The goal of the workshop is to advance the state-of-the-art of coding group knowledge building. In particular, the workshop will aim to define a coding scheme, a set of coding schemes or criteria for a coding scheme that is well suited for formalizing and tracking group knowledge-building processes in a setting like the VMT case study of text chat and shared whiteboard for math exploration by a small group of students.
Participants will be encouraged to consider specific coding strategies in the context of this specific data set. The dataset is "Team B from VMT Spring Fest 2006". It is available at: http://vmt.mathforum.org/vmtwiki/index.php/VMTGroupB∞ . Download the spreadsheet with the chat postings (teamb.xls). To replay the entire four-hour interaction including the graphics exactly as seen by the participants, download the replayer (VMT Player v1 (2006)) and the data for the replayer (teamb.jno). The math topic for the students is also downloadable, as are the wiki pages where the students posted their findings.

The dataset is "Team B from VMT Spring Fest 2006". It is available at: http://vmt.mathforum.org/vmtwiki/index.php/VMTGroupB∞ . Download the spreadsheet with the chat postings (teamb.xls). To replay the entire four-hour interaction including the graphics exactly as seen by the participants, download the replayer (VMT Player v1 (2006)) and the data for the replayer (teamb.jno).

People wanting to present a coding schemes should submit a two- to four-page position paper describing their present work developing coding schemes for group knowledge building in disciplines. People wanting to attend the workshop without presenting should provide a one-page statement of research interest.

Analysis Complementary to Knowledge-Building Coding

Coding schemes to explicate the degree of knowledge building in virtual environments may be aided by analysis of interaction patterns that focus on the frequency, lag, size and dimensionality of interactions between members in a rich environment like VMT. To seed thinking about complementary approaches to knowledge building, the workshop organizers will perform network analysis on the 3,000 chat postings and 3,000 other actions in the VMT data set in order to identify statistical patterns or cycles of interaction. Since the networks in this corpus are closed and small, this pre-workshop analysis will focus on small network evolution and elaborating semantically meaningful measures of tie-strength between members. Analyzing evolution involves developing a time-series set of network views, and possibly addressing the state of the network as a feature that contributes to the other forms of analysis. Through the efforts of this workshop, we may also derive measures of tie-strength from the results of the knowledge-building coding schemes.
Gerry Stahl has investigated issues and theories related to how small groups build knowledge together (Stahl, 2006). He has directed the Virtual Math Teams Project (Stahl, 2009), which studies the interactions within online groups of students exploring math problems. He is Executive Editor of the International Journal of Computer-Supported Collaborative Learning. He teaches at the College of Information Science and Technology at Drexel University in Philadelphia and collaborates with the Math Forum at Drexel.

