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CPSX Collaborative Problem Solving Online

CPSX is an online extension to the Open edX learning management system designed to support synchronous, remote collaboration on homework problems, particularly in math and science. Traditional end-of-chapter problems can be converted to collaborative forms that engage students in higher-order processes such as negotiation of shared understanding. Data collected using this platform can also be analyzed using innovative psychometric models. By conditioning collaborative performance on individual strengths, these analytics provide a direct measure of the collaboration skills of group members. To support learning how to solve problems collaboratively, we need opportunities to both practice and assess collaborative problem solving.

Students Working Together in front of a Laptop

Collaboration Feedback

Collaboration is one of the skills required to be a professional in the 21st century. As important as they are, collaboration skills are seldom explicitly taught in formal education. These skills are often learned as by-products of collaborative activities with little to no feedback from teachers about individual or group performance with respect to the actual collaboration. This project researches ways to transform feedback processes for collaborative learning activities. It innovates upon the current state-of-the-art in multimodal learning analytics techniques by using computer vision, automatic human-behaviour analysis, speech recognition and natural language processing to estimate different collaboration constructs. These estimates of collaboration are then used to explore innovative feedback interfaces (real-time, multimodal, human-amplified) and their affordances in different collaborative learning contexts and with different types of data.

Discussion Forum Analytics

The Discussion Forum Analytics project is a partnership between LEARN and NYU’s School of Professional Studies to support online students with feedback on their group processes. An initial pilot of relevant metrics are tied to course expectations with the potential to feed into global learning analytic services.