Aplicación de métodos de diseño centrado en el usuario y minería de datos para definir recomendaciones que promuevan el uso del foro en una experiencia virtual de aprendizaje

  1. Valdiviezo, Priscila M.
  2. Santos, Olga C.
  3. González Boticario, Jesús
Revista:
RIED: revista iberoamericana de educación a distancia

ISSN: 1138-2783

Any de publicació: 2010

Títol de l'exemplar: Adaptación y accesibilidad de las tecnologías para el aprendizaje

Volum: 13

Número: 2

Pàgines: 237-264

Tipus: Article

Altres publicacions en: RIED: revista iberoamericana de educación a distancia

Resum

Recommender systems in learning virtual environments are increasingly becoming a feasible approach to provide the adaptive support required to attend students� learning needs. With interaction data obtained from these virtual environments, it is possible to find indicators where data mining and machine learning techniques can be applied to identify relevant information that allows for the definition of recommenders. In this research we have applied unsupervised learning techniques to identify common interaction patterns with available forums in the OpenACS/dotLRN platform course. In this way, it will allow to define recommendations which help to improve the learning experience of students.

Referències bibliogràfiques

  • Adomavicius, G.; Tuzhilin, A. (2005). Toward the Next Generation of Recommender Systems: A Survey of the State-of-the- Art and Possible Extensions. IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 6, June.
  • Anaya, A.; Boticario, J. G. (2010). Application of machine learning techniques to analyse student interactions and improve the collaboration process. Expert Systems with Applications: Special Issue on Computer Supported Cooperative Work in Design. (In Press).
  • Boticario, J. G.; Anaya, A. (2009). Clustering Learners according to their Collaboration. In Proceedings of the 13th international conference on computer supported cooperative work in design (CSCWD 2009), IEEE Computer Society Press.
  • Couchet, J. ; Santos O.C. ; Raffenne E. ; Boticario J. G. (2008). The Tracking and Auditing Module for the OpenACS Framework, 7th OPENACS / .LRN Conference, Valencia, España. November 18-19.
  • Drachsler, H.; Hummel, H.; Koper, R. (2008) Personal recommender systems for learners in lifelong learning networks: the requirements, techniques and model. International Journal of Learning Technology (IJLT), Vol. 3, No. 4.
  • García, C.; Gómez, I. (2009). Algoritmos de aprendizaje: KNN & KMEANS. [en línea] Disponible en: http://www.it.uc3m.es/ jvillena/irc/practicas/08-09/06.pdf [consulta 2010, 10 de febrero]
  • García, E.; Romero, C.; Ventura, S.; Castro, C. (2009). An architecture for making recommendations to courseware authors using association rule mining and collaborative filtering. User Modeling and User-Adapted Interaction, Volume 19, 99–132.
  • Mejía, C., Mancera, L., Gómez, S., Baldiris, S., Fabregat, R. (2008). Supporting Competence upon DotLRN through Personalization. 7th OpenACS / .LRN conference. Valencia, España. 18-19 November.
  • Perera, D.; Kay, J.; Yacef, K.; Koprinska, I. (2007). Mining learners’ traces from an online collaboration toolClustering. Proceedings of the 13th International Conference of Artificial Intelligence in Education. Marina del Rey, CA. USA. July.
  • Quincey, E.; Kostkova, P.; Farrell, D. (2009). Visualising web server logs for a Web 1.0 audience using Web 2.0 technologies: eliciting attributes for recommendation and profiling systems. In the Proceedings of the Workshop on Adaptation and Personalization for Web 2.0 in connection with UMAP, June 22- 26.
  • Sanjog, R. ; Mahanti, A. (2008). Filler Items Strategies for E®ective Shilling Attacks. In Workshop on Recommender Systems, Patras, Greece. (ECAI).
  • Santos, O. C., Boticario, J. G. (2004). Supporting a collaborative task in a web-based learning environment with Artificial Intelligence and User Modelling techniques. 6º Simposio Internacional de Informática Educativa (SIIE’04). November 16-18. Caceres, España.
  • Santos, O. C.; Boticario, J. G.; Raffenne, E.; Pastor, R. (2007). Why using dotLRN? UNED use cases. Proceedings of the FLOSS (Free/Libre/Open Source Systems) International Conference, 195- 212.
  • Santos, O.C.; Boticario, J. G. (2008). Users’ experience with a recommender system in an open source standard- based learning management system. In proceedings of the 4yh Symposium on Usability and HCI for Education and Work (USAB)(in press).
  • Santos, O.C., Martin, L., Del Campo, E., Saneiro, M., Mazzone, E., Boticario, J.G., Petrie, H. (2009). User-centered design methods for validating a recommendations model to enrich learning management systems with adaptive navigation support. [en línea] Disponible en: http://www.easy-hub. org/workshops/umap2009/doc/paper7. pdf [consulta 2009, 15 de Septiembre].
  • Santos, O.C.; Rodríguez, A.; Gaudioso, E.; Boticario, J. G. (2003). Helping the tutor to manage a collaborative task in a web-based learning environment. Artificial intelligence in education (AIED): Supplementary Proceedings. Universidad de Sidney, Volume 4, Australia, 153-162.
  • Schank, R. C.; Cleary, C. (1995). Engines for Education. Lawrence Erlbaum. [en línea] Disponible en: http://www.engines4ed.org/hyperbook/ [consulta 2009, 15 de Septiembre].
  • Sierra, B. (2006). Aprendizaje Automático: Conceptos básicos y avanzados. Madrid: Pearson Prentice Hall.
  • Talavera, L.; Gaudioso, E. (2004). Mining Student Data to Characterize Similar Behavior Groups In Unstructured Collaboration Spaces. In Proceedings of the workshop on artificial intelligence in CSCL. 16th European conference on artificial intelligence, ECAI (17-23). Valencia, Spain.
  • Vialardi, C.; Bravo, J.; Shafti, L.; Ortigosa, A. (2009). Recommendation in Higher Education Using Data Mining Techniques. In Educational Data Mining (EDM).
  • Witten, I. H.; Frank, E. (2005). Data Mining: Practical machine learning tools and techniques. 2nd Edition. Morgan Kaufmann.
  • Yu, Z.; et al. (2007). Ontology-Based Semantic Recommendation for ContextAware E-Learning. In Proceedings of the 4th Conference on Ubiquitous Intelligence and Computing, v.4611, Berlin, Heidelberg: Springer, 898-907.