Sharon Oviatt
Kevin Hang
ABSTRACT
Prevailing social norms prohibit interrupting another person when they are speaking. In this research, simultaneous speech was investigated in groups of students as they jointly solved math problems and peer tutored one another. Analyses were based on the Math Data Corpus, which includes ground-truth performance coding and speech transcriptions. Simultaneous speech was elevated 120-143% during the most productive phase of problem solving, compared with matched intervals. It also was elevated 18-37% in students who were domain experts, compared with non-experts. Qualitative analyses revealed that experts differed from non-experts in the function of their interruptions. Analysis of these functional asymmetries produced nine key behaviors that were used to identify the dominant math expert in a group with 95-100% accuracy in three minutes. This research demonstrates that overlapped speech is a marker of group problem-solving progress and domain expertise. It provides valuable information for the emerging field of learning analytics.
- Arthur, A., Lunsford, R., Wesson, M. & Oviatt, S.L. (2006) Prototyping novel collaborative multimodal systems: Simulation, data collection and analysis tools for the next decade, Eighth International Conference on Multimodal Interfaces (ICMI'06), ACM: New York, 209--226. Google Scholar
Digital Library
- Cetin, O. & Shriberg, L. (2006) Overlap in meetings: ASR effects and analysis by dialog factors, speakers, and collection site, Machine Learning and Multimodal Interaction, Lecture Notes in Computer Science, Springer, vol. 4299, 212--224. Google ScholarDigital Library
- Chi, M.T.H., Glaser, R., and Rees, E. (1982). Expertise in problem solving. In R.J. Sternberg (Ed.), Advances in the psychology of human intelligence, (Vol. 1). Hillsdale, NJ: Erlbaum.Google Scholar
- Dong, W., Kim, T. & Pentland, A. (2009) A Quantitative Analysis of Collective Creativity in Playing 20-Questions Games, ACM Creativity & Cognition, Berkeley, CA, October 27--30, 2009. Google ScholarDigital Library
- Dral, J., Heylen, D. & op den Akker, R. (2008) Detecting uncertainty in spoken dialogues: An explorative research to the automatic detection of a speakers' uncertainty by using prosodic markers, Sentiment analysis: emotion, metaphor, ontology and terminology, Marrakech, Morocco, 72--78.Google Scholar
- Ferguson, N. (1977) Simultaneous speech, interruptions, and dominance, British Journal of Social and Clinical Psychology, 16, 295--302.Google ScholarCross Ref
- Krauss, R. & Chiu, C.-Y. (1997) Language and social behavior, in D. Gilbert, S. Fiske & G. Lindsey (eds.) Handbook of social psychology, 4th edition, vol. 2, Boston: McGraw-Hill, 41--88.Google Scholar
- Liscombe, J., Hirschberg, J. & Vendetti, J. (2005) Detecting certainness in spoken tutorial dialogs, Proc. of Interspeech Europeech, Lisbon, Portugal.Google Scholar
- Lunsford, R., Oviatt, S. L. & Arthur, A. (2006) Toward open-microphone engagement for multiparty interactions, Eighth International Conference on Multimodal Interfaces (ICMI'06), ACM: New York, 273--280. Google ScholarDigital Library
- Luria, A. (1961) The Role of Speech in the Regulation of Normal and Abnormal Behavior, Liveright: NY.Google Scholar
- Oviatt, S.L. (2012) Multimodal interfaces, The HumanComputer Interaction Handbook: Fundamentals, Evolving Technologies and Emerging Applications (3rd ed.), (ed. by J. Jacko), LEA: Mahwah, NJ, chap. 18, 405--430.Google Scholar
- Oviatt, S.L. (2013a) Problem solving, domain expertise and learning: Ground-truth performance results for math data corpus, Second Intl. Grand Challenge Workshop on Multimodal Learning Analytics, December 2013. Google ScholarDigital Library
- Oviatt, S.L. (2013b) The Design of Future Educational Interfaces, Routledge Press.Google Scholar
