Visualizaciones del análisis temporal del aprendizaje para aumentar el conocimiento durante la evaluación

Zacharoula Papamitsiou, Anastasios A. Economides

Resumen


Las representaciones visuales de datos de trazas generados por el alumnado durante las actividades de aprendizaje ayudan tanto a los estudiantes como a los profesores a interpretarlos intuitivamente y a percibir con rapidez aspectos ocultos. En este trabajo, describimos la visualización de datos de trazas temporales durante la evaluación. El estudio tenía un doble objetivo: a) describir la implicación de los estudiantes en el proceso de evaluación en cuanto a tiempo invertido y factores temporales asociados con características concretas del aprendizaje, y b) explorar los factores que influyen en la intención comportamental del profesorado en cuanto a emplear el sistema propuesto como sistema de información y sus percepciones de la efectividad y la aceptación de nuestro enfoque. Las visualizaciones propuestas se han examinado en un estudio con 32 profesores de educación secundaria. Adoptamos una metodología de investigación basada en el diseño y utilizamos un instrumento de encuesta –basada en el modelo de aceptación del análisis del aprendizaje– para medir el impacto esperado de las visualizaciones propuestas. El análisis de los hallazgos indica que a) los factores temporales se pueden utilizar para visualizar el comportamiento de los estudiantes durante la evaluación, y b) la visualización de la dimensión temporal del comportamiento de los estudiantes aumenta el conocimiento del profesor respecto al progreso de los alumnos, posibles conceptos erróneos (por ejemplo, adivinar la respuesta correcta) y dificultad de la tarea. 

Palabras clave


análisis temporal del aprendizaje, visualizaciones, conocimiento, monitorización, evaluación, aceptación

Referencias


Ajzen, I. (2002). Perceived Behavioral Control, Self-Efficacy, Locus of Control, and the Theory of Planned Behavior. Journal of Applied Social Psychology, 32, 665-683.

Ali, L., Hatala, M., Gašević, D., & Jovanović, J. (2012). A qualitative evaluation of evolution of a learning analytics tool. Computers & Education, 58(1), 470–489.

Anderson, T., & Shattuck, J. (2012). Design-based research: A decade of progress in education research? Educational Researcher, 41(1), 16-25.

Barclay, D., Higgins, C., & Thompson, R. (1995). The Partial Least Squares approach to causal modelling: Personal computer adoption and use as an illustration. Technology Studies, 2(1), 285–309.

Chin, W. W. (1998). The partial least squares approach to structural equation Modeling. In G. A. Marcoulides (Ed.), Modern Business research Methods (pp. 295–336). Mahwah, NJ: Lawrence Erlbaum Associates.

Cohen, J. (1988). Statistical Power analysis for the Behavioural Sciences (2nd ed.). Hillsdale, NJ: Erlbaum.

Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: development of a measure and Initial test. MIS Quarterly, 19(2), 189–211.

Cortina, J. M. 1993. What is Coefficient Alpha? An Examination of Theory and Applications. Journal of Applied Psychology, 78(1), 98-107.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13, 319–340.

Duval, E. (2011). Attention please!: learning analytics for visualization and recommendation. In Proceedings of the 1st International Conference on Learning Analytics and Knowledge (LAK '11). ACM, New York, USA, 9-17. DOI=10.1145/2090116.2090118

Economides, A. A. (2005). Adaptive orientation methods in computer adaptive testing. In Griff Richards (ed.) Proceedings E-Learn 2005 World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, Vancouver, Canada, 1290-1295.

Fornell, C. & Larcker, D.F. (1981). Evaluating structural equation models with unobservable variables and measurement error, Journal of Marketing Research, 18(1), 39-50.

France, L., Heraud, J.-M., Marty, J.-C., Carron, T., & Heili, J. 2006. Monitoring Virtual Classroom: Visualization Techniques to Observe Student Activities in an e-Learning System. Proceedings of the Sixth International Conference on Advanced Learning Technologies, 716-720.

Govaerts, S., Verbert, K. and Duval, E. (2011). Evaluating the student activity meter: two case studies. In Proceedings of the 10th international conference on Advances in Web-Based Learning (ICWL'11), Leung, H., Popescu, E., Cao, Y., Lau, R. H., & Wolfgang Nejdl (Eds.). Springer-Verlag, Berlin, Heidelberg, 188-197. DOI=10.1007/978-3-642-25813-8_20

Govaerts, S., Verbert, K., Duval, E., & Pardo, A. (2012). The Student Activity Meter for Awareness and Self-reflection. Proceedings of the 2012 ACM annual conference on Human Factors in Computing Systems Extended Abstracts (pp. 869– 884), ACM.

Kerly, A., Ellis, R. & Bull, S. (2007). CALMsystem: A Conversational Agent for Learner Modelling, in R. Ellis, T. Allen & M. Petridis (eds), Applications and Innovations in Intelligent Systems XV – Proceedings of AI-2007, 27th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence (pp. 89-102). Springer Verlag.

Lee, Y. C. (2008). The role of perceived resources in online learning adoption. Computers & Education, 50(4), 1423–1438.