• Bereiter, C. (2002). Education and mind in the knowledge age. Hillsdale, NJ: Lawrence Erlbaum Associates.
• Berkowitz, M., & Gibbs, J. (1983). Measuring the developmental features of moral discussion. Merrill-Palmer Quarterly, 29, 399-410.
• Cakir, M. P., Zemel, A., & Stahl, G. (2009). The joint organization of interaction within a multimodal CSCL medium. International Journal of Computer-Supported Collaborative Learning, 4(2), 115-149. Available at http://dx.doi.org/10.1007/s11412-009-9061-0∞.
• Gorman, J., Cooke, N., Amazeen, P., Hessler, E., & Rowe, L. (2009). Automatic tagging of macrocognitive collaborative processes through communication analyses, final technical report: ONR Grant N000140510625.
• Gunawardena, C. N., Lowe, C. A., & Anderson, T. (1997). Analyisis of global online debate and the development of an interaction analysis model for examining social construction of knowledge in computer conferencing. Journal of Educational Computing Research, 17(4).
• Hakkarainen, K. (2009). A knowledge-practice perspective on technology-mediated learning. International Journal of Computer-Supported Collaborative Learning, 4(2), 213-231. Available at http://dx.doi.org/10.1007/s11412-009-9064-x∞.
• Joshi, M., & Rosé, C. P. (2007). Using transactivity in conversation summarization of educational dialogue. Paper presented at the SLaTE Workshp on Speech and Language Technology in Education.
• Letsky, M. P., Warner, N. W., Fiore, S. M., & Smith, C. A. P. (Eds.). (2009). Macrocognition in teams: Theories and methodologies. Burlington, VT: Ashgate Publishing Company.
• Resnick, L., Levine, J., & Teasley, S. (Eds.). (1991). Perspectives on socially shared cognition. Washington, DC: American Psychological Association.
• Robbins, P., & Aydede, M. (Eds.). (2009). The Cambridge handbook of situated cognition. Cambridge, UK: Cambridge University Press.
• Salomon, G. (1993). Distributed cognitions: Psychological and educational considerations. Cambridge, UK: Cambridge University Press.
• Scardamalia, M., & Bereiter, C. (1996). Computer support for knowledge-building communities. In T. Koschmann (Ed.), CSCL: Theory and practice of an emerging paradigm (pp. 249-268). Hillsdale, NJ: Lawrence Erlbaum Associates.
• Stahl, G. (2006). Group cognition: Computer support for building collaborative knowledge. Cambridge, MA: MIT Press. Available at http://GerryStahl.net/mit/∞.
• Stahl, G. (2009). Studying virtual math teams. New York, NY: Springer. Available at http://GerryStahl.net/vmt/book∞.
• Strijbos, J. W., & Stahl, G. (2007). Methodological issues in developing a multi-dimensional coding procedure for small group chat communication. Learning & Instruction. Special issue on measurement challenges in collaborative learning research, 17(4), 394-404. Available at http://GerryStahl.net/vmtwiki/jw.pdf∞.
• van Aalst, J. (2009). Distinguishing knowledge-sharing, knowledge-construction, and knowledge-creation discourses. International Journal of Computer-Supported Collaborative Learning, 4(3), 259-287. Available at http://dx.doi.org/10.1007/s11412-009-9069-5∞.
• Warner, N., Letsky, M., & Cowen, M. (2005). Cognitive model of team collaboration: Macrocognitive focus. Paper presented at the 49th Annual Meeting of the Human Factors and Ergonomic Society, Santa Monica, CA: Human Factors and Ergonomics Society.
• Wegerif, R. (2007). Dialogic, education and technology: Expanding the space of learning. New York, NY: Kluwer-Springer.
• Wegner, D. (1986). Transactive memory: A contemporary analysis of the group mind. In B. Mullen & G. R. Goethals (Eds.), Theories of group behavior (pp. 185-208). New York, NY: Springer Verlag.
• Weinberger, A., & Fischer, F. (2006). A framework to analyze argumentative knowledge construction in computer-supported collaborative learning. Computers & Education.

A Workshop at ICLS 2010 all day Monday, June 28, 2010, Chicago

In order to provide a helpful focus for the workshop, we will consider the applicability of various coding schemes to a dataset from the VMT Project (Stahl, 2009). This data involves three students working in an online environment that integrates text chat with a shared whiteboard. This is a synchronous, text-based interaction. The utterances are typically very brief. In addition to chat postings, the students engage in drawing diagrams. The chat and drawing are often tightly integrated. The students are engaged in exploring a mathematical world, or set of problems that they and others propose. They discuss how 2-D and 3-D patterns of lines grow from stage to stage. They do this in textual narrative, graphical drawings and symbolic expressions (Cakir, Zemel & Stahl, 2009). This poses a challenging dataset for a coding scheme to classify in a way that will be useful for assessing both knowledge sharing and knowledge building by the student group.
Carolyn Rosé is an Assistant Professor in the school of Computer Science at Carnegie Mellon University, with a joint appointment between the Language Technologies Institute and the Human-Computer Interaction Institute. She serves as an Executive Committee member of the Pittsburgh Science of Learning Center and co-thrust leader of its Social and Communicative Factors in Learning thrust. Her research team has worked on analyses of collaborative learning interactions in both text and speech, pairs and small groups, middle school through college aged, in the US and abroad, in a variety of subjects such as math, science, engineering and psychology. Her team has produced tools for supporting automatic collaborative learning process analyses, including Taghelper tools, which has a user base of over 1,200 users from 69 countries.

• Cakir, M. P., Zemel, A., & Stahl, G. (2009). The joint organization of interaction within a multimodal CSCL medium. International Journal of Computer-Supported Collaborative Learning, 4(2), 115-149. Available at http://dx.doi.org/10.1007/s11412-009-9061-0∞.

