pp. 346-359.
  • Clow, D. (2012). The learning analytics cycle: closing the loop effectively. En National Conference on Learning Analytics & Knowledge (pp. 134–137). Vancouver. Recuperado de http://oro.open.ac.uk/34330/1/LAK12-DougClow-personalcopy.pdf
  • Collins, E.D. (2013). SJSU plus augmented online learning environment: Pilot Project report. The Research & Planning Group for California Community Colleges. Sacramento, CA.
  • Dabbagh, N., & Fake, H. (2017). “College Students’ Perceptions of Personal Learning Environments Through the Lens of Digital Tools, Processes and Spaces” en Journal of New Approaches in Educational Research, 6(1), pp. 28-36. doi: http://dx.doi.org/10.7821/naer.2017.1.215
  • Daniel, J., Vázquez-Cano, E., & Gisbert, M. (2015). “The future of MOOCs: Adaptative Learning or Business Model?” en RUSC. Universities and Knowlwdge Society Journal, 12(1), pp. 64-73.
  • Domínguez, D., Álvarez, J. F., & Gil-Juarena, I. (2016). “Analítica del aprendizaje y Big Data: heurísticas y marcos interpretativos” en Dilemata, 22, pp. 87-103.
  • Drachsler, H., & Greller, W. (2016). Privacy and Learning Analytics-it’s a delicate issue. En Proceedings of the Sixth International Conference of Learning Analytics and Knowledge (LAK16’), Edinburgh, United Kingdom. ACM.
  • Ferguson, R., & Sharples, M. (2014). Innovative Pedagogy at Massive Scale: Teaching and Learning in MOOCs. En S. I. de Freitas, T. Ley, & P. J. Muñoz-Merino (Eds.), Open Learning and Teaching in Educational Communities (pp. 98–111). Springer International Publishing.
  • Garcia, L. (2015). Learning Analytics in On Line Educational Environments. Final Project. University Autónoma of Madrid.
  • Hervas Gomez, C., Real Plehan, S., López Mata, E., & Fernández Márquez, E. (2016). “Tecnofobia: competencias, actitudes y formación del alumnado del Grado en Educación Infantil” en IJERI: International Journal of Educational Research and Innovation, 0(6), pp. 83-94.
  • Johnson, M., Prescott, D., & Lyon, S. (2017). “Learning in Online Continuing Professional Development: An Institutionalist View on the Personal Learning Environment” en Journal of New Approaches in Educational Research, 6(1), pp. 20-27. doi: http://dx.doi.org/10.7821/naer.2017.1.189
  • Jovanovic, M., Vukicevic, M., Milovanovic, M., & Minovic, M. (2012). “Using data mining on student behavior and cognitive style data for improving e-learning systems: a case study” en International Journal of Computational Intelligence Systems, 5(3), pp. 597-610.
  • Khalil, M., & Ebner, M. (2016a). “De-Identification in Learning Analytics” en Journal of Learning Analytics, 3(1), pp. 129-138.
  • Khalil, M., & Ebner, M. (2016b). What Massive Open Online Course (MOOC) Stakeholders Can Learn from Learning Analytics? En J. M. Spector, B. B. Lockee, & D. M. Childress (Eds.), Learning, Design, and Technology: An International Compendium of Theory, Research, Practice, and Policy (pp. 1-30). Cham: Springer International Publishing.
  • Khalil, M., Kastl, C., & Ebner, M. (2016). Portraying MOOCs Learners: a Clustering Experience Using Learning Analytics. En Khalil, M., Ebner, M., Kopp, M., Lorenz, A. & Kalz. M. (Eds.), Proceedings of the European Stakeholder Summit on experiences and best practices in and around MOOCs (EMOOCs 2016) (pp. 265-278). BookOnDemand, Norderstedt.
  • Khribi, M. K., Jemni, M., & Nasraoui, O. (2008). Automatic Recommendations for e-Learning Personalization Based on Web Usage Mining Techniques and Information Retrieval. En Paloma Díaz, Kinshuk, Ignacio Aedo and Eduardo Mora, editores, Proceedings of the 8th IEEE International Conference on Advanced Learning Technologies- ICALT, (pp. 241-245), Santander.
