Content-free collaborative learning modeling using data mining

  • Antonio R. Anaya
  • Jesús G. Boticario
Original Paper


Modeling user behavior (user modeling) via data mining faces a critical unresolved issue: how to build a collaboration model based on frequent analysis of students in order to ascertain whether collaboration has taken place. Numerous human-based and knowledge-based solutions to this problem have been proposed, but they are time-consuming or domain-dependent. The diversity of these solutions and their lack of common characteristics are an indication of how unresolved this issue remains. Bearing this in mind, our research has made progress on several fronts. First, we have found supportive evidence, based on a collaborative learning experience with hundreds of students over three consecutive years, that an approach using domain independent learning that is transferable to current e-learning platforms helps both students and teachers to manage student collaboration better. Second, the approach draws on a domain-independent modeling method of collaborative learning based on data mining that helps clarify which user-modeling issues are to be considered. We propose two data mining methods that were found to be useful for evaluating student collaboration, and discuss their respective advantages and disadvantages. Three data sources to generate and evaluate the collaboration model were identified. Third, the features being modeled were made accessible to students in several meta-cognitive tools. Their usage of these tools showed that the best approach to encourage student collaboration is to show only the most relevant inferred information, simply displayed. Moreover, these tools also provide teachers with valuable modeling information to improve their management of the collaboration. Fourth, an ontology, domain independent features and a process that can be applied to current e-learning platforms make the approach transferable and reusable. Fifth, several open research issues of particular interest were identified. We intend to address these open issues through research in the near future.


Collaborative learning Collaboration modeling Data mining Open models Collaboration evaluation Meta-cognitive tools in collaborative learning 


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  1. Anaya, A.R., Boticario, J.G.: Clustering learners according to their collaboration. 13th International Conference on Computer Supported Cooperative Work in Design (CSCWD 2009), April 22–24, 2009, Santiago, Chile, pp. 540–545 (2009)Google Scholar
  2. Anaya, A.R., Boticario, J.G.: Ranking learner collaboration according to their interactions. The 1st Annual Engineering Education Conference (EDUCON 2010), Madrid, Spain, IEEE Computer Society Press, 2010, pp. 797–803 (2009)Google Scholar
  3. Anaya A.R., Boticario J.G.: Application of machine learning techniques to analyze student interactions and improve the collaboration process. Expert Syst. Appl. Intell. Collab. Des. 38(2), 1171–1181 (2011)CrossRefGoogle Scholar
  4. Baghaei, N., Mitrovic, A.: From modelling domain knowledge to metacognitive skills: extending a constraint-based tutoring system to support collaboration. In: Conati, C., McCoy, K., Paliouras, G. (eds.) UM ‘07 Proceedings of the 11th International Conference on User Modeling, pp. 217–227, (2007)Google Scholar
  5. Baker, R.S.J.d.: Mining data for student models. In: Nkmabou, R., Mizoguchi, R., Bourdeau, J. (eds.) Advances in Intelligent Tutoring Systems, Studies in Computational Intelligence, vol. 308/2010, pp. 323–337 (2010)Google Scholar
  6. Baker, R.S., Corbett, A.T., Koedinger, K.R., Wagner, A.Z.: Off-task behavior in the cognitive tutor classroom: when students “Game the System”. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, April 24–29, 2004, Vienna, Austria (2004)Google Scholar
  7. Baldiris S., Santos O.C., Barrera C., Boticario J.G., Velez J., Fabregat R.: Integration of educational specifications and standards to support adaptive learning scenarios in ADAPTAPlan. Int. J. Comput. Appl. 5(1), 88–107 (2008)Google Scholar
