Abstract
This paper contributes to a theory-grounded methodological foundation for automatic collaborative learning process analysis. It does this by illustrating how insights from the social psychology and sociolinguistics of speech style provide a theoretical framework to inform the design of a computational model. The purpose of that model is to detect prevalence of an important group knowledge integration process in raw speech data. Specifically, this paper focuses on assessment of transactivity in dyadic discussions, where a transactive contribution is operationalized as one where reasoning is made explicit, and where that reasoning builds on a prior reasoning statement within the discussion. Transactive contributions can be either self-oriented, where the contribution builds on the speaker’s own prior contribution, or other-oriented, where the contribution builds on a prior contribution of a conversational partner. Other-oriented transacts are particularly central to group knowledge integration processes. An unsupervised Dynamic Bayesian Network model motivated by concepts from Speech Accommodation Theory is presented and then evaluated on the task of estimating prevalence of other-oriented transacts in dyadic discussions. The evaluation demonstrates a significant positive correlation between an automatic measure of speech style accommodation and prevalence of other-oriented transacts (R = .36, p < .05).
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References
Ai, H., Kumar, R., Nguyen, D., Nagasunder, A., Rosé, C.P. (2010). Exploring the effectiveness of social capabilities and goal alignment in computer-supported collaborative learning. In Proc. ITS 2010. LNCS (vol. 6095, pp. 134–143). Heidelberg: Springer.
Ang, J., Dhillon, R., Krupski, A., Shriberg, E., Stolcke, A. (2002). Prosody-based automatic detection of annoyance and frustration in human-computer dialog. In Proc. International Conference on Spoken Language Processing (ICSLP 2002) (pp. 2037–2039). Denver, CO.
Arnold, A., Nallapati, R., Cohen, W. (2008). Exploiting feature hierarchy for transfer learning in named entity recognition. In Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (pp. 245–253).
Azmitia, M., & Montgomery, R. (1993). Friendship, transactive dialogues, and the development of scientific reasoning. Social Development, 2, 202–221.
Berkowitz, M., & Gibbs, J. (1979). Unpublished manual for coding transactivity.
Berkowitz, M., & Gibbs, J. (1983). Measuring the developmental features of moral discussion. Merrill-Palmer Quarterly, 29, 399–410.
Bilous, F., & Krauss, R. (1988). Dominance and accommodation in the conversational behaviours of same-and mixed-gender dyads. Language and Communication, 8(3), 183–194.
Bourhis, R., & Giles, H. (1977). The language of intergroup distinctiveness. In H. Giles (Ed.), Language, ethnicity and intergroup relations (pp. 119–135). London: Academic.
Chaudhuri, S., Kumar, R., Joshi, M., Terrell, E., Higgs, F., Aleven, V., et al. (2008). It’s not easy being green: Supporting collaborative “green design” learning. In Proceedings of Intelligent Tutoring Systems, Lecture Notes in Computer Science Volume 5091 (pp. 807–809).
Coupland, N. (2007). Style: Language variation and identity. Cambridge: Cambridge University Press.
Daumé, H. III. (2007). Frustratingly easy domain adaptation. In Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics (pp. 256–263).
de Lisi, R., & Golbeck, S.L. (1999). Implications of the Piagetian Theory for peer learning. In Cognitive perspectives on peer learning (pp. 3–37).
Eckert, P., & Rickford, J. (2001). Style and sociolinguistic variation. Cambridge Univ Press.
Edlund, J., Heldner, M., Hirschberg, J. (2009). Pause and gap length in face-to-face interaction. In Proceedings of Interspeech (pp. 2779–2782).
Erkens, G., & Janssen, J. (2008). Automatic coding of dialogue acts in collaboration protocols. International Journal of Computer-Supported Collaborative Learning (3), 447–470.
Fina, A., Schiffrin, D., Bamberg, M. (2006). Discourse and identity, Cambridge University Press.
Finkel, J., & Manning, C. (2009). Hierarchical Bayesian domain adaptation. In Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics (pp. 602–610).
Garrod, S., & Pickering, M. J. (2004). Why is conversation so easy? Trends in Cognitive Sciences, 8, 8–11.
Giles, H. (1984). The dynamics of speech accomodation. Amsterdam: Mouton.
Giles, H., & Coupland, N. (1991). Language: Contexts and consequences. Thomson Brooks/Cole Publishing Co.
Giles, H., Mulac, A., Bradac, J., & Johnson, P. (1987). Speech accommodation theory: The next decade and beyond. Communication Yearbook, 10, 13–48.
Gweon, G., Jeon, S., Lee, J., Rosé, C.P. (2011a). Diagnosing problems in student project groups. In S. Puntambekar, G. Erkens, C. Hmelo-Silver (Eds.) Analyzing collaborative interactions in CSCL: Methods, approaches and issues (pp. 293–318). Springer.
Gweon, G., Agarwal, P., Udani, M., Raj, B., Rosé, C.P. (2011b). The automatic assessment of knowledge integration processes in project teams. In Proceedings of Computer Supported Collaborative Learning, Hong Kong, China (pp. 462–469).
Hecht, M., Boster, F., & LaMer, S. (1989). The effect of extroversion and differentiation on listener adapted communication. Communication Reports, 2(1), 1–8.
Jaffe, A. (2009). Stance: Sociolinguistic perspectives. Oxford University Press.
Jain, M., McDonogh, J., Gweon, G., Raj, B., Rosé, C.P. (2012). An unsupervised Dynamic Bayesian Network approach to measuring speech style accommodation. In EACL 2012 Proceedings of the 13th Conference of the European Association for Computational Linguistics, Avingon, France, April 23–27, 2012 (pp. 787–797).
Jensen, F.V. (1996). An introduction to Bayesian networks. UCL Press.
