Webcam-based eye tracking to detect mind wandering and comprehension errors.

Recent advances in computer vision have opened the door for scalable eye tracking using only a webcam. Such solutions are particularly useful for online educational technologies, in which a goal is to respond adaptively to students' ongoing experiences. We used WebGazer, a webcam-based eye-tracker, to automatically detect covert cognitive states during an online reading-comprehension task related to task-unrelated thought and comprehension. We present data from two studies using different populations: (1) a relatively homogenous sample of university students (N = 105), and (2) a more diverse sample from Prolific (N = 173, with < 20% White participants). Across both studies, the webcam-based eye-tracker provided sufficiently accurate and precise gaze measurements to predict both task-unrelated thought and reading comprehension from a single calibration. We also present initial evidence of predictive validity, including a positive correlation between predicted rates of task-unrelated thought and comprehension scores. Finally, we present slicing analyses to determine how performance changed under certain conditions (lighting, glasses, etc.) and generalizability of the results across the two datasets (e.g., training on the data Study 1 and testing on data from Study 2, and vice versa). We conclude by discussing results in the context of remote research and learning technologies.

Using artificial intelligence to assess personal qualities in college admissions.

Personal qualities like prosocial purpose and leadership predict important life outcomes, including college success. Unfortunately, the holistic assessment of personal qualities in college admissions is opaque and resource intensive. Can artificial intelligence (AI) advance the goals of holistic admissions? While cost-effective, AI has been criticized as a "black box" that may inadvertently penalize already disadvantaged subgroups when used in high-stakes settings. Here, we consider an AI approach to assessing personal qualities that aims to overcome these limitations. Research assistants and admissions officers first identified the presence/absence of seven personal qualities in n = 3131 applicant essays describing extracurricular and work experiences. Next, we fine-tuned pretrained language models with these ratings, which successfully reproduced human codes across demographic subgroups. Last, in a national sample (N = 309,594), computer-generated scores collectively demonstrated incremental validity for predicting 6-year college graduation. We discuss challenges and opportunities of AI for assessing personal qualities.

How Does High School Extracurricular Participation Predict Bachelor's Degree Attainment? It is Complicated.

This study answered novel questions about the connection between high school extracurricular dosage (number of activities and participation duration) and the attainment of a bachelor's degree. Using data from the Common Application and the National Student Clearinghouse (N = 311,308), we found that greater extracurricular participation positively predicted bachelor's degree attainment. However, among students who ultimately earned a bachelor's degree, participating in more than a moderate number of high school activities (3 or 4) predicted decreasing odds of earning a bachelor's degree on time (within 4 years). This effect intensified as participation duration increased, such that students who participated in the greatest number of high school activities for the most years were the most likely to delay college graduation.

Gaze-based signatures of mind wandering during real-world scene processing.

Physiological limitations on the visual system require gaze to move from location to location to extract the most relevant information within a scene. Therefore, gaze provides a real-time index of the information-processing priorities of the visual system. We investigated gaze allocation during mind wandering (MW), a state where cognitive priorities shift from processing task-relevant external stimuli (i.e., the visual world) to task-irrelevant internal thoughts. In both a main study and a replication, we recorded the eye movements of college-aged adults who studied images of urban scenes and responded to pseudorandom thought probes on whether they were mind wandering or attentively viewing at the time of the probe. Probe-caught MW was associated with fewer and longer fixations, greater fixation dispersion, and more frequent eyeblinks (only observed in the main study) relative to periods of attentive scene viewing. These findings demonstrate that gaze indices typically considered to represent greater engagement with scene processing (e.g., longer fixations) can also indicate MW. In this way, the current work exhibits a need for empirical investigations and computational models of gaze control to account for MW for a more accurate representation of the moment-to-moment information-processing priorities of the visual system. (PsycINFO Database Record