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Artifact Analysis

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International Handbook of Computer-Supported Collaborative Learning

Part of the book series: Computer-Supported Collaborative Learning Series ((CULS,volume 19))

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Abstract

Artifacts are constructed as a result of human activity. They are the tools for further activities and the basis for communication and collaboration. Within any given learning context, artifacts may be produced and can serve as a basis for assessment as well as resources for subsequent activity, being semiotic mediators. Learning scientists analyze artifacts as a method of evaluating their own interventions and to informing their understanding of learning processes. This chapter provides a short review of relevant theoretical perspectives and prior research and describes different forms of language and text artifact analysis that are presently applied within the learning sciences. These include dialog analysis; conversation analysis; content analysis of verbal, textual, and other forms of data; social network analysis; and polyphonic analysis. Applications to the analysis of online discussions and classroom discourse are discussed, as well as future directions for research.

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References

  • Bakhtin, M. M. (1984). Problems of Dostoevsky’s poetics (C. Emerson, Ed.; C. Emerson, Trans.). Minneapolis: University of Minnesota Press.

    Google Scholar 

  • Bakhtin, M. M. (1986). Speech genres and other late essays. Austin: University of Texas Press.

    Google Scholar 

  • Bereiter, C. (2002). Education and mind in the knowledge age. Mahwah, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3(4–5), 993–1022.

    Google Scholar 

  • Budanitsky, A., & Hirst, G. (2006). Evaluating WordNet-based measures of lexical semantic relatedness. Computational Linguistics, 32(1), 13–47.

    Article  Google Scholar 

  • Çakir, M., Xhafa, F., & Zhou, N. (2009). Thread-based analysis of patterns in VMT. In G. Stahl (Ed.), Studying virtual math teams (pp. 359–371). New York, NY: Springer.

    Google Scholar 

  • Carrington, P., Scott, J., & Wasserman, S. (2005). Models and methods in social network analysis (structural analysis in the social sciences). Cambridge: Cambridge University Press.

    Google Scholar 

  • Chi, M. T. H. (1997). Quantifying qualitative analyses of verbal data: A practical guide. The Journal of the Learning Sciences, 6(3), 271–315.

    Article  Google Scholar 

  • Chiu, M. M. (2013). Social metacognition, micro-creativity and justifications: Statistical discourse analysis of a mathematics classroom conversation. In D. D. Suthers, K. Lund, C. Penstein Rosé, C. Teplovs, & N. Law (Eds.), Productive multivocality in the analysis of collaborative learning (pp. 141–160). Springer.

    Google Scholar 

  • Csikszentmihalyi, M. (1996). Creativity: Flow and the psychology of discovery and invention. New York: Harper Collins.

    Google Scholar 

  • Dascalu, M., Trausan-Matu, S., McNamara, D. S., & Dessus, P. (2015). ReaderBench—Automated evaluation of collaboration based on cohesion and dialogism. International Journal of Computer-Supported Collaborative Learning, 10(4), 395–423.

    Article  Google Scholar 

  • Saussure, F. de (1983). Course in general linguistics (C. Bally & A. Sechehaye, Eds.; R. Harris, Trans.). Open Court. (Original work published 1916).

    Google Scholar 

  • Dong, A. (2005). The latent semantic approach to studying design team communication. Design Studies, 26(5), 445–461.

    Article  Google Scholar 

  • Ericsson, K. A., & Simon, H. A. (1984). Protocol analysis: Verbal reports as data. Cambridge, MA: MIT Press.

    Google Scholar 

  • Garfinkel, H. (1967). Studies in ethnomethodology. Englewood Cliffs, NJ: Prentice-Hall.

    Google Scholar 

  • Halatchliyski, I., Moskaliuk, J., Kimmerle, J., & Cress, U. (2014). Explaining authors’ contribution to pivotal artifacts during mass collaboration in the Wikipedia’s knowledge base. International Journal of Computer-Supported Collaborative Learning, 9(1), 97–115.

