Eye movements provide a window into cognitive processes, but much of the research harnessing this data has been confined to the laboratory. We address whether eye gaze can be passively, reliably, and privately recorded in real-world environments across extended timeframes using commercial-off-the-shelf (COTS) sensors. We recorded eye gaze data from a COTS tracker embedded in participants (N=20) work environments at pseudorandom intervals across a two-week period. We found that valid samples were recorded approximately 30% of the time despite calibrating the eye tracker only once and without placing any other restrictions on participants. The number of valid samples decreased over days with the degree of decrease dependent on contextual variables (i.e., frequency of video conferencing) and individual difference attributes (e.g., sleep quality and multitasking ability). Participants reported that sensors did not change or impact their work. Our findings suggest the potential for the collection of eye-gaze in authentic environments. © 2022 ACM.

Eye tracking has been a research tool for decades, providing insights into interactions, usability, and, more recently, gaze-enabled interfaces. Recent work has utilized consumer-grade and webcam-based eye tracking, but is limited by the need to repeatedly calibrate the tracker, which becomes cumbersome for use outside the lab. To address this limitation, we developed an unsupervised algorithm that maps gaze vectors from a webcam to fxation features used for user modeling, bypassing the need for screen-based gaze coordinates, which require a calibration process. We evaluated our approach using three datasets (N=377) encompassing diferent UIs (computerized reading, an Intelligent Tutoring System), environments (laboratory or the classroom), and a traditional gaze tracker used for comparison. Our research shows that webcam-based gaze features correlate moderately with eye-tracker-based features and can model user engagement and comprehension as accurately as the latter. We discuss applications for research and gaze-enabled user interfaces for long-term use in the wild.

Multi-level vector autoregression (mlVAR) is a recently developed dynamic network model for assessing multimodal temporal data streams derived from multiple users over time. Importantly, mlVAR facilitates investigations into highly complex collaborative interactions within a unifed framework. In order to demonstrate the utility of mlVAR for understanding the temporal dynamics of multimodal multi-party (MMP) interactions, we apply it to 9 signals measured from 201 users (67 triads) who engaged in a 15-minute collaborative problem solving task. Measured signals refect participants' afective states (positive valence and negative valence), physiological states (skin conductance and heart rate), attention (gaze fxation duration and gaze dispersion), nonverbal communication (head acceleration and facial expressiveness), and verbal communication (speech rate). Using node-level metrics of in-strength, out-strength, and synchrony, we show that mlVAR is capable of teasing apart complex role-based dynamics (controller, primary contributor, or secondary contributor) between participants. Our fndings also provide evidence for a complex feedback system between individuals where internal states (i.e., skin conductance) are infuenced by external signals of shared attention and communication (i.e., gaze and speech).

Apparel product development is an iterative problem-solving process that is heuristic in nature and involves turning 2D flat patterns into a 3D garment that would fit human anatomy. The digital transformation trend in the apparel industry will accelerate in the post-pandemic era. Therefore, it is crucial to better understand the dynamics as well as the types of information generated during apparel product development to translate this information into the digital realm to better support the apparel industry. Technical apparel designers acquire important knowledge on garment fit during their years in the workforce. Documenting their knowledge could assure that the company's know-how would be saved and used to train the future workforce. Technical designers' problem-solving strategies can be tracked, coded, and made a part of artificial intelligence (AI) technology for product development sessions. This would help strengthen the competitiveness of both existing and new design companies. However, the knowledge possessed by the technical designers to enable much-improved digitalization would require a thorough analysis of tacit or implicit knowledge, which is acquired through years of experience. Due to the nature of how apparel designers learn by directly manipulating materials with their hands, this knowledge type is challenging to document. Nonetheless, if a successful method can be developed to document this knowledge, it would be very rewarding, especially for organizational knowledge management, and allow companies to digitalize using AI.

Historically, teachers had been delegated the primary responsibility for the organization and management of classroom instruction in US public schools. While this delegation afforded teachers professional autonomy in their work, it has also resulted in disparities in students’ educational experiences and outcomes within and between classrooms, schools, and systems. In the effort to improve instruction and reduce disparities for students on a large scale, one reform effort in the US has focused on building instructionally focused education systems (IFESs) where central office and school leaders collaborate with teachers to organize and manage instruction. These efforts are playing out in a variety of contexts in the US, including in public school districts, non-profits, and other educational networks, and it is shifting how teachers carry out the day-to-day work of instruction. In this comparative case study, we investigate two IFESs in which efforts to improve instruction pushed against historic norms of teacher autonomy. We found that these new systems are not at odds with teacher autonomy, but rather these systems reflect a transition to more interdependent notions of teacher autonomy.

