• ELENA B. DURÁN National University of Santiago del Estero, Argentina
  • ANALÍA AMANDI UNICEN University, CONICET, Argentina


Web Usage Mining, association rules, collaborative learning, student model, collaborative profile


An effective collaboration in learning environments involves a set of skills that students must learn and cultivate. Detecting the contexts in which students apply these skills facilitates personalized assistance in learning environments during the learning process. This work introduces a method to detect collaborative behavior patterns automatically. It is based on Web Usage Mining techniques and allows us to identify contexts in which collaborative skills are applied. The patterns are discovered using association rules and then are used to update a Collaborative Profile in a Collaborative and Dynamic Student Model. The method was validated with simulation techniques and the results obtained suggest that Web Usage Mining is an effective method for detecting collaborative profiles in distance learning environments.



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Becker, K., Marquardt, C.G. and Ruiz, D.D. 2004. A Pre-Processing Tool for Web Usage Mining

in the Distance Education Domain, Proc. of the International Database Engineering and

Applications Symposium (IDEAS'04). 00, 78-87.

Belbin, R. 1981. Management Teams. John Wiley (ed.). New York.

Bransford, J., Brown A., and Cocking R. 1999. How People Learn: Brain, Mind, Experience, and

School. National Academy Press.

Burton, M., Brna P. Pilkington R. 2000. Clarissa: A Laboratory for the Modelling of

Collaboration. International Journal of Artificial Intelligence in Education, 11, 79-105.

Castelfranchi, C., de Rossi, F. and Falcone, R. 1997. Social Attitudes and Ground Social

Interaction. In Association for the Advancement of Artificial Intelligence (AAAI) Fall Symposium

Series. Socially Intelligent Agents, Massachusetts, November 1997. AAAI Press.

Convertino, G., Neale, D.C., Hobby, L., Carroll, J. M. and Rosson, M. B. 2004. A Laboratory

Model for Studying Activity Awareness. In Proceedings of NordiCHI 2004. Tampere, Finlandia.

ACM Press.

Cristofor, L. 2002. ARtool v1.1.2. [Computer Software], Association Rule Mining Algorithms and


Dillenbourg, P. and Tchounikine, P. 2007. Flexibility in macro-scripts for computer-supported

collaborative learning. Journal of Computer Assisted Learning, 23(1), 1-13.

Dillenbourg, P. 1999. What do you mean by “collaborative learning”?. In P. Dillenbourg (ed.)

Collaborative-learning: Cognitive and Computational Approaches. Elsevier: 1-19.

Dillenbourg, P., Baker M., Blaye A., and O’Malley C. (1996). The evolution of research on

collaborative learning. In E. Spada and P. Reiman (eds.) Learning in Humans and Machine:

Towards an interdisciplinary learning science. Elsevier. 189-211.

Felder, R. M. and Silverman, L. K. 1988. Learning and teaching styles in engineering education.

Journal of Engineering Education, 78(7), 674-681.

Freyberger, J., Hefferman, N.T. and Ruiz, C. 2004. Using Associations Rules to Guide a Search

for Best Fitting Transfer Models of Student Learning, in International Conf. on Intelligent

Tutoring Systems.

Gouli, E., Gogoulou, A., Grigoriadou, M. and Samarakou, M. 2003. Towards the Development of

an Adaptive Communication Tool Promoting Cognitive and Communications Skills. In

Proceedings of the 11th International Conference PEG 2003: Powerful ICT Tools for Teaching

and Learning. June-July, 2003, St. Petersburgo, Rusia.

Han, J. and Kamber, M. 2001. Data Mining. Concepts and Techniques. Morgan Kaufman

Publishers, San Francisco, USA.

Hoppe, H.U., Joiner, R., Milrad, M. and Sharples, M. 2003. Guest editorial: Wireless and Mobile

Technologies in Education. Journal of Computer Assisted Learning,19(3), 255-259, September

Jehn, K. A. and Mannix, E. A. 2001. The dynamic nature of conflict: a longitudinal study of

intragroup conflict and group performance. Academy of Management Journal 44(2), 238-252.

Kay, J., Maisonneuve, N., Yacef, K., and Zaïane, O. 2006. Mining patterns of events in students´

teamwork data, in Proc. of the Workshop on Educational Data Mining at the 8th International

Conference on Intelligent Tutoring Systems, Taiwan, 45-52.

Kristofic, A. and Bieliková, M. 2005. Improving Adaptation in Web-based Educational

Hypermedia by means of Knowledge Discovery, in Sixteenth ACM Conference on Hypertext and

Hypermedia (HT’2005), September 2005, Salzburgo, Austria, 184-192.