  8. Barkley E., Cross K.P, Major C.H.: Collaborative Learning Techniques: A Practical Guide to Promoting Learning in Groups. Jossey Bass, San Francisco, CA (2004)Google Scholar
  9. Barros, B., Verdejo, M.F., Read, T., Mizoguchi, R.: Applications of a collaborative learning ontology. MICAI 2002: Advances in Artificial Intelligence, Lecture Notes in Computer Science, 2002, vol. 2313/2002, pp. 103–118, doi: 10.1007/3-540-46016-0_32 (2002)
  10. Boticario, J.G., Gaudioso, E.: Towards a personalized Web-based educational system. Mexican International Conference on Artificial Intelligence 2000. Springer Verlang, Acapulco, Mexico, pp. 729–740 (2000)Google Scholar
  11. Bratitsis, T., Dimitracopoulou, A.: Indicators for measuring quality in asynchronous discussion forae. The 12th International Workshop on Groupware, CRIWG 2006. Springer Verlag, Spain, pp. 54–61 (2006)Google Scholar
  12. Bratitsis, T., Dimitracopoulou, A., Martínez-Monés, A., Marcos-García, J.A., Dimitriadis, Y.: Supporting members of a learning community using interaction analysis tools: the example of the Kaleidoscope NoE scientific network. Proceedings of the IEEE International Conference on Advanced Learning Technologies, ICALT 2008, pp. 809–813, Santander, Spain, (July 2008)Google Scholar
  13. Breiman L.: Bagging predictors. Mach. Learn. 24((2), 123–140 (1996). doi: 10.1007/BF00058655 MathSciNetzbMATHGoogle Scholar
  14. Brooks, C., Winter, M., Greer, J., McCalla, G.: The Massive User Modelling System (MUMS). In: Lester, J.C., et al. (eds.) ITS 2004, LNCS 3220, pp. 635–645 (2004)Google Scholar
  15. Bull, S., Kay, J.: Metacognition and open learner models. In: Roll, I., Aleven, V. (eds.) Proceedings of Workshop on Metacognition and Self-Regulated Learning in Educational Technologies, International Conference on Intelligent Tutoring Systems, pp. 7–20 (2008)Google Scholar
  16. Bull S., Gardner P., Ahmad N., Ting J., Clarke B.: Use and trust of simple independent open learner models to support learning within and across courses. In: Houben, G.-J., McCalla, G., Pianesi, F., Zancanari, M. (eds) User Modeling, Adaptation and Personalization, pp. 42–53. Springer-Verlag, Berlin, Heidelberg (2009)CrossRefGoogle Scholar
  17. Burleson W.: Developing creativity, motivation, and self-actualization with learning systems. Int. J. Hum.-Compu. Stud. 62, 664–685 (2005)CrossRefGoogle Scholar
  18. Chin D.: Empirical evaluation of user models and user-adapted systems. User Model. User-adapt. Interact. 11, 181–194 (2001)CrossRefzbMATHGoogle Scholar
  19. Cocea M., Weibelzahl S.: Log file analysis for disengagement detection in e-Learning environments. User Model. User-adapt. Interact. 19(4), 341–385 (2009)CrossRefGoogle Scholar
  20. Collazos C.A., Guerrero L.A., Pino J.A., Renzi S., Klobas J., Ortega M., Redondo M.A., Bravo C.: Evaluating collaborative learning processes using system-based measurement. Educ. Technol. Soc. 10(3), 257–274 (2007)Google Scholar
  21. Daradoumis T., Martínez-Mónes A., Xhafa F.: A layered framework for evaluating online collaborative learning interactions. Int. J. Hum.-Comput. Stud. 64(7), 622–635 (2006)CrossRefGoogle Scholar
  22. Denaux, R., Aroyo, L., Dimitrova, V.: OWL-OLM: interactive Ontology-basedLls. Workshop on Personalisation for the Semantic Web PerSWeb05 at 10th International Conference on User Modeling, Edinburgh, UK, 23–29, (July 2005)Google Scholar
  23. Dimitracopoulou, A.: Computer based interaction analysis supporting self-regulation: achievements and prospects of an emerging research direction. In: Kinshuk, Spector, M., Sampson, D., Isaias, P. (Guest editors). Technology, Instruction, Cognition and Learning (TICL) vol. 6, no. 4 (2009)Google Scholar
  24. Dringus L.P., Ellis E.: Using data mining as a strategy for assessing asynchronous discussion forums. Comput. Educ. 45, 140–160 (2005)CrossRefGoogle Scholar
  25. Dringus L.P., Ellis E.: Temporal transitions in participation flow in an asynchronous discussion forum. Comput. Educ. 54(2), 340–349 (2010)CrossRefGoogle Scholar
  26. Duque R., Bravo C.: A method to classify collaboration in CSCL systems. Adaptive and natural computing algorithms. Lect. Notes Comput. Sci. 4431/2007, 649–656 (2007). doi: 10.1007/978-3-540-71618-1_72 CrossRefGoogle Scholar