Background: To stem the tide of teacher turnover and prevent shortages, teacher turnover interventions and policies often focus on new and novice teachers because evidence suggests that teacher turnover is particularly high among these teachers. In addition, researchers continue to investigate the root causes of the high teacher turnover observed in many low-income, high-minority schools and whether this is due more to school demographics or poor working conditions. Purpose: This article examines New York City Teaching Fellows (NYCTF) teachers' risk of leaving their first school in their first 9 years. It both describes the patterns in leaving and examines how school demographics and school climate predict these leaving patterns. Participants: The study follows 608 teachers: two cohorts of secondary mathematics NYCTF teachers who entered the classroom in New York City in 2006 or 2007. Research Design: This is a quantitative study of survey and retention data that were collected as part of a longitudinal research project on NYCTF mathematics teachers. Data Analysis: We use an event history analysis (including a life table and hazard function graphs) to describe patterns in teachers' timing of leaving their first school. We also use a discrete time hazard model to estimate the relative relationships between the predictors of interest (school demographics and school climate) and teacher turnover. Results: The findings from this study provide evidence against the general hypothesis in the field that teachers leave their first schools at the highest rate during their first 1 to 3 years. Second, we also found that the turnover of alternatively certified teachers who began in low-income, high-minority urban schools was driven by both student demographics and school climate conditions, including teacher collegiality and student behavior. Third, we found evidence to support our hypothesis that teachers' individual perceptions of their school environment are stronger drivers of their turnover compared with the perceptions of their colleagues. Conclusion: The results from this study add to our understanding about the timing of teacher turnover among secondary mathematics NYCTF teachers, illustrating that teacher turnover may remain higher later in beginning teachers' careers than currently assumed. This suggests that teachers in Years 3 to 5 in their careers may be good targets for supports. Our findings support the theory that improving school climate can help retain teachers but also provide a cautionary tale for a complete focus on school climate; stemming teacher turnover may require addressing larger economic forces (e.g., the global trend toward temporary work) and more insidious social forces, such as structural racism and inequality.

Social support has a well-documented impact on adolescent educational success. Nonetheless, there has been less focus on the relationship between social supports and educational attainment for Latinas. Using a sample of 138 Hispanic females (ages 25-31) from an ongoing longitudinal National Science Foundation (NSF)-funded study (2004-present), we identified key sources of social support (family, teachers, and peers) and types of social support (emotional, informational, and instrumental) in the educational pipeline of Hispanic females. We also examined the associations between social supports and their educational attainment. Through descriptive analyses, we found that family was perceived to provide the most support followed by peers and teachers in adolescence. Through regression analyses, we found that family support positively predicted their educational attainment. Our findings highlight the importance of family-rendered support for educational attainment while also expanding our understanding of the social support mechanisms for Hispanic females.

The aim of this study was to use sociocultural perspectives to elaborate on Eccles' parent socialization model and create a culturally grounded, multidimensional model of parent support among Mexican-descent families. Given Latinx underrepresentation in science, technology, engineering, and mathematics careers, we focus on science as an important domain in which to study parent support. Using a qualitative approach, this study examines (a) what forms of parent science support do Mexican-descent parents and adolescents perceive as best practices and (b) what are the social, cultural, and contextual barriers parents face and in what ways do parents continue to support their adolescents in science in spite of those barriers? Seventy-four parent (mean age: 40 years; 23% U.S.-born and 77% Mexico-born) and 73 adolescent (mean age: 15 years; 41% female) nterviews were analyzed using inductive and deductive approaches. Findings suggest that parents use traditional and nontraditional culturally grounded forms of support: involvement at home, providing words of encouragement (e.g.,echale ganas), and leveraging resources (e.g., kin support). Participants felt work-related barriers, linguistic barriers, and limited science knowledge shaped parents' support. Results highlight the unique ways parents support their adolescents' science education as well as the need for educators to consider how parents' sociocultural experiences shape their support.

This study examines the extent to which the New York City Teaching Fellows (NYCTF) has delivered on its promise of improving mathematics teacher diversity, preparedness, effectiveness, and retention in hard-to-staff city schools. As a program theory evaluation study, it articulates the theory of action for selective alternative route programs and uses this to evaluate NYCTF's program for secondary mathematics. The analysis draws on longitudinal data from 620 secondary mathematics teachers who began NYCTF in the prior decade. While the results point to potential improvements, it provides evidence that selective programs like NYCTF serve to maintain important gaps in teacher quality that they were designed to address.

A growth-mindset intervention teaches the belief that intellectual abilities can be developed. Where does the intervention work best? Prior research examined school-level moderators using data from the National Study of Learning Mindsets (NSLM), which delivered a short growth-mindset intervention during the first year of high school. In the present research, we used data from the NSLM to examine moderation by teachers’ mindsets and answer a new question: Can students independently implement their growth mindsets in virtually any classroom culture, or must students’ growth mindsets be supported by their teacher’s own growth mindsets (i.e., the mindset-plus-supportive-context hypothesis)? The present analysis (9,167 student records matched with 223 math teachers) supported the latter hypothesis. This result stood up to potentially confounding teacher factors and to a conservative Bayesian analysis. Thus, sustaining growth-mindset effects may require contextual supports that allow the proffered beliefs to take root and flourish. (PsycInfo Database Record (c) 2022 APA, all rights reserved)