Unraveling Students’ Interaction Around a Tangible Interface using Multimodal Learning Analytics

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

PDF
Published Oct 18, 2015
DOI https://doi.org/10.5281/zenodo.3554729
Bertrand Schneider
Stanford University
Paulo Blikstein
Stanford University

Abstract

In this paper, we describe multimodal learning analytics (MMLA) techniques to analyze data collected around an interactive learning environment. In a previous study (Schneider and Blikstein, 2015), we designed and evaluated a Tangible User Interface (TUI) where dyads (i.e., pairs) of students were asked to learn about the human auditory system by reconstructing it. In the current study, we present the analysis of the data collected in the form of logs, both from students' interaction with the tangible interface as well as from their gestures, and we describe how we extracted meaningful predictors for student learning from these two datasets. First we show how information retrieval techniques can be used on the tangible interface logs to predict learning gains. Second, we explored how KinectTM data can inform "in-situ" interactions around a tabletop by using clustering algorithms to find prototypical body positions. Finally, we fed those features to a machine-learning classifier (Support Vector Machine) and divided students in two groups after performing a median split on their learning scores. We found that we were able to predict students' learning gains (i.e., being above or belong the median split) with very high accuracy. We discuss the implications of these results for analyzing rich data from multimodal learning environments.

How to Cite

Schneider, B., & Blikstein, P. (2015). Unraveling Students’ Interaction Around a Tangible Interface using Multimodal Learning Analytics. Journal of Educational Data Mining, 7(3), 89–116. https://doi.org/10.5281/zenodo.3554729
Abstract 1043 | PDF Downloads 448

##plugins.themes.bootstrap3.article.details##

Keywords

Collaborative Learning, Tangible User Interface, Kinect Sensor, Machine Learning, Information Retrieval Techniques

References
ABRAHAMSON, D., TRNINIC, D., GUTIÉRREZ, J.F., HUTH, J., AND LEE, R.G. 2011. Hooks and Shifts: A Dialectical Study of Mediated Discovery. Technology, Knowledge and Learning 16, 1, 55–85.

ANDERSON, M.L. 2003. Embodied cognition: A field guide. Artificial intelligence 149, 1, 91– 130.

BACHOUR, K., KAPLAN, F., & DILLENBOURG, P. 2008. Reflect: An interactive table for regulating face-to-face collaborative learning. Times of Convergence. Technologies Across Learning Contexts. Springer Berlin Heidelberg, 39-48.

BLIKSTEIN, P. & WORSLEY, M. (accepted). Multimodal Learning Analytics: a methodological framework for research in constructivist learning. Journal of Learning Analytics.

BLIKSTEIN, P., WORSLEY, M., PIECH, C., SAHAMI, M., COOPER, S., & KOLLER, D. 2014. Programming Pluralism: Using Learning Analytics to Detect Patterns in the Learning of Computer Programming. Journal of the Learning Sciences.

BRANSFORD, J. AND SCHWARTZ, D. 1999. Rethinking Transfer: A Simple Proposal with Multiple Implications. Review of Research in Education, 24.

CHARTRAND, T.L. AND BARGH, J.A. 1999. The chameleon effect: The perception–behavior link and social interaction. Journal of Personality and Social Psychology 76, 6, 893–910.

CHURCH, R. BRECKINRIDGE. 1999. Using gesture and speech to capture transitions in learning. Cognitive Development 14, no. 2: 313-342.

ESCALERA, S., GONZÀLEZ, J., BARÓ, X., REYES, M., LOPES, O., GUYON, I., AND ESCALANTE, H. 2013. Multi-modal gesture recognition challenge 2013: Dataset and results. In Proceedings of the 15th ACM on International conference on multimodal interaction (pp. 445-452). ACM.

GWEON, G., JAIN, M., MCDONOUGH, J., RAJ, B., & ROSÉ, C. P. 2013. Measuring prevalence of other-oriented transactive contributions using an automated measure of speech style accommodation. International Journal of Computer-Supported Collaborative Learning, 8(2), 245-265.

HALL, E.T. The hidden dimension (1st ed.). 1966. Doubleday & Co, New York, NY, US.

HATANO, G. AND INAGAKI, K. Two courses of expertise. 1986. In H.W. Stevenson, H. Azuma and K. Hakuta, eds., Child development and education in Japan. W H Freeman/Times Books/ Henry Holt & Co, New York, NY, US, 262–272.

MANNING, C.D., RAGHAVAN, P., AND SCHÜTZE, H. 2008. Introduction to Information Retrieval. Cambridge University Press, New York, NY, USA.

PENTLAND, A., & HEIBECK, T. 2008. Honest signals. Cambridge, MA: MIT press.

RICHARDSON, D.C. AND DALE, R. 2005. Looking To Understand: The Coupling Between Speakers’ and Listeners’ Eye Movements and Its Relationship to Discourse Comprehension. Cognitive Science 29, 6, 1045–1060.

