Operationalizing and Detecting Disengagement Within Online Science Microworlds

Abstract

In recent years, there has been increased interest in engagement during learning. This is of particular interest in the science, technology, engineering, and mathematics domains, in which many students struggle and where the United States needs skilled workers. This article lays out some issues important for framing research on this topic and provides a review of some existing work with similar goals on engagement in science learning. Specifically, here we seek to help better concretize engagement, a fuzzy construct, by operationalizing and detecting (i.e., identifying using a computational method) disengaged behaviors that are antithetical to engagement. We, in turn, describe our real-time detector (i.e., machine learned model) of disengaged behavior and how it was developed. Last, we address our ongoing research on how our detector of disengaged behavior will be used to intervene in real time to better support students' science inquiry learning in Inq-ITS (Inquiry-Intelligent Tutoring System; Gobert, Sao Pedro, Baker, Toto, & Montalvo, 2012; Gobert, Sao Pedro, Raziuddin, & Baker, 2013).

ACKNOWLEDGMENTS

We thank Michael Sao Pedro for assistance in data distillation and for helpful comments and suggestions; we thank Arnon Hershkovitz and Adam Nakama for helpful comments and suggestions in the development of the detector, and thank Yoon Jeon Kim for helpful comments on the final draft of the manuscript.

Additional information

Funding

This research was supported by grant “Empirical Research: Emerging Research: Using Automated Detectors to Examine the Relationships Between Learner Attributes and Behaviors During Inquiry in Science Microworlds”, National Science Foundation award #DRL-100864 awarded to Janice Gobert and Ryan Baker.