  27. Durán, E.B.: Modelo del Alumno para Sistemas de aprendizaje Colaborativo. Workshop de Inteligencia Artificial en Educación (WAIFE 2006) (2006)Google Scholar
  28. Field, J.: Lifelong Learning and the New Educational Order. Trentham Books. ISBN 1858563461 (2006)Google Scholar
  29. Gama, J., Gaber, M.M. (eds.): Learning from Data Streams: Processing Techniques in Sensor Networks. Springer Verlag (2007), ISBN:978-3-540-73678-3Google Scholar
  30. Gaudioso, E., Santos, O.C., Rodriguez, A., Boticario, J.G.: A proposal for modelling a collaborative task in a web-based learning environment. Papers for the UM’03 Workshop ‘User and Group Models for Web-Based Adaptive Collaborative Environments’ in Conjunction with User Modelling 2003. 22 June University of Pittsburg (2003)Google Scholar
  31. Gaudioso E., Montero M., Talavera L., Hernandez-del-Olmo F.: Supporting teachers in collaborative student modeling: a framework and an implementation. Expert Syst. Appl. 36, 2260–2265 (2009)CrossRefGoogle Scholar
  32. Gómez-Sánchez E., Bote-Lorenzo M.L., Jorrín-Abellán I.M., Vega-Gorgojo G., Asensio-Pérez J.I., Dimitriadis Y.: Conceptual framework for design, technological support and evaluation of collaborative learning. Int. J. Eng. Educ. 25(3), 557–568 (2009)Google Scholar
  33. Heckmann, D.: Situation modeling and smart context retrieval with semantic web technology and conflict resolution. In: Roth-Berghofer, T.R., Schulz, S., Leake, D.B. (eds.) MRC 2005, LNAI 3946, pp. 34–47 (2006)Google Scholar
  34. Huang, Y., Dimitrova, V., Agarwal, P.: Detecting mismatches between a user’s and a system’s conceptualisations. Workshop on Personalisation for the Semantic Web PerSWeb05 at 10th International Conference on User Modeling, Edinburgh, UK, 23–29, (July 2005)Google Scholar
  35. Hummel H.G.K., Burgos D., Tattersall C., Brouns F., Kurvers H., Koper R.: Encouraging contributions in learning networks using incentive mechanisms. J. Comput. Assist. Lear. 21(5), 355–365 (2005)CrossRefGoogle Scholar
  36. Johnson, D.W., Johnson, R.: Cooperation and the use of technology. In: Jonassen, D. (ed.) Handbook of Research on Educational Communications and Technology, pp. 785–812 (2004)Google Scholar
  37. Kahrimanis, G., Meier, A., Chounta, I.-A., Voyiatzaki, E., Spada, H., Rummel, N., Avounis, N.: Assessing collaboration quality in synchronous CSCL problem-solving activities: adaptation and empirical evaluation of a rating scheme. Learning in the Synergy of Multiple Disciplines. 4th European Conferenceon Technology Enhanced Learning, EC-TEL 2009. Nice, France, September 29–October 2, 2009, Springer-Verlag, pp. 267–272 (2009)Google Scholar
  38. Kay, J.: Ontologies for reusable and scrutable student models. In: Mizoguchi, R. (ed.) AIED Workshop W2: Workshop on Ontologies for Intelligent Educational Systems, pp. 72–77 (1999)Google Scholar
  39. Kirk, R.E.: Experimental Design: Procedures for the Behavioral Sciences. Brooks/Cole, Pacific Grove, CA (1995)Google Scholar
  40. Kobsa A., Brusilovsky P., Kobsa A., Nejdl W.: Generic user modeling systems. In: (eds) The Adaptive Web: Methods and Strategies of Web Personalization, Springer Verlag, Heidelberg, Germany (2007)Google Scholar
  41. Kobsa A., Koenemann J., Pohl W.: Personalized hypermedia presentation techniques for improving online customer relationships. Knowl. Eng. Rev. 16(2), 111–155 (2001)CrossRefzbMATHGoogle Scholar
  42. Martínez, A., De La Fuente, P., Dimitriadis, Y.: An XML-based representation of collaborative interaction. Proceedings of CSCL 2003, pp. 379–383 (2003)Google Scholar
  43. Martínez A., Dimitriadis Y., Gómez E., Jorrín I., Rubia B., Marcos J.A.: Studying participation networks in collaboration using mixed methods. Int. J. Comput.-Supported Collaborative Learn 1(3), 383–408 (2006)CrossRefGoogle Scholar
  44. Meier A., Spada H., Rummel N.: A rating scheme for assessing the quality of computer-supported collaboration processes. Comput.-Supported Collaborative Learn. 2, 63–86 (2007)CrossRefGoogle Scholar
  45. Mizoguchi R.: The role of ontological engineering for AIED research. Comput. Sci. Inf. Syst. 2(1), 31–42 (2005)MathSciNetGoogle Scholar
