More Like this

1. Longitudinal dataset of human-building interactions in U.S. offices

   https://doi.org/10.1038/s41597-019-0273-5  

   Langevin, Jared ( November 2019 , Scientific Data)

   Adaptive interactions between building occupants and their surrounding environments affect both energy use and environmental quality, as demonstrated by a large body of modeling research that quantifies the impacts of occupant behavior on building operations. Yet, available occupant field data are insufficient to explore the mechanisms that drive this interaction. This paper introduces data from a one year study of 24 U.S. office occupants that recorded a comprehensive set of possible exogenous and endogenous drivers of personal comfort and behavior over time. The longitudinal data collection protocol merges individual thermal comfort, preference, and behavior information from online daily surveys with datalogger readings of occupants’ local thermal environments and control states, yielding 2503 survey responses alongside tens of thousands of concurrent behavior and environment measurements. These data have been used to uncover links between the built environment, personal variables, and adaptive actions, and the data contribute to international research collaborations focused on understanding the human-building interaction.
2. Immersive Virtual Reality Training Module for Active Shooter Events

   https://doi.org/10.2352/EI.2022.34.12.ERVR-299  

   Sharad Sharma, Sri Teja ( January 2022 , Proceedings of the IS&T International Symposium on Electronic Imaging (EI 2022), in the Engineering Reality of Virtual Reality)

   Active shooter events are not emergencies that can be reasonably anticipated. However, these events do occur more than we think, and there is a critical need for an effective emergency preparedness plan that can increase the likelihood of saving lives and reducing casualties in the event of an active shooting incident. There has been a major concern about the lack of tools available to allow for modeling and simulation of human behavior during emergency response training. Over the past few decades, virtual reality-based training for emergency response and decision making has been recognized as a novel alternative for disaster preparedness. This paper presents an immersive virtual reality (VR) training module for active shooter events for a building emergency response. There are two immersive active shooter modules developed: occupant’s module and Security personnel module. We have developed an immersive virtual reality training module for active shooter events using an Oculus for the course of action, visualization, and situational awareness for active shooter events. The immersive environment is implemented in Unity 3D where the user has an option to enter the environment as security personnel or as an occupant in the building. The immersive VR training module offers a unique platform formore »emergency response and decision making training. The platform allows for collecting data on different what-if scenarios in response to active shooter events that impact the actions of security personnel and occupants in a building. The data collected can be used to educate security personnel on how to reduce response times. Moreover, security personnel can be trained to respond to a variety of emergencies safely and securely without ever being exposed to real-world dangers.« less
3. A Global Building Occupant Behavior Database

   https://doi.org/10.1038/s41597-022-01475-3  

   Dong, Bing ; Liu, Yapan ; Mu, Wei ; Jiang, Zixin ; Pandey, Pratik ; Hong, Tianzhen ; Olesen, Bjarne ; Lawrence, Thomas ; O’Neil, Zheng ; Andrews, Clinton ; et al ( December 2022 , Scientific Data)

   Abstract This paper introduces a database of 34 field-measured building occupant behavior datasets collected from 15 countries and 39 institutions across 10 climatic zones covering various building types in both commercial and residential sectors. This is a comprehensive global database about building occupant behavior. The database includes occupancy patterns (i.e., presence and people count) and occupant behaviors (i.e., interactions with devices, equipment, and technical systems in buildings). Brick schema models were developed to represent sensor and room metadata information. The database is publicly available, and a website was created for the public to access, query, and download specific datasets or the whole database interactively. The database can help to advance the knowledge and understanding of realistic occupancy patterns and human-building interactions with building systems (e.g., light switching, set-point changes on thermostats, fans on/off, etc.) and envelopes (e.g., window opening/closing). With these more realistic inputs of occupants’ schedules and their interactions with buildings and systems, building designers, energy modelers, and consultants can improve the accuracy of building energy simulation and building load forecasting.
4. Fostering a sense of belonging in STEM mentoring of underrepresented minority (URM) students Through use of Common Factors. The Chronicles of Mentoring and Coaching

   Stallsmith V. and Chavez E. ( December 2022 , The chronicle of mentoring coaching)

