Abstract
This research aims to support collaborative distance learners by demonstrating how a probabilistic machine learning method can be used to model and analyze online knowledge sharing interactions. The approach applies Hidden Markov Models and Multidimensional Scaling to analyze and assess sequences of coded online student interaction. These analysis techniques were used to train a system to dynamically recognize (1) when students are having trouble learning the new concepts they share with each other, and (2) why they are having trouble. The results of this research may assist an instructor or intelligent coach in understanding and mediating situations in which groups of students collaborate to share their knowledge.
Similar content being viewed by others
References
Baker, M. and Lund,K.: 1997, Promoting re.ective interactions in a computer-supported collaborative learning environment. Journal of Computer Assisted Learning 13,175–193.
Barros, B. and Verdejo, M.F.: 1999, An approach to analyse collaboration when shared structured workspaces are used for carrying out group learning processes. Proceedings of the Ninth International Conference on Artificial Intelligence in Education, Le Mans, France, pp.449–456.
Brown, A. and Palincsar, A.: 1989, Guided,cooperative learning and individual knowledge acquisition. In L.Resnick (ed.), Knowing,Learning,and Instruction:Essays in Honor of Robert Glaser. Hillsdale, NJ: Lawrence Erlbaum Associates, pp. 393–451.
Carletta, J., Isard, A., Isard,S., Kowtko,J., Doherty-Sneddon, G. and Anderson, A.: 1997, The reliability of a dialogue structure coding scheme. Computational Linguistics 23, 13–32.
Charniak, E.: 1993, Statistical Language Learning. Cambridge, MA: MIT Press.
Chi, M.T.H., Bassok, M., Lewis, M.W., Reimann, P. and Glaser, R.: 1989, Self-Explanations:How students study and use examples in learning to solve problems. Cognitive Science 13,145–182.
Constantino-Gonzalez, M.A., Suthers, D. and Escamilla de los Santos, J.: 2003, Coaching web-based collaborative learning based on problem solution differences and participa-tion. International Journal of Artificial Intelligence in Education 13, 263–299.
Dillenbourg, P.: 1999, What do you mean by “Collaborative Learning ”? In P. Dillenbourg (ed.), Collaborative learning:Cognitive and computational approaches. Amsterdam: Elsevier Science, pp.1–19.
Fisher, L. and van Belle, G.: 1993, Biostatistics:A methodology for the health sciences. New York: John Wiley and Sons Inc.
Freitag, D. and McCallum, A.: 2000, Information extraction with HMM structures learned by stochastic optimization. Proceedings of the AAAI 2000, Austin, Texas, pp. 584–589.
Goodman, B., Linton, F., Gaimari, R., Hitzeman, J., Ross, H. and Zarrella, J.: 2004, Using dialog features to predict trouble during collaborative learning. Manuscript in prepara-tion.
Gott, S. and Lesgold, A.: 2000, Competence in the workplace:How cognitive performance models and situated instruction can accelerate skill acquisition. In R. Glaser (ed.), Advances in Instructional Psychology: Vol. 5. Educational Design and Cognitive Science. Mahwah, NJ: Lawrence Erlbaum Associates, pp. 239–327.
Jeong, H.: 1998, Knowledge Co-construction During Collaborative Learning. Doctoral Dissertation. University of Pittsburgh, Pittsburgh, PA.
Jermann, P. and Schneider, D.: 1997, Semi-structured interface in collaborative problem solving. Proceedings of the First Swiss Workshop on Distributed and Parallel Systems, Lausanne, Switzerland.
Jermann, P., Soller, A. and Muehlenbrock, M.: 2001, Frommirroring to guiding:Areviewof state of the art technology for supporting collaborative learning. Proceedings of the First European Conference on Computer-Supported Collaborative Learning, Maastricht, The Netherlands, pp.324–331.
Jermann, P., Soller, A. and Lesgold, A.: 2004, Computer software support for CSCL. In P. Dillen-bourg (series ed.), and J.W. Strijbos, P.A. Kirschner, and R.L. Martens (vol.eds.), Computer-Supported Collaborative Learning:Vol 3.What We Know About CSCL...and Implementing it in Higher Education. Boston, MA: Kluwer Academic Publishers,pp.141–166.
Juang, B. and Rabiner, L.: 1985, A probabilistic distance measure for Hidden Markov Models. AT and T Technical Journal 64 (2), 391–408.
Katz, S., Aronis, J. and Creitz, C.: 1999, Modelling pedagogical interactions with machine learning. Proceedings of the Ninth International Conference on Artificial Intelligence in Education, Le Mans, France, pp. 543–550.
Kruskal, J. and Wish, M.: 1978, Multidimensional scaling. Newbury Park, California: Sage Publications.
Lavery, T., Franz,T., Winquist, J. and Larson, J.: 1999, The role of information exchange in predicting group accuracy on a multiple judgment task. Basic and Applied Social Psychology 2 (4), 281–289.
Linton, F. Goodman, B., Gaimari,R., Zarella,J. and Ross,H.: 2003, Student modeling for an intelligent agent in a collaborative learning environment. Proceedings of User Modeling 2003 Johnstown, PA,pp. 342–351.
Looney,C.: 1997, Pattern Recognition Using Neural Networks. New York: Oxford Univer-sity Press.