A Workshop at ICLS 2010 all day June 30, 2010, Chicago

Organized by:

Target Discipline: Mathematics

References

Express your interest

Please email the organizers (email addresses listed above)

• To volunteer to present a coding scheme at the workshop (by March 15)
• To express interest in attending the workshop
• To ask a question about the workshop

Proposal for a workshop at ICLS 2010

Analyzing Knowledge Sharing and Knowledge Co-Construction

Gerry Stahl, Drexel University, Gerry.Stahl@drexel.edu
Carolyn P. Rosé, Carnegie Mellon University, cprose@cs.cmu.edu
Sean Goggins, Drexel University, sgoggins@drexel.edu

Overview

Group Knowledge Building

An important contemporary theory of learning within the learning sciences and CSCL is the theory of knowledge building (Bereiter, 2002; Scardamalia & Bereiter, 1996). This theory notes that knowledge in the disciplines is typically constructed by ideas being made public, becoming successively refined and resulting in knowledge artifacts (technical terms, theories, documents, tools). The knowledge-building theory then suggests that students could effectively learn about a discipline by similarly engaging in group knowledge-building efforts. The processes by which knowledge is shared, transformed, integrated, and even co-constructed through conversational interactions has been a fascination of the Learning Sciences (LS), Computer-Supported Collaborative Learning (CSCL) and Collaborative Knowledge Interoperability (CKI) communities for at least the past decade (Letsky et al., 2009; Resnick, Levine & Teasley, 1991; Stahl, 2006).
Many classrooms and on-line learning environments have attempted to promote student learning through instituting collaborative knowledge building. A variety of techniques have been developed and used in recent years, often including computer support. Within the learning sciences, one common approach is to classify the utterances of students involved in knowledge-building activities according to a coding scheme and to use the results of this classification to map out and understand the process. In order to design appropriate dynamic support that is capable of operating effectively in real time, we must understand the collaborative discussion processes well enough to formalize them in terms of categories that can be automatically identified in units of conversational data. For instance, we must be able to see if different participants are contributing to the shared knowledge (Resnick et al., 1991; Robbins & Aydede, 2009; Salomon, 1993) from existing stores of disciplinary expertise or if multiple participants are co-constructing knowledge that is new to all of them through a process like inter-animation of perspectives (Wegerif, 2007), transactional building on each other (Joshi & Rosé, 2007; Wegner, 1986), successive refinement of public knowledge artifacts (Bereiter, 2002), macro-cognition (Letsky et al., 2009) or group cognition (Stahl, 2006).

Coding Schemes

A number of coding schemes have been developed and tested for the purpose of assessing knowledge-sharing and knowledge-building activities, which we will leverage for this workshop among others that may be proposed by workshop participants. Many of these schemes have been developed with specific research questions in mind or corresponding to particular experimental circumstances (media of interaction, knowledge discipline, etc.). These coding schemes each represent a different point along the continuum from knowledge sharing to knowledge co-construction in terms of the associated notion of packaging and status of knowledge. An issue that repeatedly arises in relation to these coding schemes is the feasibility of automating the analysis of data in terms of the scheme.
A sample of published schemes follows:

• • Berkowitz & Gibbs schema (Berkowitz & Gibbs, 1983)
• • Nancy Cooke schema (Gorman et al., 2009)
• • Gunawardena schema (Gunawardena, Lowe & Anderson, 1997)
• • Kai Hakkarainen schema: (Hakkarainen, 2009)
• • J-W Strijbos schema (Strijbos & Stahl, 2007)
• • Jan von Aalst schema (van Aalst, 2009)
• • Norm Warner schema (Warner, Letsky & Cowen, 2005)
• • Weinberger & Fischer schema (Weinberger & Fischer, 2006)

• Target Discipline: Mathematics

In order to provide a helpful focus for the workshop, we will consider the applicability of various coding schemes to a dataset from the VMT Project (Stahl, 2009). This data involves three students working in an online environment that integrates text chat with a shared whiteboard. This is a synchronous, text-based interaction. The utterances are typically very brief. In addition to chat postings, the students engage in drawing diagrams. The chat and drawing are often tightly integrated. The students are engaged in exploring a mathematical world, or set of problems that they and others propose. They discuss how 2-D and 3-D patterns of lines grow from stage to stage. They do this in textual narrative, graphical drawings and symbolic expressions (Çakır, Zemel & Stahl, 2009). This poses a challenging dataset for a coding scheme to classify in a way that will be useful for assessing both knowledge sharing and knowledge building by the student group.