    Article  Google Scholar 

  • Halliday, M. A. K., & Hasan, R. (1976). Cohesion in English. London: Longman.

    Google Scholar 

  • Holmer, T., Kienle, A., & Wessner, M. (2006). Explicit referencing in learning chats: Needs and acceptance. In W. Nejdl & K. Tochtermann (Eds.), Innovative approaches for learning and knowledge sharing, first European conference on technology enhanced learning, EC-TEL 2006 (pp. 170–184). Springer.

    Google Scholar 

  • Holtz, P., Kimmerle, J., & Cress, U. (2018). Using big data techniques for measuring productive friction in mass collaboration online environments. International Journal of Computer-Supported Collaborative Learning, 13, 439–456.

    Article  Google Scholar 

  • Jefferson, G. (1984). Transcription notation. In J. Atkinson & J. Heritage (Eds.), Structures of social interaction. Cambridge: Cambridge University Press.

    Google Scholar 

  • Jurafsky, D., & Martin, J. H. (2009). An introduction to natural language processing. Computational linguistics, and speech recognition (2nd ed.). Upper Saddle River, NJ: Pearson Prentice Hall.

    Google Scholar 

  • Koschmann, T. (1999). Toward a dialogic theory of learning: Bakhtin’s contribution to learning in settings of collaboration. In Proceedings of the 1999 conference on computer support for collaborative learning (pp. 308–313). International Society of the Learning Sciences.

    Google Scholar 

  • Landauer, T. K., & Dumais, S. T. (1997). A solution to Plato’s problem: The latent semantic analysis theory of acquisition, induction and representation of knowledge. Psychological Review, 104(2), 211–240.

    Article  Google Scholar 

  • Ludvigsen, S., Stahl, G., Law, N., & Cress, U. (2015). Collaboration and the formation of new knowledge artifacts. International Journal of Computer-Supported Collaborative Learning, 10(1), 1–6.

    Article  Google Scholar 

  • Mariotti, M. A. (2009). Artifacts and signs after a Vygotskian perspective: The role of the teacher. ZDM Mathematics Education, 41, 427–440.

    Article  Google Scholar 

  • Miller, G. A. (1995). WordNet: A lexical database for English. Communications of the ACM, 38(11), 39–41.

    Article  Google Scholar 

  • Peirce, C. S. (1998). Pragmatism. In Peirce Edition Project (Ed.), The essential Peirce: Selected philosophical writings (Vol. 2, p. 411). Indiana University Press.

    Google Scholar 

  • Rosé, C. P., Wang, Y. C., Cui, Y., Arpguello, J., Stegmann, K., Weinberger, A., & Fischer, F. (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.

    Article  Google Scholar 

  • Sacks, H. (1992). Lectures on conversation. Oxford: Blackwell.

    Google Scholar 

  • Sacks, H., Schegloff, E. A., & Jefferson, G. (1974). A simplest systematics for the organization of turn taking for conversation. Language, 50(4), 696–735.

    Article  Google Scholar 

  • Sawyer, R. K. (2006). Group creativity: Musical performance and collaboration. Psychology of Music, 34(2), 148–165.

    Article  Google Scholar 

  • Schneider, B., Worsley, M., & Martinez-Maldonado, R. (this volume). Gesture and gaze: Multimodal data in dyadic interactions. In U. Cress, C. Rosé, A. F. Wise, & J. Oshima (Eds.), International handbook of computer-supported collaborative learning. Cham: Springer.

    Google Scholar 

  • Simpson, A., Bannister, N., & Matthews, G. (2017). Cracking her codes: Understanding shared technology resources as positioning artifacts for power and status in CSCL environments. International Journal of Computer Supported Collaborative Learning, 12(3), 221–249.

    Article  Google Scholar 

  • Slotta, J. D., Chi, M. T. H., & Joram, E. (1995). Assessing Students’ misclassifications of physics concepts: An ontological basis for conceptual change. Cognition and Instruction, 13(3), 373–400.