  • Kizilcec, R. F., Piech, C., & Schneider, E. (2013). Deconstructing disengagement: analyzing learner subpopulations in massive open online courses. En Proceedings of the third international conference on Learning Analytics and knowledge (pp. 170-179). ACM.
  • Lan, A. S., Waters, A. E., Studer, C., & Baraniuk, R. G. (2014). “Sparse factor analysis for learning and content analytics” en Journal of Machine Learning Research, 15, pp. 1959-2008.
  • Laurillard, D. (2002) Rethinking University Teaching: A Conversational Framework for the Effective Use of Learning Technologies. London, Routledge Falmer.
  • López Meneses, E., Vázquez-Cano, E., & Román, P. (2015). “Analysis and implications of the impact of MOOC movement in the scientific community: JCR and Scopus (2010-2013)” en Comunicar, 44, pp. 73-80. doi: 10.3916/C44-2015-08.
  • Mariño, S., & Primorac, C. (2016). “Propuesta metodológica para desarrollo de modelos de redes neuronales artificiales supervisadas” en IJERI: International Journal of Educational Research and Innovation, 0(6), pp. 231-245.
  • Martos-Garcia, D., Usabiaga, O., & Valencia-Peris, A. (2017). “Students’ Perception on Formative and Shared Assessment: Connecting two Universities through the Blogosphere” en Journal of New Approaches in Educational Research, 6(1), pp. 64-70. doi: http://dx.doi.org/10.7821/naer.2017.1.194.
  • Mengual-Andrés, S. (2013). “Rethinking the role of Higher Education” en Journal of New Approaches in Educational Research, 2(1), pp. 1-2. doi:http://dx.doi.org/10.7821/naer.2.1.1-2.
  • Mengual-Andrés, S., Vázquez-Cano, E., & López Meneses, E. (2017). “La productividad científica sobre MOOC: aproximación bibliométrica 2012-2016 a través de Scopus” en RIED. Revista Iberoamericana de Educación a Distancia, 20(1) pp. 39-58. doi: http://dx.doi.org/10.5944/ried.20.1.16662
  • Moreno Martínez, N., Leiva Olivencia, J., & Matas Terrón, A. (2016). “Mobile learning, Gamificación y Realidad Aumentada para la enseñanza-aprendizaje de idiomas” en IJERI: International Journal of Educational Research and Innovation, 0(6), pp. 16-34.
  • Murphy, R., Gallagher, L., Krumm, A., Mislevy, J., & Hafter, A. (2014). Research on the Use of Khan Academy in Schools (Research Brief). Menlo Park, CA., SRI International. Recuperado de https://www.sri.com/sites/default/files/publications/2014-03-07_implementation_briefing.pdf
  • Papamitsiou, Z. K., & Economides, A. A. (2014). “Learning Analytics and Educational Data Mining in Practice: A Systematic Literature Review of Empirical Evidence” en Educational Technology & Society, 17(4), pp. 49-64.
  • Pérez Parras, J. (2016). “Nuevas tecnologías e influencia del ambiente dentro del proceso enseñanza-aprendizaje: Impacto de los cursos MOOC en educación” en IJERI: International Journal of Educational Research and Innovation, 0(6), pp. 176-186.
  • Reckase, M. (2009). Multidimensional item response theory. New York, NY, Springer.
  • Reich, J. (2015). “Rebooting MOOC Research. Improve assessment, data sharing, and experimental design” en Science, 347(6217), pp. 34-35.
  • Reich, J., Emanuel, J., Nesterko, S., Seaton, D.T., Mullaney, T., Waldo, J., Chuang, I., & Ho, A.D. (2014): HeroesX: The Ancient Greek Hero: Spring 2013 Course Report. HarvardX Working Paper Series, 3, 1-19. Recuperado de https://dash.harvard.edu/handle/1/11988100
  • Romero, C., & Ventura, S. (2006). Data Mining in e-Learning. Southampton, UK, WIT Press.
  • Romero, C., Ventura, S., & De Bra, P. (2004). “Knowledge Discovery with Genetic Programming for Providing Feedback to Courseware Authors” en User Modeling and User-Adapted Interaction, 4(5), pp. 425-464.
  • Schreurs, B., Teplovs, C., Ferguson, R., de Laat, M.F., & Buckingham-Shum, S. (2013). Visualizing Social Learning Ties by Type and Topic: Rationale and Concept Demonstrator. LAK13: 3th International Conference on Learning Analytics and Knowledge (30 April - 2 May), Leuven, Belgium.