Antecedents of Knowledge-Building Coding

Coding schemes to explicate the degree of knowledge building in virtual environments may be aided by analysis of interaction patterns that focus on the frequency, lag, size and dimensionality of interactions between members in a rich environment like VMT. To seed thinking about antecedents to knowledge building the workshop organizers will perform network analysis on the 3,000 chat postings and 3,000 other actions in the VMT data set identify statistical patterns or cycles of interaction. The resulting networks will be bipartite (users and objects) and regular. Since the networks in this corpus are closed and small, this pre-workshop analysis will focus on small network evolution and elaborating semantically meaningful measures of tie strength between members. Evolution means developing a time-series set of network views, and possibly addressing the state of the network as a feature that contributes to the other forms of analysis. Through the efforts of this workshop, we may also derive measures of tie strength from the results of the knowledge-building coding schemes.

Workshop Goals and Activities

The goal of the workshop is to advance the state-of-the-art of coding group knowledge building in disciplines. In particular, the workshop will aim to define a coding scheme, a set of coding schemes or criteria for a coding scheme that is well suited for formalizing and tracking group knowledge-building processes in a setting like the VMT case study of text chat and shared whiteboard for math exploration by a small group of students.
Participants who want to present a coding scheme will be asked to write a two- to four-page position paper describing their present work developing coding schemes for group knowledge building in disciplines. Participants not intending to present their own scheme in the workshop will be asked to provide a one-page statement of research interest. These materials will be shared with all participants prior to the workshop. Participants will also have access to the VMT data set prior to the workshop, and will be encouraged to consider specific coding strategies in the context of this specific data set.

Intended Audience

The workshop will bring together researchers from the LS, CSCL and CKI communities; the workshop proposers are active participants in all of these and will personally solicit relevant participants. The workshop will include researchers who will present and explain coding schemes that they have developed and/or applied in their research. The authors of the schemes listed above will be personally invited to attend and present. In addition, researchers and graduate students interested in tools for the assessment of knowledge building will be welcome.

Organizers

Gerry Stahl has investigated issues and theories related to how small groups build knowledge together (Stahl, 2006). He has directed the Virtual Math Teams Project (Stahl, 2009), which studies the interactions within online groups of students exploring math problems. He is Executive Editor of the International Journal of Computer-Supported Collaborative Learning. He teaches at the College of Information Science and Technology at Drexel University in Philadelphia and collaborates with the Math Forum at Drexel.

Carolyn Rosé is an Assistant Professor in the school of Computer Science at Carnegie Mellon University, with a joint appointment between the Language Technologies Institute and the Human-Computer Interaction Institute. She serves as an Executive Committee member of the Pittsburgh Science of Learning Center and co-thrust leader of its Social and Communicative Factors in Learning thrust. Her research team has worked on analyses of collaborative learning interactions in both text and speech, pairs and small groups, middle school through college aged, in the US and abroad, in a variety of subjects such as math, science, engineering and psychology. Her team has produced tools for supporting automatic collaborative learning process analyses, including TagHelper tools, which has a user base of over 1,200 users from 69 countries.

Sean Goggins is an Assistant Professor in the College of Information Science and Technology at Drexel University in Philadelphia and is a member of the Group Cognition Lab, headed by Gerry Stahl. Sean has researched: synchronous and asynchronous learning teams, using content analysis to measure knowledge co-construction; theories of human information behavior, to analyze learner interactions with information; and social network analysis built from log data, to explicate the relationship between group development and knowledge co-construction in online groups. Sean’s work at the Group Cognition Lab explores the development of automated methods for early identification of interaction patterns, and information uses that correspond with higher levels of knowledge co-construction.
References