- Oviatt, S., Cohen, A. & Weibel, N. (2013) Multimodal learning analytics: Description of math data corpus for ICMI grand challenge workshop, Second International Workshop on Multimodal Learning Analytics, Sydney Australia, Dec. 2013. Google ScholarDigital Library
- Oviatt, S., Cohen, A. & Weibel, N., Hang, K. & Thompson, K. (2014) Multimodal learning analytics data resources: Description of math data corpus and coded documents, Third Intl. Workshop on Multimodal Learning Analytics, Istanbul Turkey (for version with full appendices, see: http://mla.ucsd.edu/data/MMLA_Math_Data_Corpus.pdfGoogle Scholar
- Oviatt, S.L., Hang, K., Zhou, J., Yu, K. & Chen, F. (in press) Dynamic handwriting signal features predict domain expertise.Google Scholar
- Purandare, A. & Litman, D. (2008) Content-learning correlations in spoken tutoring dialogs at word, turn, and discourse levels, Proc. of the 21st Intl. FLAIRS Conference, Coconut Grove, FL.Google Scholar
- Roger, D. Bull, P. & Smith, S. (1988) The development of a comprehensive system for classifying interruptions, Journal of Language and Social Psychology, 7, 27--34.Google ScholarCross Ref
- Sacks, H., Schegloff, E.A. & Jefferson, G. (1974) A simplest systematics for the organization of turn-taking for conversation, Language, 50(4), 696--735.Google ScholarCross Ref
- Schegloff, E.A. (2000) Overlapping talk and the organization of turn-taking for conversation, Language in Society, 29:1, 1--63.Google ScholarCross Ref
- Scherer, S., Weibel, N., Oviatt, S. & Morency, L.P. (2012) Multimodal prediction of expertise and leadership in learning groups, Proc. of the First International Workshop on Multimodal Learning Analytics, ACM: N.Y. Google ScholarDigital Library
- Shriberg, L., Stolcke, A. & Baron, D. (2001) Observations on Overlap: Findings and implications for automatic processing of multi-party conversation, Proc. of Eurospeech.Google Scholar
- Vygotsky, L. (1962) Thought and Language, MIT Press, Cambridge MA. (Transl. by Hanfmann & Vakar from 1934 original).Google Scholar
- Worsley, M. & Blikstein, P. (2010) Toward the development of learning analytics: Student speech as an automatic and natural form of assessment, AERA Conference.Google Scholar
- Zimmerman, B. (2006) Development and adaptation of expertise: The role of self-regulatory processes and beliefs, The Cambridge Handbook of Expertise and Expert Performance (ed. by K. Ericsson, N. Charness, P. Feltovich & R. Hoffman), Cambridge University Press: New York, 705--722.Google Scholar
Index Terms
- Spoken Interruptions Signal Productive Problem Solving and Domain Expertise in Mathematics
Recommendations
Written and multimodal representations as predictors of expertise and problem-solving success in mathematics
ICMI '13: Proceedings of the 15th ACM on International conference on multimodal interactionOne aim of multimodal learning analytics is to analyze rich natural communication modalities to identify domain expertise and learning rapidly and reliably. In this research, written and multimodal representations are analyzed from the Math Data Corpus, ...
Problem solving, domain expertise and learning: ground-truth performance results for math data corpus
ICMI '13: Proceedings of the 15th ACM on International conference on multimodal interactionProblem solving, domain expertise, and learning are analyzed for the Math Data Corpus, which involves multimodal data on collaborating student groups as they solve math problems together across sessions. Compared with non-expert students, domain experts ...
Written Activity, Representations and Fluency as Predictors of Domain Expertise in Mathematics
ICMI '14: Proceedings of the 16th International Conference on Multimodal InteractionThe emerging field of multimodal learning analytics evaluates natural communication modalities (digital pen, speech, images) to identify domain expertise, learning, and learning-oriented precursors. Using the Math Data Corpus, this research investigated ...
Comments