Blending Peer Instruction with Just-In-Time Teaching: Jointly Optimal Task Scheduling with Feedback for Classroom Flipping

Blended learning often requires alternating between asynchronous pre-class and synchronous in-class activities using online technologies to enhance the overall learning experience. Subject to constraints on desired learning outcome specifications and individual student preference, can we jointly optimize pre-class and in-class tasks to improve the two-way interaction between students and the instructor? We leverage ideas of self-assessment in Just-In-Time Teaching and Peer Instruction to propose an optimization-theoretic framework to analyze the optimal trade-off between the time invested in two different learning tasks for each individual student. We show that the problem can be formulated as a linear program, which can be efficiently solved to determine the optimal amount of time for pre-class and in-class learning. We develop a mobile chatbot software integrated with feedback data analytics to blend asynchronous pre-class quiz assessment together with the synchronous in-class poll-quiz routine of Peer Instruction to achieve classroom flipping that can be used for remote and hybrid teaching and learning.