Leony, D., Pardo, A., de la Fuente Valentín, L., de Castro, D. S., and Kloos, C. D. (2012). GLASS: a learning analytics visualization tool. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (LAK '12), Buckingham Shum, S., Gasevic, D. & Ferguson, R. (Eds.). ACM, New York, USA, 162-163. DOI=10.1145/2330601.2330642

Mazza, R. & Milani, C. (2005). Exploring Usage Analysis in Learning Systems: Gaining Insights from Visualisations. In Proceedings of the International Conference on Artificial Intelligence in Education (AIED 2005), Amsterdam.

Mazza, R. & Dimitrova, V. (2007). CourseVis: A graphical student monitoring tool for supporting instructors in web-based distance courses. International Journal of Human-Computer Studies. 65(2), 125-139. DOI=10.1016/j.ijhcs.2006.08.008

Merceron, A. & K. Yacef (2005). TADA-Ed for Educational Data Mining. Interactive Multimedia Electronic Journal of Computer-Enhanced Learning, 7(1).

Padilla-Melendez, A., Garrido-Moreno, A., & Del Aguila-Obra, A. R. (2008). Factors affecting e-collaboration technology use among management students. Computers & Education, 51(2), 609-623.

Papamitsiou, Z., & Economides, A. A. (2014).Students’ perception of performance vs. actual performance during computer-based testing: a temporal approach, In 8th International Technology, Education and Development Conference, Valencia, Spain, 401-411.

Papamitsiou, Z., Terzis, V. & Economides, A. A. (2014). Temporal Learning Analytics during computer based testing, In Proceedings of the 4th International Conference on Learning Analytics and Knowledge (LAK’14), ACM, New York, USA, 31-35. DOI=10.1145/2567574.2567609 http://doi.acm.org/10.1145/2567574.2567609

Rivera-Pelayo, V., Munk, J., Zacharias, V., & Braun, S. (2013). Live interest meter: learning from quantified feedback in mass lectures. In Proceedings of the Third International Conference on Learning Analytics and Knowledge (LAK '13), Suthers, D., Verbert, K., Duval, E. & Ochoa, X. (Eds.). ACM, New York, USA, 23-27. DOI=10.1145/2460296.2460302

Santos, J. L., Verbert, K., Govaerts, S. and Duval, E. (2013). Addressing learner issues with StepUp!: an evaluation. In Proceedings of the Third International Conference on Learning Analytics and Knowledge (LAK '13), Suthers, D., Verbert, K., Duval, E. & Ochoa, X. (Eds.). ACM, New York, USA, 14-22. DOI=10.1145/2460296.2460301

Soller, A., Mart´ınez-Mon´es, A., Jermann, P., & Muehlenbrock, M. (2005). From mirroring to guiding: A review of state of the art technology for supporting collaborative learning. International Journal of Artificial Intelligence in Education. 15(4), 261–290.

Thomas, J. & Cook, C. A. (Ed.) (2005). Illuminating the Path: The R&D Agenda for Visual Analytics National Visualization and Analytics Center.

Van Raaij, E. M., & Schepers, J. J. L. (2008). The acceptance and use of a virtual learning environment in China. Computers & Education, 50(3), 838–852.

Venkatesh, V. (1999). Creation of favorable user perceptions: Exploring the role of intrinsic motivation. MIS Quarterly, 23, 239–260.

Wang, F. & Hannafin, M. (2005). Design-based research and technology-enhanced learning environments. Educational Technology Research and Development, 53, 5-23. 10.1007/BF02504682.

Wetzels, M., Odekerken-Schröder, G., & Van Oppen, C. (2009). Using PLS path modelling for assessing hierarchical construct models: Guidelines and empirical illustration. MIS Quarterly, 33(1), 177–195.

Wolpers, M., Najjar, J., Verbert, K. & Duval, E. (2007). Tracking actual usage: the attention metadata approach. Educational Technology and Society, 10(3), 106-121.

Yi, M. Y., & Hwang, Y. (2003). Predicting the use of web-based information systems: self-efficacy, enjoyment, learning goal orientation, and the technology adoption model. International Journal of Human Computer Studies, 59(4), 431–449.

Zimmerman, B.J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41 (2), 64-70.

Zimmerman, B. J., & Moylan, A. R. (2009). Self-regulation: Where metacognition and motivation intersect. In D. J. Hacker, J. Dunlosky & A. C. Graesser (Eds.), Handbook of metacognition in education (pp. 299-316). New York: Routledge.

Zinn, C., and Scheuer, O. 2007. How did the e-learning session go? The Student Inspector. In In Luckin, R., Koedinger, K.R., & Greer, J. (Eds.) Proceeding of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work (pp. 487-494). IOS Press: Amsterdam, The Netherlands.




DOI: http://dx.doi.org/10.7238/rusc.v12i3.2519

Enlaces refback

  • No hay ningún enlace refback.




Universitat Oberta de Catalunya. eLearn Center 

RUSC. Universities and Knowledge Society Journal es una revista científica editada por la Universitat Oberta de Catalunya (Barcelona).

Creative Commons
Los textos publicados en esta revista están sujetos –si no se indica lo contrario– a una licencia de Reconocimiento 3.0 España de Creative Commons. Puede copiarlos, distribuirlos, comunicarlos públicamente, hacer obras derivadas y usos comerciales siempre que reconozca los créditos de las obras (autoría, nombre de la revista, institución editora) de la manera especificada por los autores o por la revista. La licencia completa se puede consultar en http://creativecommons.org/licenses/by/3.0/es/deed.es.