Abstract
In this chapter we present a framework for learner modelling that combines latent semantic analysis and social network analysis of online discourse. The framework is supported by newly developed software, known as the Knowledge, Interaction and Social Student Modelling Explorer (KISSME), that employs highly interactive visualizations of interactions and semantic similarity among learners. Our goal is to develop, use and refine KISSME to generate and test predictive models of learner interactions to optimise learning.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bavelas, A. (1950). Communication patterns in task-oriented groups. Journal of the Acoustical Society of America, 22, 271–282.
Bavelas, A., & Barrett, D. (1951). An experimental approach to organizational communication. Personnel, 27, 366–371.
Coleman, J. S., Katz, E., & Menzel, H. (1957). The diffusion of an innovation among physicians. Sociometry, 20, 253–270.
Coleman, J. S., Katz, E., & Menzel, H. (1966). Medical innovation: A diffusion study. Indianapolis, IN: Bobbs-Merrill.
Contractor, N. (2009). The emergence of multidimensional networks. Journal of Computer-Mediated Communication, 14, 743–747.
de Laat, M., Lally, V., Lipponen, L., & Simons, R.-J. (2007). Investigating patterns of interaction in networked learning and computer-supported collaborative learning: A role for Social Network Analysis. International Journal of Computer-Supported Collaborative Learning, 2, 87–103.
Dessus, P. (2009). An overview of LSA-based systems for supporting learning and teaching. In Proceeding of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling. IOS Press: Amsterdam, Netherlands.
Dessus, P., Mandin, S., & Zampa, V. (2008). What is teaching? Cognitive-based tutoring principles for the design of a learning environment. In S. Tazi & K. Zreik (Eds.), Common innovation in e-learning, machine learning and humanoid (ICHSL.6) (pp. 49–55). Paris, France: Europa/IEEE.
Freeman, L. C., Romney, A. K., & Freeman, S. C. (1987). Cognitive structure and informant accuracy. American Anthropologist, 89, 310–325.
Fujita, N. (this volume). Online graduate education course using knowledge forum. In D. D. Suthers, K. Lund, C. P. Rosé, C. Teplovs, & N. Law (Eds.), Productive multivocality in the analysis of group interactions, Chapter 20. New York, NY: Springer.
Hara, N., Bonk, C. J., & Angeli, C. (2000). Content analyses of on-line discussion in an applied educational psychology course. Instructional Science, 28, 115–152.
Haythornthwaite, C. (2001). Exploring multiplexity: Social network structure in a computer-supported distance learning class. The Information Society, 17, 211–226.
Henri, F. (1992). Computer conferencing and content analysis. In A. R. Kaye (Ed.), Collaborative learning through computer conferencing. London, UK: Springer.
Kintsch, E., Caccmise, D., Franzke, M., Johnson, N., & Dooley, S. (2007). Summary street®: Computer-guided summary writing. In T. K. Landauer, D. S. McNamara, S. Dennis, & W. Kintsch (Eds.), Handbook of latent semantic analysis. Mahwah, NJ: Lawrence Erlbaum Associates.
Krackhardt, D. (1987). Cognitive social structures. Social Networks, 9, 109–134.
Landauer, T. K., & Dumais, S. T. (1997). A solution to Plato’s problem: The latent semantic analysis theory of the acquisition, induction, and representation of knowledge. Psychological Review, 104, 211–240.
Landauer, T. K., Laham, D., & Derr, M. (2004). From paragraph to graph: Latent semantic analysis for information visualization. Proceedings of the National Academy of Sciences of the United States of America, 101, 5214–5219.
Leavitt, H. J. (1951). Some effects of communication patterns on group performance. Journal of Abnormal and Social Psychology, 46, 38–50.
MartÃnez, A., Dimitriadis, Y., Rubia, B., Gomez, E., & de la Fuente, P. (2003). Combining qualitative evaluation and social network analysis for the study of classroom social interactions. Computers & Education, 41, 353–368.
Penumatsa, P., Ventura, M., Graesser, A. C., Louwerse, M. M., Hu, X., Cai, Z., et al. (2006). The right threshold value: What is the right threshold of cosine measure when using latent semantic analysis for evaluating student answers? International Journal on Artificial Intelligence Tools, 15, 767–778.
Reffay, C., & Chanier, T. (2003). How social network analysis can help to measure cohesion in collaborative distance-learning. In Designing for change in networked learning. Proceedings of the international conference on Computer Supported Collaborative Learning 2003 (pp. 343–352). Kluwer Academic Publishers: Bergen, Norway.
Reffay, C., Teplovs, C., & Blondel, F.-M. (2011). Productive re-use of CSCL data and analytic tools to provide a new perspective on group cohesion. In Proceedings of the 10th International Conference on Computer Supported Collaborative Learning, 2011, Hong Kong.
Rehder, B., Schreiner, M. E., Wolfe, M. B., Laham, D., Landauer, T. K., & Kintsch, W. (1998). Using latent semantic analysis to assess knowledge: Some technical considerations. Discourse Processes, 25, 337–354.
Reimann, P. (2009). Time is precious: Variable- and event-centred approaches to process analysis in CSCL research. International Journal of Computer-Supported Collaborative Learning, 4, 239–257.
Rogers, E. M. (1979). Network analysis of the diffusion of innovations. In P. W. Holland & S. Leinhardt (Eds.), Perspectives on social network research (pp. 137–164). New York, NY: Academic.
Scardamalia, M., & Bereiter, C. (2006). Knowledge building: theory, pedagogy, and technology. In K. Sawyer (Ed.), Cambridge Handbook of the Learning Sciences (pp. 97–118). New York: Cambridge University Press. Suthers, D., Dwyer, N., Medina, R., & Vatrapu, R. (2010). A framework for conceptualizing, representing, and analyzing distributed interaction. International Journal of Computer-Supported Collaborative Learning, 5, 5–42.
Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Cambridge, MA: Harvard University Press.
Wasserman, S., & Faust, K. (1997). Social network analysis: Methods and applications. Cambridge, UK: Cambridge University Press.
Wellman, B. (1979). The community question: The intimate networks of East Yorkers. American Journal of Sociology, 84, 1201–1231.
Wolfe, M. B., Schreiner, M. E., Rehder, B., Laham, D., Foltz, P. W., Kintsch, W., et al. (1998). Learning from text: Matching readers and text by latent semantic analysis. Discourse Processes, 25, 309–336.
Zampa, V., & Lemaire, B. (2002). Latent semantic analysis for user modeling. Journal of Intelligent Information Systems, 18(1), 15–30.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer Science+Business Media New York
About this chapter
Cite this chapter
Teplovs, C., Fujita, N. (2013). Socio-Dynamic Latent Semantic Learner Models. In: Suthers, D., Lund, K., Rosé, C., Teplovs, C., Law, N. (eds) Productive Multivocality in the Analysis of Group Interactions. Computer-Supported Collaborative Learning Series, vol 15. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-8960-3_21
Download citation
DOI: https://doi.org/10.1007/978-1-4614-8960-3_21
Published:
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4614-8959-7
Online ISBN: 978-1-4614-8960-3
eBook Packages: Humanities, Social Sciences and LawEducation (R0)