Harnessing the Wisdom of the Classes

Abstract

Generating questions to engage and measure students is often challenging and time-consuming. Furthermore, these questions do not always transfer well between student populations due to differences in background, course emphasis, or ambiguity in the questions or answers. We introduce a contributing student pedagogy activity facilitated by machine learning that can generate questions with associated answer-reasoning sets. We call this process Adaptive Tool-Driven Conception Generation. A tool implementing this process has been deployed, and it explicitly optimizes the process for questions that divide student opinion. In a study involving arrays in Java, this novel process: generates questions similar to expertdesigned questions, produces novel questions that identify potential student misconceptions, and provides statistical estimates of the prevalence of misconceptions. This process allows the generation of quiz and discussion questions with less expert effort, facilitates a subprocess in the creation of concept inventories, and also raises the possibility of running reproduction studies relatively cheaply. CCS CONCEPTS • Social and professional topics → Student assessment; • Computing methodologies → Machine learning;