Abstract
In recent years, knowledge building as a pedagogical approach has gained in popularity, and some sizeable networks of teachers using the Knowledge Forum® platform to support the learning and teaching process have emerged. This provides a unique opportunity for empirical explorations that span across different classrooms to identify trends and regularities in the knowledge building developmental trajectory of a community of learners (including the teacher who is designing and facilitating the educational process) in classroom contexts. On the other hand, it poses a serious challenge to teachers and researchers on how to analyze and make sense of the large corpora of discourses generated by students. This study is an attempt to develop a methodology that can take advantage of the increasing collection of online corpora in Knowledge Forum® for the incremental establishment of profiles and models of knowledge building behavior and interactions that reflect different levels of productivity in knowledge building. The focus of the present research is on the progress and development of the entire community, which aligns with the predominant focus on the construction and advancement of collective knowledge in knowledge building research.
The goal of this study is to develop a methodology, including the identification of appropriate indicators and tools that can be used to provide a quick, first level assessment of the level of knowledge building reflected in a Knowledge Forum® corpus using machine analysis and visualization, building on previous research in the area. Such a methodology will help to (1) build an empirically grounded understanding of learners’ trajectories of advancement in knowledge building; and (2) identify pedagogical and facilitation designs that are more conducive to deeper levels of knowledge building by students. We implemented automatic encoding and visualization on a number of Knowledge Forum corpora collected from primary and secondary classrooms in Hong Kong around the themes of energy crisis and global warming.
The findings indicate that visualization of different indicators applied in sequence provides some useful insight on the quality and level of engagement in online asynchronous discussions. Basic participation statistics at discourse and thread levels are useful in discriminating truncated inquiries from other, more engaging ones. Linear visualization of the time sequence of discourse markers for argumentation, questions and scaffolds sheds light on whether there is evidence of cycles of progressive inquiry. Fine grained identification of subject matter content reveals whether the discussion is likely to be an extensive inquiry or an intensive one. The study also demonstrates a clear need for automated coding and visualization of discourse corpora to be augmented and validated by a review of the actual discourse data by human researchers to yield more in-depth understanding of the knowledge building processes.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Andriessen, J., Baker, M., & Suthers, D. (2003). Arguing to learn: Confronting cognition in computer-supported collaborative learning environment. Dordrecht: Kluwer Academic.
Andriessen, J. E. B., & Coirier, P. (1999). Foundations of argumentative text processing. Amsterdam: Amsterdam University Press.
Bereiter, C. (2002). Education and mind in the knowledge age. Mahwah: Lawrence Erlbaum.
Biggs, J. B. (1999). Teaching for quality learning at university. Buckingham: Society for Research into Higher Education and Open University Press.
Biggs, J. B., & Collis, K. F. (1982). Evaluating the quality of learning: The SOLO taxonomy. New York: Academic.
Burtis, J. (1998). The analytic toolkit. The Ontario Institute for Studies in Education, The University of Toronto: Knowledge building research team.
Card, S. K., Mackinlay, J. D., & Shneiderman, B. (1999). Readings in information visualization: Using vision to think. San Francisco: Morgan Kaufmann.
Dyke, G., Lund, K., & Girardot, J. (2009). Tatiana: An environment to support the CSCL analysis process. In Proceedings of the 9th international conference on computer supported collaborative learning (Vol. 1, pp. 58–67). Rhodes, Greece: International Society of the Learning Sciences.
Erkens, G., & Janssen, J. (2008). Automatic coding of communication in collaboration protocols. International Journal of Computer-Supported Collaborative Learning, 3(4), 447–470.
Giguet, E., Lucas, N., Blondel, F., & Bruillard, E. (2009). Share and explore discussion forum objects on the Calico website. In Community events proceedings of the 9th international conference on computer supported collaborative learning 2009 (pp. 174–176). Poster presented at the computer supported collaborative learning 2009 (CSCL2009), Rhodes, Greece: International Society of the Learning Sciences.
Goodman, B. A., Linton, F. N., Gaimari, R. D., Hitzeman, J. M., Ross, H. J., & Zarrella, G. (2005). Using dialogue features to predict trouble during collaborative learning. User Modeling User-Adaptated Interaction, 15, 85–134.
Gweon, G., Jeon, S., Lee, J., Finger, S., & Rosé, C. P. (this volume). A framework for assessment of student project groups on-line and off-line. In S. Puntambekar, G. Erkens, & C. Hmelo-Silver (Eds.), Analyzing interactions in CSCL: Methods, approaches and issues (pp. 293–317) Springer.
Hakkarainen, K. (1998). Epistemology of scientific inquiry and computer-supported collaborative learning. Unpublished thesis for the degree of Doctor of Philosophy. University of Toronto.
Hakkarainen, K. (2004). Pursuit of explanation within a computer-supported classroom. International Journal of Science Education, 26(8), 979–996.
Hakkarainen, K., & Sintonen, M. (2002). Interrogative model of inquiry and computer-s collaborative learning. Science & Education, 11, 25–43.
Heeman, P. A., Byron, D., & Allen, J. F. (1998). Identifying discourse markers in spoken dialog. Paper presented at the AAAI Spring symposium on applying machine learning and discourse processing, Stanford, March.