    Article  Google Scholar 

  • Slotta, J. D., Tissenbaum, M., & Lui, M. (2013). Orchestrating of complex inquiry: Three roles for learning analytics in a smart classroom infrastructure. In Proceedings of the third international conference on learning analytics and knowledge (pp. 270–274). ACM.

    Google Scholar 

  • Stahl, G. (2006). Group cognition: Computer support for building collaborative knowledge. Cambridge, MA: MIT Press.

    Google Scholar 

  • Stahl, G. (2013). Learning across levels. International Journal of Computer-Supported Collaborative Learning, 8(1), 1–12.

    Article  Google Scholar 

  • Stahl, G., Ludvigsen, S., Law, N., & Cress, U. (2014). CSCL artifacts. International Journal of Computer-Supported Collaborative Learning, 9(3), 237–245.

    Article  Google Scholar 

  • Suthers, D., & Desiato, C. (2012). Exposing chat features through analysis of uptake between contributions. In Proceedings of the 45th Hawaii international conference on system sciences (pp. 3368–3377). IEEE Computer Society.

    Google Scholar 

  • Suthers, D., & Rosen, D. (2011). A unified framework for multi-level analysis of distributed learning. In Proceedings of the first international conference on learning analytics and knowledge (pp. 64–74). Association for Computing Machinery.

    Google Scholar 

  • Tannen, D. (2007). Talking voices: Repetition, dialogue, and imagery in conversational discourse (2nd ed.). Cambridge: Cambridge University Press.

    Google Scholar 

  • Trausan-Matu, S. (2010). The polyphonic model of hybrid and collaborative learning. In F. L. Wang, J. Fong, & R. Kwan (Eds.), Handbook of research on hybrid learning models: Advanced tools, technologies, and applications (pp. 466–486). Hershey, NY: Information Science Publishing.

    Google Scholar 

  • Trausan-Matu, S. (2013). Collaborative and differential utterances, pivotal moments, and polyphony. In D. D. Suthers, K. Lund, C. Penstein Rosé, C. Teplovs, & N. Law (Eds.), Productive multivocality in the analysis of collaborative learning (pp. 123–139). New York: Springer.

    Google Scholar 

  • Trausan-Matu, S., Dascalu, M., & Rebedea, T. (2014). PolyCAFe—Automatic support for the polyphonic analysis of CSCL chats. International Journal of Computer-Supported Collaborative Learning, 9(2), 127–156.

    Article  Google Scholar 

  • Whitney, W. D. (1879). A Sanskrit grammar, including both the classical language, and the older dialects, of Veda and Brahmana. Retrieved October 14, 2018, from Internet Archive website https://archive.org/details/1941sanskritgram00whituoft/page/n0

  • Zahn, C., Ruf, A., & Goldman, R. (this volume). Video data collection and video analyses in CSCL research. In U. Cress, C. Rosé, A. F. Wise, & J. Oshima (Eds.), International handbook of computer-supported collaborative learning. Cham: Springer.

    Google Scholar 

  • Zemel, A., Xhafa, F., & Çakir, M. (2009). Combining coding and conversation analysis of VMT chats. In G. Stahl (Ed.), Studying virtual math teams (pp. 421–450). New York, NY: Springer.

    Google Scholar 

Further Readings

  • Dascalu, M., McNamara, D. S., Trausan-Matu, S., & Allen, L. K. (2018). Cohesion network analysis of CSCL participation. Behavior Research Methods, 50(2), 604–619. The paper presents an empirical validation of the automated analysis of CSCL conversations based on the cohesion network, implemented in the opensource ReaderBench system (http://readerbench.com/), which integrates content analysis, social network analysis and the polyphonic analysis. The theoretical aspects which stay at the basis of ReaderBench are also presented. The validation was done with ten chat conversations, in which three to eight students debated the advantages and disadvantages of CSCL technologies. Human annotators scored each participant’s contributions regarding if they covered the central concepts of the conversation. Social network analysis was applied to compute metrics from the cohesive network analysis sociogram, in order to assess the degree of participation of each student. The results showed that there was a strong correlation between the computed values and the human evaluations of the conversations. Moreover, the computed indices collectively predicted 54% of the variance in the human ratings of participation, after a stepwise regression analysis.

