Visualitzacions d’anàlisi temporal de l’aprenentatge per augmentar el coneixement durant l’avaluació

Zacharoula Papamitsiou, Anastasios A. Economides

Resum


Les representacions visuals de dades de traces generades per l’alumnat durant les activitats d’aprenentatge ajuden tant els estudiants com els professors a interpretar-les intuïtivament i a percebre’n amb rapidesa aspectes amagats. En aquest treball descrivim la visualització de dades de traces temporals durant el procés d’avaluació. L’estudi tenia un doble objectiu: a) descriure la implicació dels estudiants en el procés d’avaluació pel que fa a temps esmerçat i factors temporals associats amb característiques concretes de l’aprenentatge, i b) explorar els factors que influeixen en la intenció comportamental del professorat quant a emprar el sistema proposat com a sistema d’informació i les seves percepcions de l’efectivitat i l’acceptació del nostre enfocament. Les visualitzacions proposades s’han examinat en un estudi amb 32 professors d’ensenyament secundari. Vàrem adoptar una metodologia de recerca basada en el disseny i vàrem utilitzar un instrument d’enquesta –basada en el model d’acceptació de l’anàlisi de l’aprenentatge– per mesurar l’impacte esperat de les visualitzacions proposades. L’anàlisi de les troballes indica que a) els factors temporals es poden utilitzar per visualitzar el comportament dels estudiants durant l’avaluació, i b) la visualització de la dimensió temporal del comportament dels estudiants augmenta el coneixement del professor pel que fa al progrés dels alumnes, a possibles conceptes erronis (per exemple, endevinar la resposta correcta) i a les dificultats de la tasca.

Paraules clau


anàlisi temporal de l’aprenentatge, visualitzacions, coneixement, monitoratge, avaluació, acceptació

Referències


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

Enllaços refback

  • No hi ha cap enllaç refback.




Universitat Oberta de Catalunya. eLearn Center 

RUSC. Universities and Knowledge Society Journal és una publicació electrònica editada per la Universitat Oberta de Catalunya (Barcelona).

Creative Commons
Els textos publicats en aquesta revista estan subjectes –llevat que s'indiqui el contrari– a una llicència de Reconeixement 3.0 Espanya de Creative Commons. Podeu copiar-los, distribuir-los, comunicar-los públicament i fer-ne obres derivades sempre que reconegueu els crèdits de les obres (autoria, nom de la revista, institució editora) de la manera especificada pels autors o per la revista. La llicència completa es pot consultar a http://creativecommons.org/licenses/by/3.0/es/deed.ca.