Hmelo-Silver, C. E., Jordan, R., Liu, L., & Chernobilsky, E. (this volume). Representational tools for understanding complex computer-supported collaborative learning environments. In S. Puntambekar, G. Erkens, & C. Hmelo-Silver (Eds.), Analyzing interactions in CSCL: Methods, approaches and issues (pp. 83–106) Springer.
Janssen, J., Erkens, G., & Kanselaar, G. (2007a). Visualization of agreement and discussion processes during computer-supported collaborative learning. Computers in Human Behavior, 23, 1105–1125.
Janssen, J., Erkens, G., Kanselaar, G., & Jaspers, J. (2007b). Visualization of participation: Does it contribute to successful computer-supported collaborative learning? Computers & Education, 49(4), 1037–1065.
Koschmann, T. (2003). Computer-supported collaborative learning. In J. W. Guthrie (Ed.), Encyclopedia of education (2nd ed., pp. 468–471). New York: Macmillan Reference.
Laferrière, T., & Allaire, S. (2005). Scaffolding student teachers’ online discourse for knowledge building purposes. Technology and Teacher Education Annual, 2, 939.
Lai, M., & Law, N. (2006). Peer scaffolding of knowledge building through collaboration of groups with differential learning experiences. Journal of Educational Computing Research, 35(2), 121–142.
Lamon, M., Reeve, R., & Scardamalia, M. (2001). Mapping the growth of deeply principled understandings in a knowledge building community [Electronic Version]. New directions in knowledge building. Retrieved 14 July 2009, from http://ikit.org/lamon/mapping.html.
Landauer, T., Foltz, P., & Laham, D. (1998). An introduction to latent semantic analysis. Discourse Process, 25, 259–284.
Law, N. (2005). Assessing learning outcomes in CSCL settings. In T. Koschmann, T.-W. Chan, & D. D. Suthers (Eds.), Computer supported collaborative learning conference (CSCL) (pp. 373–377). Taipei, Taiwan: Lawrence Erlbaum Associates.
Law, N., Lu, J., Leng, J., Yuen, J., & Lai, M. (2008). Understanding knowledge building from multiple perspectives. Paper presented in the workshop on common framework for CSCL interaction analysis, International conference for the learning sciences 2008, Utrecht, Netherlands.
Law, N., Lu, J., Leng, J., Yuen, J., & Lai, M. (2009). Exploring knowledge building discourse using argumentation markers. Paper presented in the workshop on analysis of computer-supported collaborative learning (CSCL) discourse: Advances in theory and analysis tools, CITE research symposium 2009. Hong Kong: University of Hong Kong.
Law, N., & Wong, E. (2003). Developmental trajectory in knowledge building: An investigation. In B. Wasson, S. Ludvigsen, & U. Hoppe (Eds.), Designing for change in networked learning environments (pp. 57–66). Dordrecht: Kluwer Academic Publishers.
Law, N., Yuen, J., Huang, R., Li, Y., & Pan, N. (2007). A learnable content & participation analysis toolkit for assessing CSCL learning outcomes and processes. In C. Chinn, G. Erkens, & S. Puntambekar (Eds.), Mice, minds & society: Proceedings of the international conference on computer supported collaborative learning 2007 (pp. 408–417). Rutgers University, New Jersey: International Society of the Learning Sciences, Inc.
Lee, E. Y. C., Chan, C. K. K., & van Aalst, J. (2005). Students assessing their own knowledge advances in a computer-supported collaborative environment. In T. Koschmann, D. Suthers, & T.-W. Chan (Eds.), Proceedings of computer supported collaborative learning 2005: The next 10 years. NJ: Lawrence Erlbaum Associates.
Lee, E. Y. C., Chan, C. K. K., & van Aalst, J. (2006). Students assessing their own collaborative knowledge building. International Journal for Computer-Supported Collaborative Learning, 1, 277–307.
Pennebaker, J. W., Booth, R. J., & Francis, M. E. (2007). Linguistic inquiry and word count (LIWC). Mahwah, NJ: Lawrence Erlbaum Associates.
Popper, K. R. (1972). Objective knowledge: An evolutionary approach. Oxford, UK: Clarendon.
Rahikainen, M., Lallimo, J., & Hakkarainen, K. (2001). Progressive inquiry in CSILE environment: Teacher guidance and students’ engagement. In P. Dillenbourg, A. Eurelings, & K. Hakkarainen (Eds.), European perspectives on computer-supported collaborative learning: Proceedings of the first European conference on CSCL (pp. 520–528). Maastricht, the Netherlands: Maastricht McLuhan Institute.
Reimann, P., Yacef, K., Kay, J. (this volume). Analyzing collaborative interactions with data mining methods for the benefit of learning. In S. Puntambekar, G. Erkens, & C. Hmelo-Silver (Eds.), Analyzing interactions in CSCL: Methods, approaches and issues (pp. 161–185) Springer.