   ABSTRACT Over the last 30 years over 30,000 articles and chapters have been published related to mentoring, with over 40% focused on mentoring students in STEM disciplines. What have we learned from this voluminous literature and what concepts stand out as needing further attention? A review of the literature indicates that mentoring of underrepresented minoritized (URM) students involve attention to the professional development of these students, active engagement in research activities, and a willingness and ability to develop a strong relationship that supersedes the aspects of traditional mentoring activities. Psychology graduate programs have long been known to teach and develop the skills necessary to help students foster strong therapeutic relationships. The foundational interpersonal skills taught in domains of psychology (e.g., counseling psychology, social psychology) are directly relevant to other relationship-building scenarios, such as mentor/mentee dyads. Budding psychologists typically learn therapeutic techniques that help build trusting relationships with clients that hold different identities than their own. But these skills apply beyond client/therapist relations and could be used to inform intensive/inclusive mentoring approaches with URM students, especially when the mentor holds a different identity. The training techniques proposed can be adapted for both formal and informal forms of mentoring and may enhancemore »a student’s sense of belonging, which is the strongest predictor of science identity development and success in STEM. This paper will focus on elements necessary to develop a strong relationship between URM students and their mentors based on the development of a therapeutic relationship using concepts from theories related to the Common Factors (Rosenzweig, 1936). These theories posit that the development of a meaningful client/therapist relationship and behavior change requires attention to four common factors: therapist qualities or in this case mentor qualities, change processes or how students are trained, treatment structures which are specific techniques, and development of a strong relationship. These factors can easily be applied to create a truly inclusive mentoring model.« less
5. Intelligence Augmentation for Collaborative Learning

   Roschelle, Jeremy ( January 2021 , HCI International)

   Today’s classrooms are remarkably different from those of yesteryear. In place of individual students responding to the teacher from neat rows of desks, one more typically finds students working in groups on projects, with a teacher circulating among groups. AI applications in learning have been slow to catch up, with most available technologies focusing on personalizing or adapting instruction to learners as isolated individuals. Meanwhile, an established science of Computer Supported Collaborative Learning has come to prominence, with clear implications for how collaborative learning could best be supported. In this contribution, I will consider how intelligence augmentation could evolve to support collaborative learning as well as three signature challenges of this work that could drive AI forward. In conceptualizing collaborative learning, Kirschner and Erkens (2013) provide a useful 3x3 framework in which there are three aspects of learning (cognitive, social and motivational), three levels (community, group/team, and individual) and three kinds of pedagogical supports (discourse-oriented, representation-oriented, and process-oriented). As they engage in this multiply complex space, teachers and learners are both learning to collaborate and collaborating to learn. Further, questions of equity arise as we consider who is able to participate and in which ways. Overall, this analysis helps usmore »see the complexity of today’s classrooms and within this complexity, the opportunities for augmentation or “assistance to become important and even essential. An overarching design concept has emerged in the past 5 years in response to this complexity, the idea of intelligent augmentation for “orchestrating” classrooms (Dillenbourg, et al, 2013). As a metaphor, orchestration can suggest the need for a coordinated performance among many agents who are each playing different roles or voicing different ideas. Practically speaking, orchestration suggests that “intelligence augmentation” could help many smaller things go well, and in doing so, could enable the overall intention of the learning experience to succeed. Those smaller things could include helping the teacher stay aware of students or groups who need attention, supporting formation of groups or transitions from one activity to the next, facilitating productive social interactions in groups, suggesting learning resources that would support teamwork, and more. A recent panel of AI experts identified orchestration as an overarching concept that is an important focus for near-term research and development for intelligence augmentation (Roschelle, Lester & Fusco, 2020). Tackling this challenging area of collaborative learning could also be beneficial for advancing AI technologies overall. Building AI agents that better understand the social context of human activities has broad importance, as does designing AI agents that can appropriately interact within teamwork. Collaborative learning has trajectory over time, and designing AI systems that support teams not just with a short term recommendation or suggestion but in long-term developmental processes is important. Further, classrooms that are engaged in collaborative learning could become very interesting hybrid environments, with multiple human and AI agents present at once and addressing dual outcome goals of learning to collaborate and collaborating to learn; addressing a hybrid environment like this could lead to developing AI systems that more robustly help many types of realistic human activity. In conclusion, the opportunity to make a societal impact by attending to collaborative learning, the availability of growing science of computer-supported collaborative learning and the need to push new boundaries in AI together suggest collaborative learning as a challenge worth tackling in coming years.« less