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Trust calibration poses a significant challenge in the interaction between drivers and automated vehicles (AVs) in the context of human-automation collaboration. To effectively calibrate trust, it becomes crucial to accurately measure drivers’ trust levels in real time, allowing for timely interventions or adjustments in the automated driving. One viable approach involves employing machine learning models and physiological measures to model the dynamic changes in trust. This study introduces a technique that leverages machine learning models to predict drivers’ real-time dynamic trust in conditional AVs using physiological measurements. We conducted the study in a driving simulator where participants were requested to take over control from automated driving in three conditions that included a control condition, a false alarm condition, and a miss condition. Each condition had eight takeover requests (TORs) in different scenarios. Drivers’ physiological measures were recorded during the experiment, including galvanic skin response (GSR), heart rate (HR) indices, and eye-tracking metrics. Using five machine learning models, we found that eXtreme Gradient Boosting (XGBoost) performed the best and was able to predict drivers’ trust in real time with an f1-score of 89.1% compared to a baseline model of K -nearest neighbor classifier of 84.5%. Our findings provide good implications on how to design an in-vehicle trust monitoring system to calibrate drivers’ trust to facilitate interaction between the driver and the AV in real time. 

This research paper measures college students' sense of belonging. Students' sense of belonging (SB) has been identified as a critical contributor to engineering students’ persistence, academic success, and professional identity in engineering. Therefore, how to accurately measure SB has become an emerging topic but is still challenging. Although engineering education researchers are interested in measuring students’ SB, they have presented concerns over selecting an appropriate instrument that results in trustworthy measurement outcomes. One of the reasons that cause challenges is that SB is a complicated construct that has various conceptual definitions. For example, Goodenow (1993) defined SB as “being accepted, valued, included, and encouraged by others...feeling oneself to be an important part of the life and activity of the class” (p. 25), which can be measured as a general SB. On the other hand, Freeman et al. (2007) viewed SB as a multi-dimensional construct that includes class belonging, university belonging, professors’ pedagogical caring, and social acceptance. Thus far, several instruments have been developed to measure SB from a single-dimensional perspective (e.g., Goodenow’s Psychological Sense of School Membership) and a multi-dimensional perspective (e.g., Slaten et al.’s the University Belonging Questionnaire). To our best knowledge, little research effort has been made to synthesize the information of instruments developed for measuring college students’ SB. This paper attempts to close the gap in the literature by conducting a systematic review following PRISMA (the Preferred Reporting Items for Systematic reviews and Meta-Analyses) guidelines to summarize the information and characteristics of existing SB instruments, including the theoretical framework underlying the instrument, psychometric properties in previous studies, and validation works that have been carried out. Specifically, this paper focuses on the following aims: (a) to summarize how SB has been constructed and defined by different theories in higher education, (b) to report existing measurement instruments of SB used in higher education and their psychometric properties (reliability and validity), and (c) to compare various analytical plans for establishing the construct validity (including multicultural validity) in prior instrument development studies. The emergent findings provide insights into how to effectively measure SB and would facilitate school leaders' and educators’ work in promoting engineering students’ success and broadening participation in engineering. Keywords: Sense of belonging, engineering education, instrument, systematic review