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1. Using Teacher Moments During the COVID-19 Pivot.

   Sullivan, Florence; Garron Hillaire; Laura Larke; and Justin Reich. (April 2020, Journal of technology and teacher education)

   Teacher Moments is an open source resource for teacher educators to create and use practice-based simulations in teacher education. Teacher Moments may be used to create digital clinical simulations (DCS) which are defined as opportunities for improvisational interaction with scripted character(s). During the COVID-19 crisis, we implemented an equity-based simulation created by a teacher educator. Results demonstrate the utility of the system for surfacing student perspectives which, in turn, provides opportunities for deeper discussion and reflection. 

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2. Investigating Adoption and Collaboration with Digital Clinical Simulations by Teacher Educators.

   Dutt, Ritam; Hillaire, Garron; Fang, Alison; Larke, Laura; Rosé, Carolyn; Reich, Justin (January 2021, Proceedings of the 2021 Society for Information Technology and Teacher Education (SITE) Conference)

   Langran, E.; Archambault, L. (Ed.)

   In this study, we examine the outcome of a four-day workshop with 24 Teacher Educators (fellows) who were supported in using two tools - Teacher Moments (TM) and Eliciting Learner Knowledge (ELK). The tools are designed for authoring, implementing, and research Digital Clinical Simulations in education. The simulations centered around issues of equity in K-12 computer science education to provide in-/pre-service teachers with opportunities to practice high-stakes interactions in low-stakes settings. We operationalize the technology adoption of the fellows through the notions of self-efficacy, help-seeking, and technology concerns to recognize the potential barriers they faced in transitioning from authoring to implementing and research design. Finally, we note the fellows' implementation plans in the ensuing academic year and examine potential collaborations amongst them using social network analysis. Our results reveal how a small group of fellows, spanning major regions of the U.S., generate a broad range of scenarios, as well as clusters of scenarios, enabling simulation-based research supported by collaboration. 

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3. Digital Storytelling through Authoring Simulations with Teacher Moments

   Hillaire, G; Larke, L.; Reich, J. (April 2020, Society for Information Technology & Teacher Education International Conference)

   In this paper we explore how to support teacher educators to author their own digital clinical simulations to prepare K-12 pre-service computer science teachers. Teacher educators have the potential to create simulations about relevant content for their teacher preparation programs and contextualize those simulations for their students. To benefit from this unique perspective, we support teacher educators in authoring simulations. We consider the relationship between authoring simulations and digital storytelling to inform our authoring tools and supports. In this study, we report results on what kinds of supports are needed for authoring simulations based on a co-design workshop with 12 teacher educators from nine U.S. states across all regions of the country. We examine how these authors set context, select content, and engage in the simulation authoring process to gain insight into supporting teacher educators as digital storytellers 

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4. Supporting Elementary Teacher's Reflections on Equity in CS Education: A Case Study Approach

   https://doi.org/10.1145/3291279.3341208  

   Sullivan, Florence; Tulungen, Catherine; Veeragoudar, Sneha; Pektas, Emrah (August 2019, Proceedings of the 2019 ACM Conference on International Computing Education Research)

   The dearth of women and people of color in the field of computer science is a well-documented phenomenon. Following Obama's 2016 declaration of the need for a nationwide CS for All movement in the US, educators, school districts, states and the US-based National Science Foundation have responded with an explosion of activity directed at developing computer science learning opportunities in K-12 settings. A major component of this effort is the creation of equitable CS learning opportunities for underrepresented populations. As a result, there exists a strong need for educational research on the development of equity-based theory and practice in CS education. This poster session reports on a work-in-progress study that uses a case study approach to engage twenty in-service elementary school teachers in reflecting on issues of equity in CS education as part of a three-day CS professional development workshop. Our work is unfolding in the context of a four-year university/district research practice partnership in a mid-sized city in the Northeastern United States. Teachers in our project are working to co-design integrated CS curriculum units for K-5 classrooms. We developed four case studies, drawn from the first year of our project, that highlight equity challenges teachers faced in the classroom when implementing the CS lessons. The case studies follow the "Teacher Moments" template created by the Teaching Systems Lab in Open Learning at MIT. The case study activity is meant to deepen reflection and discussion on how to create equitable learning opportunities for elementary school students. We present preliminary findings. 

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5. mRisk: Continuous Risk Estimation for Smoking Lapse from Noisy Sensor Data with Incomplete and Positive-Only Labels

   https://doi.org/10.1145/3550308  

   Ullah, Md Azim; Chatterjee, Soujanya; Fagundes, Christopher P.; Lam, Cho; Nahum-Shani, Inbal; Rehg, James M.; Wetter, David W.; Kumar, Santosh (September 2022, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies)

   Passive detection of risk factors (that may influence unhealthy or adverse behaviors) via wearable and mobile sensors has created new opportunities to improve the effectiveness of behavioral interventions. A key goal is to find opportune moments for intervention by passively detecting rising risk of an imminent adverse behavior. But, it has been difficult due to substantial noise in the data collected by sensors in the natural environment and a lack of reliable label assignment of low- and high-risk states to the continuous stream of sensor data. In this paper, we propose an event-based encoding of sensor data to reduce the effect of noises and then present an approach to efficiently model the historical influence of recent and past sensor-derived contexts on the likelihood of an adverse behavior. Next, to circumvent the lack of any confirmed negative labels (i.e., time periods with no high-risk moment), and only a few positive labels (i.e., detected adverse behavior), we propose a new loss function. We use 1,012 days of sensor and self-report data collected from 92 participants in a smoking cessation field study to train deep learning models to produce a continuous risk estimate for the likelihood of an impending smoking lapse. The risk dynamics produced by the model show that risk peaks an average of 44 minutes before a lapse. Simulations on field study data show that using our model can create intervention opportunities for 85% of lapses with 5.5 interventions per day. 

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