How learning works in Peer Instruction and Learning Catalytics


March 11, 2013

Many teachers are using Peer Instruction and classroom response systems (CRSs) to flip their classrooms and to engage students in deep learning and subject-matter understanding. After trying a range of CRSs throughout his career, in 2011 Eric Mazur teamed up with Brian Lukoff and Gary King at Harvard University to develop Learning Catalytics (LC). LC is a new CRS that allows instructors to ask a wide range of open-ended questions, can automatically group students based on their responses to questions, provide out-of-class and in-class quizzing, and fully facilitate Team-Based Learning. (Math Professor Robert Talbert writes a lot about LC here.)

In a new book by Emerald Publishing, “Increasing Student Engagement and Retention Using Classroom Technologies: Classroom Response Systems and Mediated Discourse Technologies,” we have a chapter titled “Catalyzing Learner Engagement Using Cutting-Edge Classroom Response Systems in Higher Education” (Schell, Lukoff, & Mazur, 2013).

The chapter poses two questions:

1) How can LC help students engage with subject matter in ways that will help them learn?

2) How can instructors use LC to measure student engagement in new ways?

We begin with an overview of key learning science principles, such as metacognition, self-monitoring, self regulation,  conceptual change, transfer of learning, and feedback, and provide examples of how LC relates to those principles.  A brief outline of Peer Instruction is also included, as is some screen shots of nifty features native to LC–such as a wide range of question types that go beyond multiple choice and intelligent grouping of students. The chapter concludes with pilot data from a course using LC at Harvard University we used to identify possible examples of student metacognition as well as “natural teachers”– students who are consistently able to convince their peers of the correct answer.

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