ROTH, W.-M. 2001. Gestures: Their Role in Teaching and Learning. Review of Educational Research 71, 3, 365–392.

ROTHMAN, K. J. 1990. No adjustments are needed for multiple comparisons. Epidemiology, 1(1), 43-46. SCHNEIDER B., WALLACE J., BLIKSTEIN P., & PEA, R. 2013. Preparing for Future Learning with a Tangible User Interface: the Case of Neuroscience. IEEE Transactions on Learning Technologies, 6, 2, 117-129.

SCHNEIDER, B., AND BLIKSTEIN, P. 2015. Discovery Versus Direct Instruction: Learning Outcomes of Two Pedagogical Models Using Tangible Interfaces. International Conference on Computer-Supported Collaborative Learning, CSCL'2015.

SCHNEIDER, B. AND PEA, R. 2013. Real-time mutual gaze perception enhances collaborative learning and collaboration quality. International Journal of Computer-Supported Collaborative Learning 8, 4, 375–397.

SCHWARTZ, D.L. AND ARENA, D. 2013. Measuring What Matters Most: Choice-Based Assessments for the Digital Age. MIT Press.

SHAER, O., STRAIT, M., VALDES, C., FENG, T., LINTZ, M., AND WANG, H. 2011. Enhancing genomic learning through tabletop interaction. Proceedings of the 2011 annual conference on Human factors in computing systems, ACM, 2817–2826.

SHERIN, B. 2012. Using Computational Methods to Discover Student Science Conceptions in Interview Data. Proceedings of the 2Nd International Conference on Learning Analytics and Knowledge, ACM, 188–197.

SIEGLER, R. S., AND K. CROWLEY. 1991. The microgenetic method: A direct means for studying cognitive development. American Psychologist 46.6: 606.

TSCHAN, F. 2002. Ideal Cycles of Communication (or Cognitions) in Triads, Dyads, and Individuals. Small Group Research 33, 6, 615 –643.

WORSLEY, M. AND BLIKSTEIN, P. 2013. Towards the Development of Multimodal Action Based Assessment. Proceedings of the Third International Conference on Learning Analytics and Knowledge, ACM, 94–101.
Issue
Vol. 7 No. 3 (2015)
Section
Articles

Authors who publish with this journal agree to the following terms:

  1. The Author retains copyright in the Work, where the term “Work” shall include all digital objects that may result in subsequent electronic publication or distribution.
  2. Upon acceptance of the Work, the author shall grant to the Publisher the right of first publication of the Work.
  3. The Author shall grant to the Publisher and its agents the nonexclusive perpetual right and license to publish, archive, and make accessible the Work in whole or in part in all forms of media now or hereafter known under a Creative Commons 4.0 License (Attribution-Noncommercial-No Derivatives 4.0 International), or its equivalent, which, for the avoidance of doubt, allows others to copy, distribute, and transmit the Work under the following conditions:
    1. Attribution—other users must attribute the Work in the manner specified by the author as indicated on the journal Web site;
    2. Noncommercial—other users (including Publisher) may not use this Work for commercial purposes;
    3. No Derivative Works—other users (including Publisher) may not alter, transform, or build upon this Work,with the understanding that any of the above conditions can be waived with permission from the Author and that where the Work or any of its elements is in the public domain under applicable law, that status is in no way affected by the license.
  4. The Author is able to enter into separate, additional contractual arrangements for the nonexclusive distribution of the journal's published version of the Work (e.g., post it to an institutional repository or publish it in a book), as long as there is provided in the document an acknowledgement of its initial publication in this journal.
  5. Authors are permitted and encouraged to post online a pre-publication manuscript (but not the Publisher’s final formatted PDF version of the Work) in institutional repositories or on their Websites prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (see The Effect of Open Access). Any such posting made before acceptance and publication of the Work shall be updated upon publication to include a reference to the Publisher-assigned DOI (Digital Object Identifier) and a link to the online abstract for the final published Work in the Journal.
  6. Upon Publisher’s request, the Author agrees to furnish promptly to Publisher, at the Author’s own expense, written evidence of the permissions, licenses, and consents for use of third-party material included within the Work, except as determined by Publisher to be covered by the principles of Fair Use.
  7. The Author represents and warrants that:
    1. the Work is the Author’s original work;
    2. the Author has not transferred, and will not transfer, exclusive rights in the Work to any third party;
    3. the Work is not pending review or under consideration by another publisher;
    4. the Work has not previously been published;
    5. the Work contains no misrepresentation or infringement of the Work or property of other authors or third parties; and
    6. the Work contains no libel, invasion of privacy, or other unlawful matter.
  8. The Author agrees to indemnify and hold Publisher harmless from Author’s breach of the representations and warranties contained in Paragraph 6 above, as well as any claim or proceeding relating to Publisher’s use and publication of any content contained in the Work, including third-party content.