  • Sclater, N. (2014). Code of practice for Learning Analytics: A literature review of the ethical and legal issues. Recuperado de http://repository.jisc.ac.uk/5661/1/Learning_Analytics_A-Literature_Review.pdf.
  • Sharples, M., de Roock , R., Ferguson, R., Gaved, M., Herodotou, C., Koh, E., Kukulska-Hulme, A., Looi, C-K, McAndrew, P., Rienties, B., Weller, M., & Wong, L. H. (2016). Innovating Pedagogy 2016: Open University Innovation Report 5. Milton Keynes: The Open University.
  • Siemens, G. (2012). Learning analytics: envisioning a research discipline and a domain of practice. En Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 4-8). ACM.
  • Su, J-M., Tseng, S-S., Wang, W., & Weng, J-F. (2006). “Learning Portfolio Analysis and Mining for SCORM Compliant Environment” en Educational Technology and Society, 9(1), pp. 262-275.
  • Talavera, L., & Gaudioso, E. (2004). Mining Student Data to Characterize Similar Behavior Groups in Unstructured Collaboration Spaces. En Proceedings of theWorkshop on Artificial Intelligence Methods in Computer-Supported Collaborative Learning (CSCL) held in conjunction with the 16th European Conference on Artficial Intelligence-ECAI, 17-23, Valencia, Spain. Recuperado de http://www.ia.uned.es/~elena/ecai04-ws/papers/TalaveraGaudiosoECAI04ws.pdf.
  • Ueno. M. (2006). Online outlier detection of learners' irregular learning processes. En Romero & Ventura (2006). Data Mining in e-Learning (pp. 261-277). Southampton, UK: WIT Press.
  • Vázquez-Cano, E. (2013). “The Videoarticle: New Reporting Format in Scientific Journals and its Integration in MOOCs” en Comunicar, 41, pp. 83-91. http://dx.doi.org/10.3916/C41-2013-08
  • Vázquez-Cano, E., & López Meneses, E. (2014). “Los MOOC en la Educación Superior: La expansión del conocimiento” en Profesorado, 18(1), pp. 1-12.
  • Vázquez-Cano, E., & López Meneses, E. (2015). “La filosofía pedagógica de los MOOC y la educación universitaria” en Revista Iberoamericana de Educación a Distancia (RIED), 18(2) pp. 25-37.
  • Vázquez-Cano, E., & Sevillano, M.ª L. (2015). “Analysis of risks in a Learning Management System: A case study in the Spanish National University of Distance Education (UNED)” en Journal of New Approaches in Educational Research, 4(1), pp. 62-68.
  • Vázquez-Cano, E., López Meneses, E., & Sevillano García, M.ªL. (2017). “La repercusión del movimiento MOOC en las redes sociales. Un estudio computacional y estadístico en Twitter” en Revista Española de Pedagogía, 75(266), pp. 47-64. doi: 10.22550/REP75-1-2017-03
  • Verbert, K., Duval, E., Klerkx, J., Govaerts, S., & Santos, J. L. (2013). “Learning analytics dashboard applications” en American Behavioral Scientist, 57(10) pp. 1500-1509.
  • Verbert, K., Govaerts, S., Duval, E., Santos, J. L., Van Assche, F., Parra, G., & Klerkx, J. (2014). “Learning dashboards: an overview and future research opportunities” en Personal and Ubiquitous Computing, 18(6), pp. 1499-1514.
  • Weston, C. (2012). Learning analytics for better learning content. Ed Tech Now Blog. Recuperado de http://edtechnow.net/2012/03/31/learning-analytics-for-better-learning-content/
  • Wilkowski, J., Deutsch, A., & Russell, D. M. (2014). Student skin and goal achievement in the mapping with Google MOOC. En Proceedings of the first ACM Conference on Learning@Scale, (pp. 3-10), New York: ACM.
  • Zaïane, O. (2006). Recommender System for e-Learning: Towards Non-InstructiveWeb Mining. En Romero & Ventura (2006). Data Mining in e-Learning (pp. 79-96). Southampton, UK, WIT Press.
  • Zapata-Ros, M. (2013). “Analítica de aprendizaje y personalización” en Campus Virtuales, 2(2), pp. 88-118.