Automated Collaborative Process Analysis


Contributor: Carolyn Rosé (Carnegie Mellon University)

Automated Collaborative Process Analysis is one area within the field of Learning Analytics focused on applying computational modeling technology to process data from collaborative learning encounters. It draws from a similar technological foundation to processing of open response questions in educational contexts and assessment of writing, but it encompasses more than that, including analysis of discussions occurring in discussion forums, chat rooms, microblogs, blogs, and even wikis. This work has had its impact in multiple areas, including offering analytic lenses to support research, enabling formative and summative assessment, enabling of dynamic and context sensitive triggering of interventions to improve the effectiveness of learning activities, and provision of reflection tools such as reports and feedback after learning activities in support of both learning and instruction. While it is true that discourse is incredibly complex, it is still true that there are meaningful patterns that state-of-the-art modeling approaches are able to identify. Much of the existing work in this area views learning and its connection with language from a Cognitive lens, in other words, seeking categories of language behavior whose presence in a discourse makes predictions about learning gains because of the connection between the associated discourse processes and cognitive processes associated with learning. Nevertheless, some work in the area views learning and its connection with language through a Social lens in order to leverage the important interplay between Cognitive and Social factors in learning. Some of this work seeks to identify discourse processes that reveal underlying dispositions, attitudes, and relationships that play a supporting (or sometimes interfering) role in the learning interactions. Much recent published work points to progress in this area as well as opportunities for growth in ongoing work.

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Syllabi and Slides

Video Resources

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Basic Reading:
  • Rosé, C., Wang, Y., Cui, Y., Arguello, J., Stegman, K., Weinberger, A., & Fischer, F. (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. [Access Online]
Additional Reading:
  • Gweon, G., Jain, M., Mc Donough, 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.
  • Howley, I., Mayfield, E. & Rosé, C. P. (2013). Linguistic analysis methods for studying small groups. In C. E. Hmelo-Silver, A. O’Donnell, C. Chan & C. Chin (Eds.), International Handbook of Collaborative Learning. Taylor and Francis, Inc.
  • Mayfield, E. & Rosé, C. P. (2013). LightSIDE: Open source machine learning for text accessible to non-experts. Invited chapter in the Handbook of Automated Essay Grading. Routledge Academic 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.
  • Rosé, C. P. & Tovares, A. (2015). What sociolinguistics and machine learning have to say to one another about interaction analysis. In L. Resnick, C. Asterhan & S. Clarke (Eds.), Socializing Intelligence Through Academic Talk and Dialogue. Washington, DC: American Educational Research Association.

Learning Scientists Who Have Researched This Topic

  • Kristy Boyer
  • Gijsbert Erkens
  • Bruce McLaren
  • Carolyn Rosé
  • Simon Buckingham Shum
  • Stefan Trausan-Matu