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Problem solving, domain expertise and learning: ground-truth performance results for math data corpus

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Sharon OviattAuthors Info & Claims

ICMI '13: Proceedings of the 15th ACM on International conference on multimodal interaction

Pages 569 - 574

https://doi.org/10.1145/2522848.2533791

Published: 09 December 2013 Publication History

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Abstract

Problem 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 contributed more group solutions, solved more problems correctly and took less time. These differences between experts and non-experts were accentuated on harder problems. A cumulative expertise metric validated that expert and non-expert students represented distinct non overlapping populations, a finding that replicated across sessions. Group performance also improved 9.4% across sessions, due mainly to learning by expert students. These findings satisfy ground-truth conditions for developing prediction techniques that aim to identify expertise based on multimodal communication and behavior patterns. Together with the Math Data Corpus, these results contribute valuable resources for supporting data-driven grand challenges on multimodal learning analytics, which aim to develop new techniques for predicting expertise early, reliably, and objectively. as well as learning-oriented precursors.

References

[1]

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.

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Google Scholar

[2]

Baker, R. & Yacef, K. (2009) The state of educational data mining in 2009: A review and future visions, Journal of Educational Data Mining (JEDM), 1:3--17.

Google Scholar

[3]

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.

Digital Library

Google Scholar

[4]

Oviatt, S.L. (2012) Multimodal interfaces, The Human-Computer Interaction Handbook: Fundamentals, Evolving Technologies and Emerging Applications (revised 3rd edition), (ed. by J. Jacko), Lawrence Erlbaum Assoc: Mahwah, New Jersey, chap. 18, 405--430.

Google Scholar

[5]

Oviatt, S., Cohen, A. & Weibel, N. (2013) Multimodal learning analytics: Description of math data corpus for ICMI grand challenge workshop, Second Intl. Workshop on Multimodal Learning Analytics, Sydney Australia.

Digital Library

Google Scholar

[6]

Oviatt, S., Cohen, A. & Weibel, N. (2013) Multimodal learning analytics: Description of math data corpus for ICMI grand challenge workshop with full appendices, Second Intl. Workshop on Multimodal Learning Analytics, Sydney Australia: http://mla.ucsd.edu/data/MMLA_Math_Data_Corpus.pdf

Digital Library

Google Scholar

[7]

Scherer, S., Weibel, N., Oviatt, S. & Morency, L.P. Multimodal prediction of expertise and leadership in learning groups, Proc. of the First International Workshop on Multimodal Learning Analytics, 2012, ACM: N.Y.

Digital Library

Google Scholar

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Thomas CUdhayanan PYadav APurvaj SJayagopi D(2024)Multimodal Unsupervised Domain Adaptation for Predicting Speaker Characteristics from VideoSN Computer Science10.1007/s42979-024-02723-65:5Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1007/s42979-024-02723-6
Alwahaby HCukurova MPapamitsiou ZGiannakos M(2022)The Evidence of Impact and Ethical Considerations of Multimodal Learning Analytics: A Systematic Literature ReviewThe Multimodal Learning Analytics Handbook10.1007/978-3-031-08076-0_12(289-325)Online publication date: 9-Oct-2022
https://doi.org/10.1007/978-3-031-08076-0_12
Oviatt SLin JSriramulu A(2021)I Know What You Know: What Hand Movements Reveal about Domain ExpertiseACM Transactions on Interactive Intelligent Systems10.1145/342304911:1(1-26)Online publication date: 15-Mar-2021
https://dl.acm.org/doi/10.1145/3423049
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Index Terms

Problem solving, domain expertise and learning: ground-truth performance results for math data corpus
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods

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Published In

ICMI '13: Proceedings of the 15th ACM on International conference on multimodal interaction

December 2013

630 pages

ISBN:9781450321297

DOI:10.1145/2522848

General Chairs:
Julien Epps
The University of New South Wales, Australia
,
Fang Chen
National ICT Australia, Australia
,
Sharon Oviatt
Incaa Designs, USA
,
Kenji Mase
Nagoya University, Japan
,
Program Chairs:
Andrew Sears
Rochester Institute of Technology, USA
,
Kristiina Jokinen
University of Helsinki, Finland
,
Björn Schuller
Technische Universität München, Germany

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 09 December 2013

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ICMI '13: 2013 International Conference on Multimodal Interaction

December 9 - 13, 2013

Sydney, Australia

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ICMI '13 Paper Acceptance Rate 49 of 133 submissions, 37%;

Overall Acceptance Rate 453 of 1,080 submissions, 42%

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Thomas CUdhayanan PYadav APurvaj SJayagopi D(2024)Multimodal Unsupervised Domain Adaptation for Predicting Speaker Characteristics from VideoSN Computer Science10.1007/s42979-024-02723-65:5Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1007/s42979-024-02723-6
Alwahaby HCukurova MPapamitsiou ZGiannakos M(2022)The Evidence of Impact and Ethical Considerations of Multimodal Learning Analytics: A Systematic Literature ReviewThe Multimodal Learning Analytics Handbook10.1007/978-3-031-08076-0_12(289-325)Online publication date: 9-Oct-2022
https://doi.org/10.1007/978-3-031-08076-0_12
Oviatt SLin JSriramulu A(2021)I Know What You Know: What Hand Movements Reveal about Domain ExpertiseACM Transactions on Interactive Intelligent Systems10.1145/342304911:1(1-26)Online publication date: 15-Mar-2021
https://dl.acm.org/doi/10.1145/3423049
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Sriramulu ALin JOviatt S(2019)Dynamic Adaptive Gesturing Predicts Domain Expertise in Mathematics2019 International Conference on Multimodal Interaction10.1145/3340555.3353726(105-113)Online publication date: 14-Oct-2019
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Spoken Interruptions Signal Productive Problem Solving and Domain Expertise in Mathematics

Written and multimodal representations as predictors of expertise and problem-solving success in mathematics

Written Activity, Representations and Fluency as Predictors of Domain Expertise in Mathematics

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