From AI-Enabled CSCL in the Classroom Towards Team Based Learning in the Workplace

By Carolyn Penstein Rosé, Ph.D.
Professor, Language Technologies Institute and Human-Computer Interaction Institute, School of Computer Science, Carnegie Mellon University

Carolyn Rosé will be presenting a webinar on Wednesday, February 27 1:00 PM-2:00 PM Eastern Time and Thursday, February 28, 1:00 PM-2:00 PM Eastern Time.

What does the field of CSCL (computer-supported collaborative learning) have to say to the problem that the World Economic Forum estimates that up to 5 million jobs may be lost to disruptive labor changes by 2020? We suggest an answer that is designed to enable embedding collaborative learning opportunities within project work.

While on the one hand recent advances in automation represent a victory for Artificial Intelligence, we are also painfully aware of mounting international concerns regarding unemployment, underemployment and the need for workers to reskill. This crisis creates the need within the learning sciences to consider how we can best address this problem. The recent rise and then fall of Massive Open Online Courses (MOOCs) as a paradigm for offering high quality education to the masses creates skepticism that existing online learning resources will solve the problem. From a different angle, well meaning companies offer training opportunities to their employees, but when push comes to shove, companies are known to push for short term productivity over learning and higher productivity in the long term. The practical goal of this research is to yield best practices for administration of software teams that enable intentional trade-offs between organizational objectives in the short and long term. Thus, the projected impact goal of the project is to enable workers to avoid job loss in the face of the rapidly changing landscape of work by creating opportunities for soon-to-be-displaced workers to retrain and retool while they are working.

Building on over a decade of AI-enabled collaborative learning experiences in the classroom and online, in this talk we report our work in progress beginning with classroom studies in large online software courses with substantial teamwork components. Project courses provide an effective test bed to begin our investigations due to similar tensions imposed by the reward structure. Project courses are believed to be valuable experiences for students to engage in reflection on concepts while applying them in practice. However there is a concern that the reward structure encourages students to engage in performance oriented behaviors, such as the most capable student taking on the lion’s share of the work while leaving the others behind. These behaviors undercut the opportunity to use the project experience for each student to gain practice and for the students to reflect together on underlying concepts. In our classroom work, we have adapted an industry standard team practice referred to as Mob Programming into a paradigm called Online Mob Programming (OMP) for the purpose of encouraging teams to reflect on concepts and share work in the midst of their project experience.
Figure 1 displays the current OMP setup in AWS Cloud9, and industrial standard software development environment that enables collaborative software development in teams.

Figure 1. This screen shot displays the current OMP setup in AWS Cloud9. The Intelligent Conversational Agent that offers structuring support during group interactions can be seen in the chat in the right hand panel.

Carolyn Rosé’s complete list of publications are available at her Google Scholar page:

Adamson, D., Dyke, G., Jang, H. J., Rosé, C. P. (2014). Towards an Agile Approach to Adapting Dynamic Collaboration Support to Student Needs, International Journal of AI in Education 24(1), pp91-121.

Rosé, C. P. & Ferschke, O. (2016). Technology Support for Discussion Based Learning: From Computer Supported Collaborative Learning to the Future of Massive Open Online Courses, International Journal of AI in Education, 25th Anniversary Edition, volume 26(2), pp 660-678

Howley, I. & Rosé, C. P. (2016). Towards Careful Practices for Automated Linguistic Analysis of Group Learning, Journal of Learning Analytics 3(3), pp 239-262

Wen, M., Maki, K., Dow, S. P., Herbsleb, J., Rosé, C. P. (2018). Supporting Virtual Team Formation through Community-Wide Deliberation, in Proceedings of the 21st ACM Conference on Computer-Supported Cooperative Work and Social Computing

Sankaranarayanan, S., Dashti, C., Bogart, C., Wang, X., Sakr, M., Rosé, C. (2018). When Optimal Team Formation is a Choice – Self-Selection versus Intelligent Team Formation Strategies in a Large Online Project-Based Course, Proceedings of AI in Education 2018

Sankaranarayanan, S., Dashti, C., Bogart, C., Wang, X., Marshall An, Clarence Ngoh, Michael Hilton, Sakr, M., Rosé, C. (in press). Online Mob Programming: Bridging the 21st Century Workplace and the Classroom, Proceedings of Computer-Supported Collaborative Learning (Volume II, Poster, available on request).

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