Rosé, C. P., Wang, Y. C., Cui, Y., Arguello, J., Stegmann, K., Weinberger, A., et al. (2008). Analyzing collaborative learning processes automatically: Exploiting the advances of computational linguistics in computer-supported collaborative learning. International Journal of Computer Supported Collaborative Learning, 3(3), 237–271.
Saab, N., van Joolingen, W. R., & van Hout-Wolters, B. H. A. M. (2005). Communication in collaborative discovery learning. The British Journal of Educational Psychology, 75(4), 603–621.
Salton, G., Wong, A., & Yang, C. S. (1975). A vector space model for automatic indexing. Communcations of the ACM, 18(11), 613–620.
Scardamalia, M. (2002). Collective cognitive responsibility for the advancement of knowledge. In B. Smith (Ed.), Liberal education in a knowledge society (pp. 67–98). Chicago, IL: Open Court.
Scardamalia, M., & Bereiter, C. (1991). Higher levels of agency for children in knowledge building: a challenge for the design of new knowledge media. The Journal of the Learning Sciences, 1(1), 37–68.
Scardamalia, M., & Bereiter, C. (1992). Text-based and knowledge-based questioning by children. Cognition and Instruction, 9(3), 177–199.
Scardamalia, M., & Bereiter, C. (1994). Computer support for knowledge-building communities. The Journal of the Learning Sciences, 3, 265–283.
Scardamalia, M., & Bereiter, C. (2003). Knowledge building environments: Extending the limits of the possible in education and knowledge work. In A. DiStefano, K. E. Rudestam, & R. Silverman (Eds.), Encyclopedia of distributed learning (pp. 269–272). Thousand Oaks, CA: Sage Publications.
Scardamalia, M., & Bereiter, C. (2006). Knowledge building: Theory, pedagogy, and technology. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 97–115). New York, NY: Cambridge University Press.
Scardamalia, M., Bereiter, C., & Lamon, M. (1994). The CSILE project: Trying to bring the classroom in to world 3. In K. McGilly (Ed.), Classroom lessons: Integration cognitive theory and classroom practice (pp. 201–228). Cambridge: MIT Press.
Scott, J. (2000). Social network analysis: A handbook. London: Sage.
Soller, A. (2004). Computational modeling and analysis of knowledge sharing in collaborative distance learning. User Modeling and User-Adapted Interaction, 14, 351–381.
Stolcke, A., Coccaro, N., Bates, R., Taylor, P., Van Ess-Dykema, C., Ries, K., et al. (2000). Dialogue act modeling for automatic tagging and recognition of conversational speech. Computational Linguistics, 26, 339–373.
Suthers, D., & Medina, R. (this volume). Tracing interaction in distributed collaborative learning. In S. Puntambekar, G. Erkens, & C. Hmelo-Silver (Eds.), Analyzing interactions in CSCL: Methods, approaches and issues (pp. 341–366) Springer.
Tan, S. C., Yeo, A. C. J., & Lim, W. Y. (2005). Changing epistemology of science learning through inquiry with computer-supported collaborative learning. Journal of Computers in Mathematics and Science Teaching, 24(4), 367.
Teplovs, C. (2008). The knowledge space visualizer: A tool for visualizing online discourse. Paper presented at the common framework for CSCL interaction analysis workshop at the international conference of the learning sciences 2008. Utrecht, NL.
Teplovs, C., & Scardamalia, M. (2007). Visualizations for knowledge building. Paper presented at IKIT knowledge building summer institute 2007, Toronto, Canada.
van Aalst, Chan, K. K. C., & Lee, E. (2002). Developing knowledge-building inquiry through knowledge building portfolios in a Hong Kong classroom. Paper presented in the symposium “Knowledge Building: Multicultural Perspectives” at the annual meeting of the American educational research associations, New Orleans.
van Boxtel, C., van der Linden, J., & Kanselaar, G. (2000). Collaborative learning tasks and the elaboration of conceptual knowledge. Learning Instruction, 10(4), 311–330.
Veldhuis-Diermanse, A. E. (2002). CSCLearning? Participation, learning activities and knowledge construction in computer-supported collaborative learning in higher education. Unpublished PhD Thesis, Wageningen University, Wageningen.
Zhang, J., Scardamalia, M., Lamon, M., Messina, R., & Reeve, R. (2007). Socio-cognitive dynamics of knowledge building in the work of 9- and 10-year-olds. Educational Technology Research and Development, 55, 117–145.
Acknowledgments
This research is supported by an earmarked research grant from the Research Grants Council Hong Kong (RGC Ref: 7440/05 H) and funding from the IT Strategic Research Theme at the University of Hong Kong.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Law, N., Yuen, J., Wong, W.O.W., Leng, J. (2011). Understanding Learners’ Knowledge Building Trajectory Through Visualizations of Multiple Automated Analyses. In: Puntambekar, S., Erkens, G., Hmelo-Silver, C. (eds) Analyzing Interactions in CSCL. Computer-Supported Collaborative Learning Series, vol 12. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-7710-6_3
Download citation
DOI: https://doi.org/10.1007/978-1-4419-7710-6_3
Published:
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4419-7709-0
Online ISBN: 978-1-4419-7710-6
eBook Packages: Humanities, Social Sciences and LawEducation (R0)