Mavrikis M. 2008. Data-driven modelling of students’ interactions in an ILE, in First International

Conference on Educational Data Mining (EDM’08), Canada.

Mobasher, B. 2004. Web Usage Mining and Personalization. In Practical Handbook of Internet

Computing. Munindar P. Singh (ed.), CRC Press.

Mobasher, B., Cooley, R. and Srivastava, J. 2000. Automatic Personalization Based on Web

Usage Mining, Communications of the ACM, 43(8), 142-151.

Mor, E. and Minguillón, J. 2004. E-Learning Personalization based on Itineraries and Long-term

Navigational Behavior Can Museum Exhibits Support Personalized Learning in Collaborative

Classroom Activities By Using Argumented, in World Wide Web Conference, New York, 264-

Nasraoui, O., Krishnapuram, R., Frigui, H. and Joshi, A. 2000. Extracting Web User Profiles

Using Relational Competitive Fuzzy Clustering. Artificial Intelligence Tools, 9(4), 509-526.

Ng Cheong Vee, M-H, Meyer, B. and Mannock, K.L. 2006. Understanding novice errors paths in

Object-Oriented programming through log analysis, in 8th International Conference on Intelligent

Tutoring Systems, Jhongli, Taiwan, June 2006.

Paliouras, G., Papatheodorou, C., Karkaletsis, V. and Spyropoulos, C.D. 2000. Clustering the

Users of Large Web Sites Into Commmunities, in Proceedings of 17th International Conference on

Machine Learning (ICML), Morgan Kaukmann, San Francisco, CA, 719-726.

Prinz, W., Mark, G. and Pankoke-Babatz, P. 1998. Designing Groupware for Congruency in Use,

in Proc. of Conf. on Computer Supported Cooperative Work (CSCW’98), ACM Press, 373-382.

Puntambekar, S. 2005. Tools for Scaffolding Students in a Complex Learning Environment: What

Have We Gained and What Have We Missed?. Educational Psychologist, 40(1), 1-12.

Ramadhan, H.A., Fiaidhi, J.A. and Ali, J.M.H. 2005. The Aplication of Web-Mining to Theme-

Based Recommender Systems, in International Journal of Instructional Technology and Distance

Learning. May 2005, 2(5), 9-20.

Retalis, S., Papasalouros, A., Psaromiligkos, Y., Siscos, S. and Kargidis. T. 2006. Towards

Networked Learning Analytics – A concept and a tool, Networked Learning 2006.

Romero, C., Ventura S., Espejo P., and Hervas C. 2008. Data Mining Algorithms to Classify

Students, in First International Conference on Educational Data Mining (EDM’08), Canada.

Romero, C. and Ventura, S. 2007. Educational data mining: A survey from 1995 to 2005. Expert

Systems with Applications, 33(1), 135-146.

Romero, C., Ventura, S., and Bra, P. de. 2005. Knowledge Discovery with Genetic Programming

for Providing Feedback to Courseware Author. User Modeling and User Adapted Interaction,


Soller, A. 2001. Supporting Social Interaction in an Intelligent Collaborative Learning System.

International Journal of Artificial Intelligence in Education, 12, 40-62.

Stahl, G. 2002. Contributions to a theoretical framework for CSCL, in Proceedings of Computer

Supported Collaborative Learning (CSCL 2002). Boulder, CO.

Stahl, G., Koschmann T., and Suthers D. 2006. Computer-supported collaborative learning: An

historical perspective. Based on a chapter in: R. K. Sawyer (ed.). Cambridge Handbook of the

Learning Sciences. Cambridge, UK: Cambridge University Press.

Talavera, L. and Gaudioso, E. 2004. Mining Student Data to Characterize Similar Behavior

Groups in Unstructured Collaboration Spaces, in Workshop on Artificial Intelligence in CSCL.

th European Conference on Artificial Intelligence (ECAI’04), 17-23.

Twidale, M., Randal, D. and Bentley, R. 1994. Situated Evaluation for Cooperative Systems. In

Proceedings of Conference on Computer Supported Cooperative Work (CSCW’94), ACM Press:


Vonderwell S. and Zachariah S.. Factors that Influence Participation in Online Learning. Journal

of Research on Technology in Education. ISTE, 38 (2), 213-230.

Weibelzahl, S. and Weber, G. 2003. Evaluating the Inference Mechanism of Adaptive Learning

Systems. In Brusilovsky, P., Corbett, A. and de Rosis, F. (Eds.) User Modeling: Proc. of the Ninth

International Conf.. Lecture Notes in Computer Science, 154-168, Berlin: Springer.

Zaïane, O. 2001. Web Usage Mining for a Better Web-Based Learning Environment. In

Proceedings of Conference on Advanced Technology for Education, 60-64, Banff, Alberta.