    Article  Google Scholar 

  • Holtz, P., Kimmerle, J., & Cress, U. (2018). Using big data techniques for measuring productive friction in mass collaboration online environments. International Journal of Computer-Supported Collaborative Learning, 13, 439–456. The main idea of the paper is that knowledge building is triggered by productive friction, both at individual and social levels of learning processes. The paper analyses how productive friction is involved in Wikipedia. Three approaches are analyzed: automatic classification of text, social network analysis, and cluster analysis, considering also an artifact-mediated collaboration perspective.

    Article  Google Scholar 

  • Stahl, G. (2009). Studying virtual math teams. New York, NY: Springer. This book contains a comprehensive presentation of the theoretical aspects of group cognition in CSCL conversations and several approaches of analysis: conversation analysis, code and count, statistical analysis, content analysis, and polyphonic analysis. The theoretical framework and the examples of the various analysis methods were developed in the Virtual Math Teams NSF project (http://gerrystahl.net/vmt/) from Drexel University, Philadelphia, PA, which had as subject the analysis of CSCL instant messenger (chat) sessions of students solving mathematical problems proposed as “Problems of the week” at mathforum.org. A website of the book may be accessed at http://gerrystahl.net/elibrary/svmt/.

  • Suthers, D., & Rosen, D. (2011). A unified framework for multi-level analysis of distributed learning. In Proceedings of the first international conference on learning analytics and knowledge (pp. 64–74). Association for Computing Machinery. A series of graphs and other artifacts that can be used for learning analytics are presented: process traces, contingency graphs, uptake graphs, sociograms, asociograms, and entity–relationships graphs.

    Google Scholar 

  • Trausan-Matu, S. (2012) Repetition as Artifact Generation in Polyphonic CSCL Chats. In Proceedings of the third international conference on emerging intelligent data and web technologies (pp. 194–198). IEEE Computer Society. The paper analyses CSCL chats performed by K–12 students, which are solving together mathematics problems in the context of the Virtual Math Teams NSF project (http://gerrystahl.net/vmt/). The analysis is considering the polyphonic model of discourse inspired from the musical analogy, with emphasis on repetition and rhythm. Four cases are analyzed, in which the repetition of words, phrases, notation, and numbers transforms them in artifacts that drove to solving the problems.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, University Politehnica of Bucharest, Bucharest, Romania

    Stefan Trausan-Matu

  2. Ontario Institute for Studies in Education, University of Toronto, Toronto, Canada

    James D. Slotta

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  1. Stefan Trausan-Matu
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  2. James D. Slotta
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Correspondence to Stefan Trausan-Matu .

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Editors and Affiliations

  1. Leibniz-Institut für Wissensmedien, Tübingen, Germany

    Ulrike Cress

  2. Language Technologies Institute and Human-Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, PA, USA

    Carolyn Rosé

  3. Department of Administration Leadership, and Technology, New York University, New York, NY, USA

    Alyssa Friend Wise

  4. Faculty of Informatics, Shizuoka University, Hamamatsu-shi, Shizuoka, Japan

    Jun Oshima

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Trausan-Matu, S., Slotta, J.D. (2021). Artifact Analysis. In: Cress, U., Rosé, C., Wise, A.F., Oshima, J. (eds) International Handbook of Computer-Supported Collaborative Learning. Computer-Supported Collaborative Learning Series, vol 19. Springer, Cham. https://doi.org/10.1007/978-3-030-65291-3_30

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  • DOI: https://doi.org/10.1007/978-3-030-65291-3_30

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