Joshi, M., Dredze, M., Cohen, W., Rosé, C.P. (2012). Multi-domain learning: When do domains matter. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL 2012, July 12–14, 2012, Jeju Island, Korea (pp. 1302–1312).
Kanda, T., Shimada, M., Koizumi, S. (2012). Children learning with a social robot. In The Proceedings of the 7th Annual ACM/IEEE International Conference on Human-Robot Interaction, ACM, New York (pp. 351–358).
Kuhn, D. (1991). The skills of argument. New York: Cambridge University Press.
Kumar, R., & Rosé, C. P. (2011). Architecture for building conversational agents that support collaborative learning. IEEE Transactions on Learning, 4(1), 21–34.
Kumar, R., Rosé, C.P., Litman, D. (2006). Identification of confusion and surprise in spoken dialogusing prosodic features. In Proceedings of Interspeech, Pittsburgh, PA.
Kumar, R., Rosé, C.P., Wang, Y.C., Joshi, M., Robinson, A. (2007). Tutorial dialogue as adaptive collaborative learning support. In Proceedings of the 2007 Conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work (pp 383–390).
Labov, W. (2010). Principles of linguistic change: Internal factors, volume 1. Wiley-Blackwell.
Lauritzen, S. L., & Spiegelhalter, D. J. (1988). Local computations with probabilities on graphical structures and their application to expert systems. Journal of the Royal Statistical Society, 50, 157–224.
Levitan, R., Gravano, A., Hirschberg, J. (2011). Entrainment in speech preceding backchannels. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: Short Papers-Volume 2 (pp. 113–117). Association for Computational Linguistics.
Liscombe, J., Venditti, J, Hirschberg, J. (2005). Detecting certainness in spoken tutorial dialogues. In Proceedings Interspeech (pp. 1837–1840).
Loehlin, J. (1998). Latent variable models: An introduction to factor, path, and structural equation analysis. Psychology Press.
McLaren, B., Scheuer, O., De Laat, M., Hever, R., de Groot, R., Rosé, C.P. (2007). Using machine learning techniques to analyze and support mediation of student e-discussions. In Proceedings of Artificial Intelligence in Education (pp. 331–338). IOS Press.
Mu, J., Stegmann, K., Mayfield, E., Rosé, C. P., & Fischer, F. (2012). The ACODEA framework: Developing segmentation and classification schemes for fully automatic analysis of online discussions. International Journal of Computer-Supported Collaborative Learning, 7(2), 285–305.
Nokes, T., Levine, J.M., Belenky, D., Gadgil, S. (2010). Investigating the impact of dialectical interaction on engagement, affect, and robust learning. In Proceedings of the 9th International Conference of the Learning Sciences (pp. 215–218). International Society of the Learning Sciences.
OpenSmile. (2012). Available at http://opensmile.sourceforge.net/.
Pearl, J. (1988). Probabilistic reasoning in intelligent systems: Networks of plausible inference. Morgan Kaufmann.
Purcell, A. (1984). Code shifting hawaiian style: Childrens accommodation along a decreolizing continuum. International Journal of the Sociology of Language, 1984(46), 71–86.
Putman, W., & Street, R., Jr. (1984). The conception and perception of noncontent speech performance: Implications for speech-accommodation theory. International Journal of the Sociology of Language, 46, 97–114.
Ranganath, R., Jurafsky, D., McFarland, D. (2009). It’s not you, it’s me: Detecting flirting and its misperception in speed-dates. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (pp. 334–342). Singapore.
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.
Sanders, R. (1987). Cognitive foundations of calculated speech. State University of New York Press.
Sawyer, R.K. (2005). Social emergence: Societies as complex systems. Cambridge University Press.
Schwartz, D. (1998). The productive agency that drives collaborative learning. In P. Dillenbourg (Ed.) Collaborative learning: Cognitive and computational approaches. Emrald Group Publishing.
Scotton, C. (1985). What the heck, sir: Style shifting and lexical colouring as features of powerful language. In Sequence and pattern in communicative behaviour (pp. 103–119).
Soller, A., & Lesgold, A. (2000). Modeling the process of collaborative learning. In Proceedings of the International Workshop on New Technologies in Collaborative Learning. Japan: Awaiji–Yumebutai.
Suthers, D. (2006). Technology affordances for inter-subjective meaning making: A research agenda for CSCL. International Journal of Computer-Supported Collaborative Learning, 1, 315–337.
Teasley, S. D. (1997). Talking about reasoning: How important is the peer in peer collaborations? In L. B. Resnick, C. Pontecorvo, & R. Saljo (Eds.), Discourse, tools, and reasoning: Situated cognition and technologically supported environments. Heidelberg: Springer.
Toulmin. (1958). The uses of argument. Cambridge: Cambridge University Press.
Weinberger, A., & Fischer, F. (2006). A framework to analyze argumentative knowledge construction in computer supported collaborative learning. Computers in Education, 46, 71–95.
Welkowitz, J., & Feldstein, S. (1970). Relation of experimentally manipulated interpersonal perception and psychological differentiation to the temporal patterning of conversation. In Proceedings of the 78th Annual Convention of the American Psychological Association (volume 5, pp. 387–388).
Acknowledgments
This work was supported in part by NSF grant SBE 0836012 to the Pittsburgh Science of Learning Center. We gratefully acknowledge John Levine and Timothy Nokes from the University of Pittsburgh for sharing their data with us for these experiments.
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Gweon, G., Jain, M., McDonough, J. et al. Measuring prevalence of other-oriented transactive contributions using an automated measure of speech style accommodation. Computer Supported Learning 8, 245–265 (2013). https://doi.org/10.1007/s11412-013-9172-5
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DOI: https://doi.org/10.1007/s11412-013-9172-5