If more than 70 percent of the class gets a question right initially, Mazur moves directly to the answer and the explanation, then poses a more difficult question—because there’s no point in going through the peer instruction process if most students already understand the concept, he explained.
Similarly, if fewer than 30 percent of the class is right the first time, the correct answer won’t spread throughout the classroom—and so he revisits the concept, then tries an easier question.
To prepare for class, Mazur develops a set of questions at various levels of difficulty, with the goal of having 30 percent to 70 percent of the class able to answer each question correctly.
But there are several challenges to implementing this strategy effectively, he acknowledged—including how to design good questions, optimize the discussion, and manage class time.
To address these challenges, Mazur and two Harvard colleagues have developed a unique software-based system called Learning Catalytics.
The software uses intelligent algorithms and data analytics to improve the quality of questions that instructors can pose. It also helps instructors pair students who gave right and wrong answers during the discussion phase, and it helps instructors know when it’s time to wrap up each phase of the process and move on.
The software platform is device-agnostic, meaning students can log in with whatever mobile device they already own.