McManus, M. and Aiken,R.: 1995, Monitoring computer-based problem solving. Journal of Artificial Intelligence in Education 6 (4), 307–336.
Muehlenbrock, M.: 2001, Action-based Collaboration Analysis for Group Learning. Doctoral Dissertation, University of Duisburg, Germany.
Perret-Clermont, A., Perret, J.,and Bell, N.: 1991, The social construction of meaning and cognitive activity in elementary school children. In L.Resnick, J.Levine and S. Teasley (eds.), Perspectives on Socially Shared Cognition. American Psychological Society, Washington, DC, pp. 41–62.
Rabiner, L.: 1989, A tutorial on Hidden Markov Models and selected applications in speech recognition. Proceedings of the IEEE 77 (2), 257–286.
Roth, D.: 1999, Learning in natural language. Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI '99), Stockholm, Sweden, pp. 898–904.
Rumbaugh, J., Blaha, M., Premerlani, W., Eddy, F. and Lorensen, W.: 1991, Object-oriented modeling and design. Englewood Cliffs, NJ: Prentice Hall.
Schrodt, P.: 2000, Pattern recognition of international crises using Hid-den Markov Models. In D. Richards (ed.), Political Complexity:Non-linear Models of Politics. Ann Arbor: University of Michigan Press, pp. 296–328.
Shepard, R.: 1980, Multidimensional Scaling,Treetting, and clustering. Science 210, 90–398.
Smyth, P.: 1997, Clustering sequences with Hidden Markov Models. In M. Mozer, M. Jordan and T. Petsche (eds.), Proceedings of the 1996 Conference on Advances in Neural Information Processing Systems 9, MIT Press, pp. 648–654.
Soller, A.: 2001, Supporting social interaction in an intelligent collaborative learning system. International Journal of Artificial Intelligence in Education 12 (1), 40–62.
Soller, A.: 2002, Computational Analysis of Knowledge Sharing in Collaborative Distance Learning. Doctoral dissertation, University of Pittsburgh, Pittsburgh, PA.
Soller, A.: 2004, Understanding knowledge sharing breakdowns:A meeting of the quantitative and qualitative minds. Journal of Computer Assisted Learning 20.
Soller, A. and Lesgold, A.: in press, Modeling the process of knowledge sharing. In U. Hoppe and M.Ikeda (Eds.), New Technologies for Collaborative Learning. Kluwer Aca-demic Publishers.
Soller, A., Linton, F., Goodman, B. and Gaimari, R.: 1996, [Videotaped study:3 groups of 4-5 students each solving software system design problems using Object Modeling Technique during a one week course at The MITRE Institute ]. Unpublished raw data.
Stevens, R., Ikeda, J., Casillas, A., Palacio-Cayetano J.and Clyman,S.: 1999, Artificial neural network-based performance assessments. Computers in Human Behavior 15 (1999), 295–313.
Stevens, R., Soller,A., Cooper, M., and Sprang, M.: 2004, Modeling the development of problem-solving skills in chemistry with a web-based tutor. Proceedings of the 7th International Conference on Intelligent Tutoring Systems (ITS 2004), Maceio, Brazil.
Tedesco, P.A.: 2003, MarCo:Building an Artificial conflict mediator to support group planning interactions. International Journal of Artificial Intelligence in Education 13, 117–155.
Vendlinski, T. and Stevens, R.: 2002, Assessing student problem-solving skills with complex computer-based tasks.Journal of Technology, Learning, and Assessment, 1 (3), [Avail-able:http://www.jtla.org ].
Viterbi, A.: 1967, Error bounds for convolutional codes and an asymptotically optimal decoding algorithm. IEEE Transactions on Information Theory 13, 260–269.
Walker, M., Litman, D., Kamm,C. and Abella, A.: 1997, PARADISE:A framework for evaluating spoken dialogue agents. Proceedings of the 35th Annual Meeting of the Association of Computational Linguistics (ACL '97), Madrid, Spain, pp. 271–280.
Webb, N.: 1992, Testing a theoretical model of student interaction and learning in small groups. In R. Hertz-Lazarowitz and N. Miller (eds.), Interaction in Cooperative Groups: The Theoretical Anatomy of Group Learning. New York: Cambridge University Press, pp.102–119
Webb, N. and Palincsar, A.: 1996, Group processes in the classroom. In D. Berlmer and R. Calfee (eds.), Handbook of Educational Psychology. New York: Simon and Schuster Macmillan, pp. 841–873.
Winquist, J.R. and Larson, J.R.: 1998, Information pooling:When it impacts group decision making. Journal of Personality and Social Psychology 74, 371–377.
Yang, J., Xu, Y. and Chen,C.: 1997, Human action learning via Hidden Markov Model. IEEE Transactions on Systems,Man,and Cybernetics-Part A:Systems and Humans 27 (1), 34–44.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Soller, A. Computational Modeling and Analysis of Knowledge Sharing in Collaborative Distance Learning. User Modeling and User-Adapted Interaction 14, 351–381 (2004). https://doi.org/10.1023/B:USER.0000043436.49168.3b
Issue Date:
DOI: https://doi.org/10.1023/B:USER.0000043436.49168.3b