Monolith, Multiplicity or Multivocality: What do we stand for and where do we go from here?

Authors

  • Carolyn Penstein Rose Carnegie Mellon University

DOI:

https://doi.org/10.18608/jla.2019.63.6

Keywords:

Commentary

Abstract

This contribution offers a commentary on Neil Selwyn’s write up of his keynote talk from the Learning Analytics and Knowledge Conference in 2018 (Selwyn, this issue).  The article has three main sections, namely an account of what Learning Analytics has done, an account of the values behind Learning Analytics, and some ideas for moving forward.  Thus, this reflection will also address aspects of each of these.

Author Biography

Carolyn Penstein Rose, Carnegie Mellon University

Dr. Carolyn Rosé is an Associate Professor of Language Technologies and Human-Computer Interaction in the School of Computer Science at Carnegie Mellon University.  Her research program is focused on better understanding the social and pragmatic nature of conversation, and using this understanding to build computational systems that can improve the efficacy of conversation between people, and between people and computers. In order to pursue these goals, she invokes approaches from computational discourse analysis and text mining, conversational agents, and computer supported collaborative learning.  She serves as President Elect of the International Society of the Learning Sciences and the Executive Board of the International Artificial Intelligence in Education Society.  She serves as Associate Editor of the International Journal of Computer Supported Collaborative Learning and the IEEE Transactions on Learning Technologies.

References

Chi, M. T. (1997). Quantifying qualitative analyses of verbal data: A practical guide. The journal of the learning sciences, 6(3), 271-315.

Ericsson, K. A., & Simon, H. A. (1993). Protocol Analysis (pp. 1-31). Cambridge, MA: The MIT Press.

Rosé, C. P., Wang, Y.C., Cui, Y., Arguello, J., Stegmann, K., Weinberger, A., Fischer, F., (2008). Analyzing Collaborative Learning Processes Automatically: Exploiting the Advances of Computational Linguistics in Computer-Supported Collaborative Learning, submitted to the International Journal of Computer Supported Collaborative Learning 3(3), pp237-271.

Rosé, C. P. (2017). Discourse Analytics, Handbook of Data Mining and Learning Analytics. Hoboken, NJ: Wiley.

Rosé, C. P. (2018). Learning analytics in the Learning Sciences, invited chapter in F. Fischer, C. Hmelo-Silver, S. Goldman, & P. Reimann (Eds.) International Handbook of the Learning Sciences, Taylor & Francis.

Selwyn, N. (this issue). What’s the Problem with learning analytics? Journal of Learning Analytics, 6(3), 11–19

Suthers, D. D., Lund, K., Rosé, C. P., Teplovs, C., & Law, N. (2013). Productive multivocality in the analysis of group interactions. Springer US.

van Someren, M. W., Barnard, Y. F., & Sandberg, J. A. C. (1994).The Think Aloud Method: A Practical Guide to Modelling Cognitive Processes. New York: Academic Press. Chapter 7

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Published

2019-12-13

How to Cite

Rose, C. P. (2019). Monolith, Multiplicity or Multivocality: What do we stand for and where do we go from here?. Journal of Learning Analytics, 6(3), 31–34. https://doi.org/10.18608/jla.2019.63.6

Issue

Vol. 6 No. 3 (2019): Invited Dialogue: "What's the Problem with Learning Analytics?" (Selwyn, 2019)

Section

Invited Dialogue: "What's the Problem with Learning Analytics?" (Selwyn, 2019)

License

Copyright (c) 2019 Journal of Learning Analytics

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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