  46. Muehlenbrock, M.: Formation of learning groups by using learner profiles and context information. In: Looi, C.-K., McCalla, G. (eds.) Proceedings of the 12th International Conference on Artificial Intelligence in Education AIED-2005. Amsterdam, The Netherlands (2005)Google Scholar
  47. Mustapha N., Jalali M., Jalali M.: Expectation maximization clustering algorithm for user modeling in Web usage mining systems. Eur. J. Sci. Res. 32(4), 467–476 (2009)Google Scholar
  48. Park, C.J., Hyun, J.S.: Comparison of two learning models for collaborative e-learning. In: Pan, Z., et al. (eds.) Edutainment 2006, LNCS 3942, pp. 50–59 (2006)Google Scholar
  49. Patriarcheas K., Xenos X.: Modelling of distance education forum: formal languages as interpretation methodology of messages in asynchronous text-based discussion. Comput. Educ. 52(2), 438–448 (2009)CrossRefGoogle Scholar
  50. Perera, D., Kay, J., Yacef, K., Koprinska, I.: Mining learners’ traces from an online collaboration tool. Workshop Educational Data Mining, Proceedings of the 13th International Conference of Artificial Intelligence in Education. Marina del Rey, CA, USA. July 2007, pp. 60–69 (2007)Google Scholar
  51. Redondo M.A., Bravo C., Bravo J., Ortega M.: Applying fuzzy logic to analyze collaborative learning experiences in an e-learning environment. USDLA J. (United States Distance Learning Association) 17(2), 19–28 (2003)Google Scholar
  52. Romero C., Ventura S.: Educational data mining: a review of the state-of-the-art. IEEE Trans. Syst. Man Cybernet. Part C: Appl. Rev. 40(6), 601–618 (2010)CrossRefGoogle Scholar
  53. Romero C., González P., Ventura S., del Jesus M.J., Herrera F.: Evolutionary algorithms for subgroup discovery in e-learning: a practical application using Moodle data. Expert Syst. Appl. 36, 1632–1644 (2009)CrossRefGoogle Scholar
  54. Russell S., Norvig P.: Artificial Intelligence: A Modern Approach. Prentice Hall Series in Artificial Intelligence, Englewood Cliffs, NJ (1995)zbMATHGoogle Scholar
  55. Santos, O.C., Rodríguez, A., Gaudioso, E., Boticario, J.G.: Helping the tutor to manage a collaborative task in a web-based learning environment. AIED 2003: Supplementary Proceedings, pp. 153–162 (2003)Google Scholar
  56. Santos, O.C., Boticario, J.G., Raffenne, E., Pastor, R.: Why using dotLRN? UNED use cases. Proceedings of the FLOSS (Free/Libre/Open Source Systems) International Conference 2007. Jerez de la Frontera, Spain, pp. 195–212 (2007)Google Scholar
  57. Soller A.: Supporting social interaction in an intelligent collaborative learning system. Int. J. Artif. Intell. Educ. 12(1), 40–62 (2001)Google Scholar
  58. Soller A., Martínez-Monés A., Jermann P., Muehlenbrock M.: From mirroring to guiding: a review of state of the art technology for supporting collaborative learning. Int. J. Artif. Intell. Educ. 15(4), 261–290 (2005)Google Scholar
  59. Steffens K.: Self-regulation and computer based learning. Anuario de Psicología 32(2), 77–94 (2001)MathSciNetGoogle Scholar
  60. Strijbos J.-W., Fischer F.: Methodological challenges for collaborative learning research. Learn. Instr. 17, 389–393 (2007)CrossRefGoogle Scholar
  61. Talavera, L., Gaudioso, E.: 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 2004), Valencia, Spain, 2004, pp. 17–23 (2004)Google Scholar
  62. Teng, C., Lin, C., Cheng, S., Heh, J.: Analyzing user behavior distribution on e-learning platform with techniques of clustering. In: Society for Information Technology and Teacher Education International Conference, pp. 3052–3058 (2004)Google Scholar
  63. Van Velsen L., Vander Geest T., Klaassen R., Steehouder M.: User-centered evaluation of adaptive and adaptable systems: a literature review. Knowl. Eng. Rev. 23(3), 261–281 (2008)CrossRefGoogle Scholar
  64. Vidou, G., Dieng-Kuntz, R., Ghadi, A.E., Evangelou, C., Giboin, A., Tifous, A. Jacquemart, S.: Towards an ontology for knowledge management in communities of practice. In: Reimer, U., Karagiannis, D. (eds.) PAKM 2006, LNAI 4333, pp. 303–314 (2006)Google Scholar

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© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.Artificial Intelligence Department, E.T.S.I.I., UNEDCiudad UniversitariaMadridSpain

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