Eye-Mind reader: an intelligent reading interface that promotes long-term comprehension by detecting and responding to mind wandering

ABSTRACT

We zone out roughly 20-40% of the time during reading – a rate that is concerning given the negative relationship between mind-wandering and comprehension. We tested if Eye-Mind Reader – an intelligent interface that targeted mind-wandering as it occurred – could mitigate its negative impact on reading comprehension. When an eye-gaze-based classifier indicated that a reader was mind-wandering, those in a MW-Intervention condition were asked to self-explain the concept they were reading about. If the self-explanation quality was deemed subpar by an automated scoring mechanism, readers were asked to re-read parts of the text in order to correct their comprehension deficits and improve their self-explanation. Each participant in the MW-Intervention condition was paired with a Yoked-Control counterpart who received the exact same interventions regardless of whether they were mind-wandering. Results indicate that re-reading improved self-explanation quality for the MW-Intervention group, but not the control group. The two conditions performed equally well on textbase (i.e. fact-based) and inference-level comprehension questions immediately after reading. However, after a week-long delay, the MW-Intervention condition significantly outperformed the yoked-control condition on both comprehension assessments (ds = .352 and .307). Our findings suggest that real-time interventions during critical periods of mind-wandering can promote long-term retention and comprehension.

KEYWORDS:

• Intelligent UI
• education
• mind wandering
• cognitive science

Additional information

Funding

This research was supported by the National Science Foundation (NSF) [ITR 0325428, HCC 0834847, DRL 1235958]. Any opinions, findings and conclusions, or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of NSF.

Notes on contributors

Caitlin Mills

Caitlin Mills ([email protected]) is a psychologist with an interest in the correlates and consequences of mind-wandering and affect during complex learning. She is an Assistant Professor in the Department of Psychology at the University of New Hampshire.

Julie Gregg

Julie Gregg ([email protected]) is a psychologist who utilizes machine learning to predict reading comprehension from eye-movement data. She is a data scientist in Brooklyn, NY.

Robert Bixler

Robert Bixler ([email protected]) is a computer scientist who specializes in predicting mind-wandering using physiological signals such as eye-gaze during reading and film comprehension tasks. He is a Ph.D. candidate in the Computer Science department at the University of Notre Dame.

Sidney K. D’Mello

Sidney K. D’Mello ([email protected]) conducts research in the fields of affective and attentional computing, multimodal interaction, speech and discourse processing, and intelligent learning environment. His team conducts basic research on affective and cognitive states (e.g., confusion, boredom, mind wandering) across a range of interaction contexts, develops real-time computational models of these states, and integrates the models in affect- and attention-aware intelligent technologies. He is an Associate Professor at the Institute of Cognitive Science at the University of Colorado Boulder.