Teacher Moments is an open source platform that allows the authoring of simulations used for education which we recently revised to integrate intelligent coaching agents. The initial simulation development for Teacher Moments focused on teacher education, but the platform is actively used for professional development with nurses, psychologists, police officers, judges, and attorneys. Simulations can range in complexity from single-user simulations to multi-user role-play simulations. Single-user simulations provide opportunities for participants to respond using text or audio inputs while multiuser simulations extend those response types to include chat functionality. To support participant learning, Teacher Moments simulations can now be configured to include intelligent coaching agents that review participant inputs, identify salient patterns in text or speech, and respond with feedback and coaching supports. Teacher Moments can be configured to incorporate text or audio binary classifiers or include conversational agents into the chat feature. Once a classifier is configured there is functionality to dynamically display content based on audio or text classification when authoring the simulation. In addition, conversational agents can interject comments into the chat directed at either a particular participant or to all participants in a chat. Finally, there is a new integrated labeling component that supports collecting binary labels from participants for text or audio data, which can be used either to validate the accuracy of a classifier or to establish training data for a classifier. In this demo, we will: 1) highlight GitHub repositories designed to support the deployment of classifiers that can be integrated into Teacher Moments; 2) demonstrate a conversational agent integrated into the chat feature to provide intelligent supports; 3) illustrate how binary classification can trigger the dynamic display of content providing options for dynamic learning supports; and 4) demonstrate how the labeling component can be used for either validation of a classifier or collection of training data.
Eliciting and interpreting students’ ideas are essential skills in teaching, yet pre-service teachers (PSTs) rarely have adequate opportunities to develop these skills. In this study, we examine PSTs’ patterns of discourse and perceived learning through engaging in an interactive digital simulation called Eliciting Learner Knowledge (ELK). ELK is a seven-minute, chat-based virtual role play between a PST playing a “teacher” and a PST playing a “student” where the goal is for the teacher to find out what the student knows about a topic. ELK is designed to be a practice space where pre-service and in-service teachers can learn strategies for effectively eliciting their students’ knowledge. We review the implementation of ELK in eight teacher education courses in math or science methods at six different universities and assess (a) patterns of interaction during ELK and (b) PSTs’ perceptions of ELK and their learning from the simulation. Our findings suggest that PSTs engage in effective practices such as eliciting and probing more often than less effective practices such as evaluating and telling. Results suggest that PSTs gain experience in practicing talk moves and having empathy for students’ perspectives through using ELK.
Role-plays of interpersonal interactions are essential to learning across professions, but effective simulations are difficult to create in typical learning management systems. To empower educators and researchers to advance simulation-based pedagogy, we have developed the Digital Clinical Simulation Suite (DCSS, pronounced “decks”), an open-source platform for rehearsing for improvisational interactions. Participants are immersed in vignettes of professional practice through video, images, and text, and they are called upon to improvisationally make difficult decisions through recorded audio and text. Tailored data displays support participant reflection, instructional facilitation, and educational research. DCSS is based on six design principles: 1) Community Adaptation, 2) Masked Technical Complexity, 3) Authenticity of Task, 4) Improvisational Voice, 5) Data Access through “5Rs”, and 6) Extensible AI Coaching. These six principles mean that any educator should be able to create a scenario that learners should engage in authentic professional challenges using ordinary computing devices, and learners and educators should have access to data for reflection, facilitation, and development of AI tools for real-time feedback. In this paper, we describe the architecture of DCSS and illustrate its use and efficacy in cases from online courses, colleges of education, and K-12 schools. Forthcoming in the Proceedings of the 2022 ACM Learning@Scale Conference
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