• Bereiter, C. (2002). Education and mind in the knowledge age. Hillsdale, NJ: Lawrence Erlbaum Associates.
• Berkowitz, M., & Gibbs, J. (1983). Measuring the developmental features of moral discussion. Merrill-Palmer Quarterly, 29, 399-410.
• Çakır, M. P., Zemel, A., & Stahl, G. (2009). The joint organization of interaction within a multimodal CSCL medium. International Journal of Computer-Supported Collaborative Learning, 4(2), 115-149. Available at http://dx.doi.org/10.1007/s11412-009-9061-0∞.
• Gorman, J., Cooke, N., Amazeen, P., Hessler, E., & Rowe, L. (2009). Automatic tagging of macrocognitive collaborative processes through communication analyses, final technical report: ONR Grant N000140510625.
• Gunawardena, C. N., Lowe, C. A., & Anderson, T. (1997). Analyisis of global online debate and the development of an interaction analysis model for examining social construction of knowledge in computer conferencing. Journal of Educational Computing Research, 17(4).
• Hakkarainen, K. (2009). A knowledge-practice perspective on technology-mediated learning. International Journal of Computer-Supported Collaborative Learning, 4(2), 213-231. Available at http://dx.doi.org/10.1007/s11412-009-9064-x∞.
• Joshi, M., & Rosé, C. P. (2007). Using transactivity in conversation summarization of educational dialogue. Paper presented at the SLaTE Workshp on Speech and Language Technology in Education.
• Letsky, M. P., Warner, N. W., Fiore, S. M., & Smith, C. A. P. (Eds.). (2009). Macrocognition in teams: Theories and methodologies. Burlington, VT: Ashgate Publishing Company.
• Resnick, L., Levine, J., & Teasley, S. (Eds.). (1991). Perspectives on socially shared cognition. Washington, DC: American Psychological Association.
• Robbins, P., & Aydede, M. (Eds.). (2009). The Cambridge handbook of situated cognition. Cambridge, UK: Cambridge University Press.
• Salomon, G. (1993). Distributed cognitions: Psychological and educational considerations. Cambridge, UK: Cambridge University Press.
• Scardamalia, M., & Bereiter, C. (1996). Computer support for knowledge-building communities. In T. Koschmann (Ed.), CSCL: Theory and practice of an emerging paradigm (pp. 249-268). Hillsdale, NJ: Lawrence Erlbaum Associates.
• Stahl, G. (2006). Group cognition: Computer support for building collaborative knowledge. Cambridge, MA: MIT Press. Available at http://GerryStahl.net/mit/∞.
• Stahl, G. (2009). Studying virtual math teams. New York, NY: Springer. Available at http://GerryStahl.net/vmt/book∞.
• Strijbos, J. W., & Stahl, G. (2007). Methodological issues in developing a multi-dimensional coding procedure for small group chat communication. Learning & Instruction. Special issue on measurement challenges in collaborative learning research, 17(4), 394-404. Available at http://GerryStahl.net/vmtwiki/jw.pdf∞.
• van Aalst, J. (2009). Distinguishing knowledge-sharing, knowledge-construction, and knowledge-creation discourses. International Journal of Computer-Supported Collaborative Learning, 4(3), 259-287. Available at http://dx.doi.org/10.1007/s11412-009-9069-5∞.
• Warner, N., Letsky, M., & Cowen, M. (2005). Cognitive model of team collaboration: Macrocognitive focus. Paper presented at the 49th Annual Meeting of the Human Factors and Ergonomic Society, Santa Monica, CA: Human Factors and Ergonomics Society.
• Wegerif, R. (2007). Dialogic, education and technology: Expanding the space of learning. New York, NY: Kluwer-Springer.
• Wegner, D. (1986). Transactive memory: A contemporary analysis of the group mind. In B. Mullen & G. R. Goethals (Eds.), Theories of group behavior (pp. 185-208). New York, NY: Springer Verlag.
• Weinberger, A., & Fischer, F. (2006). A framework to analyze argumentative knowledge construction in computer-supported collaborative learning. Computers & Education.

Organized by:

Gerry Stahl, Drexel University, Gerry.Stahl@drexel.edu
Carolyn Penrose Rosé, Carnegie Mellon University, cprose@cs.cmu.edu
Sean Goggins, Drexel University, sgoggins@drexel.edu

This daylong workshop brings together researchers from different sub-communities of the learning sciences, who have developed and applied coding schemes that can be used to identify and classify collaborative moves in small group discussions within or across disciplines. These sub-communities represent a spectrum of perspectives related to the packaging and status of knowledge within that process. At one end of the spectrum, researchers have assumed the knowledge is static but distributed among experts by discipline, and knowledge units are revealed and then organized within the conversations combining different expertise (producing shared knowledge). At the other end of the spectrum, researchers have assumed that knowledge is dynamic, and that knowledge itself is repackaged, transformed, constructed or emergent within the conversation (producing co-constructed knowledge). The workshop will present, compare and analyze a selection of coding schemes designed to capture these different perspectives on shared and co-constructed knowledge building. It will apply them to a common corpus from the discipline of mathematics in order to facilitate a productive exchange among research sub-communities.