Understanding Human Functioning & Enhancing Human Potential through Computational Methods

Presented online on October 8, 2020 at 2:00 p.m.Sidney D’Mello is an Associate Professor in the Institute of Cognitive Science and Department of Computer Science at the University of Colorado Boulder. His work lies at the intersection of the computing, cognitive, affective, social, and learning sciences. D’Mello is interested in the dynamic interplay between cognition and emotion while individuals and groups engage in complex real-world activities.Runtime: 55:09 minutesIt is generally accepted that computational methods can complement traditional approaches to understanding human functioning, including thoughts, feelings, behaviors, and social interactions. I suggest that their utility extends beyond a mere complementary role. They serve a necessary role when data is too large for manual analysis, an opportunistic role by addressing questions that are beyond the purview of traditional methods, and a promissory role in facilitating change when fully-automated computational models are embedded in closed-loop intelligent systems. Multimodal computational approaches provide further benefits by affording analysis of disparate constructs emerging across multiple types of interactions in diverse contexts. To illustrate, I will discuss a research program that use linguistic, paralinguistic, behavioral, and physiological signals for the analysis of individual, small group, multi-party, and human-computer interactions in the lab and in the wild with the goals of understanding cognitive, noncognitive, and socio-affective-cognitive processes while improving human efficiency, engagement, and effectiveness. I will also discuss how these ideas align with our new NSF National AI Institute on Student-AI Teaming and how you can get involved in the research

Automated Gaze-Based Mind Wandering Detection during Computerized Learning in Classrooms

We investigate the use of commercial off-the-shelf (COTS) eye-trackers to automatically detect mind wandering—a phenomenon involving a shift in attention from task-related to task-unrelated thoughts—during computerized learning. Study 1 (N = 135 high-school students) tested the feasibility of COTS eye tracking while students learn biology with an intelligent tutoring system called GuruTutor in their classroom. We could successfully track eye gaze in 75% (both eyes tracked) and 95% (one eye tracked) of the cases for 85% of the sessions where gaze was successfully recorded. In Study 2, we used this data to build automated student-independent detectors of mind wandering, obtaining accuracies (mind wandering F1 = 0.59) substantially better than chance (F1 = 0.24). Study 3 investigated context-generalizability of mind wandering detectors, finding that models trained on data collected in a controlled laboratory more successfully generalized to the classroom than the reverse. Study 4 investigated gaze- and video- based mind wandering detection, finding that gaze-based detection was superior and multimodal detection yielded an improvement in limited circumstances. We tested live mind wandering detection on a new sample of 39 students in Study 5 and found that detection accuracy (mind wandering F1 = 0.40) was considerably above chance (F1 = 0.24), albeit lower than offline detection accuracy from Study 1 (F1 = 0.59), a finding attributable to handling of missing data. We discuss our next steps towards developing gaze-based attention-aware learning technologies to increase engagement and learning by combating mind wandering in classroom contexts

Predicting Individual Action Switching in Passively Experienced and Continuous Interactive Tasks Using the Fluid Events Model

The Fluid Events Model is aimed at predicting changes in the actions people take on a moment-by-moment basis. In contrast with other research on action selection, this work does not investigate why some course of action was selected, but rather the likelihood of discontinuing the current course of action and selecting another in the near future. This is done using both task-based and experience-based factors. Prior work evaluated this model in the context of trial-by-trial, independent, interactive events, such as choosing how to copy a figure of a line drawing. In this paper, we extend this model to more covert event experiences, such as reading narratives, as well as to continuous interactive events, such as playing a video game. To this end, the model was applied to existing data sets of reading time and event segmentation for written and picture stories. It was also applied to existing data sets of performance in a strategy board game, an aerial combat game, and a first person shooter game in which a participant’s current state was dependent on prior events. The results revealed that the model predicted behavior changes well, taking into account both the theoretically defined structure of the described events, as well as a person’s prior experience. Thus, theories of event cognition can benefit from efforts that take into account not only how events in the world are structured, but also how people experience those events

Motion Tracker: Camera-Based Monitoring of Bodily Movements Using Motion Silhouettes.

Researchers in the cognitive and affective sciences investigate how thoughts and feelings are reflected in the bodily response systems including peripheral physiology, facial features, and body movements. One specific question along this line of research is how cognition and affect are manifested in the dynamics of general body movements. Progress in this area can be accelerated by inexpensive, non-intrusive, portable, scalable, and easy to calibrate movement tracking systems. Towards this end, this paper presents and validates Motion Tracker, a simple yet effective software program that uses established computer vision techniques to estimate the amount a person moves from a video of the person engaged in a task (available for download from http://jakory.com/motion-tracker/). The system works with any commercially available camera and with existing videos, thereby affording inexpensive, non-intrusive, and potentially portable and scalable estimation of body movement. Strong between-subject correlations were obtained between Motion Tracker's estimates of movement and body movements recorded from the seat (r =.720) and back (r = .695 for participants with higher back movement) of a chair affixed with pressure-sensors while completing a 32-minute computerized task (Study 1). Within-subject cross-correlations were also strong for both the seat (r =.606) and back (r = .507). In Study 2, between-subject correlations between Motion Tracker's movement estimates and movements recorded from an accelerometer worn on the wrist were also strong (rs = .801, .679, and .681) while people performed three brief actions (e.g., waving). Finally, in Study 3 the within-subject cross-correlation was high (r = .855) when Motion Tracker's estimates were correlated with the movement of a person's head as tracked with a Kinect while the person was seated at a desk (Study 3). Best-practice recommendations, limitations, and planned extensions of the system are discussed

Validation of survey effort measures of grit and self-control in a sample of high school students.

Personality traits such as grit and self-control are important determinants of success in life outcomes. However, most measures of these traits, which rely on self-reports, might be biased when used for the purpose of evaluating education policies or interventions. Recent research has shown the potential of survey effort-in particular, item non-response and careless answering-as a proxy measure of these traits. The current investigation uses a dataset of high school seniors (N = 513) to investigate survey effort measures in relationship with teacher reports, performance task measures, high school academic outcomes, and college attendance. Our results show promise for use of survey effort as proxy measures of grit and self-control