An official website of the United States government
Learner-centered interactions, whether in formal or informal settings, are by their nature unscripted and require both the educator and learner to improvise. In fact, improvisation skills have been recognized as beneficial and applied in a variety of professional development training programs (including science communication, organizational development in university administration, teambuilding and leadership in business, and communication skills in medical education); yet, their inclusion in educator training has been limited. MOXI and UCSB partnered with a professional actor and theater instructor (third author of this paper) to implement applied improvisation training to support informal educators' skills development. After four years of incorporating applied improvisation training in our facilitation training program, we have found that the basic skills of listening, observing, and responding that are critical in learner-centered education are taught effectively through the well-developed, practical, and fun exercises of improvisational theater. In this article, we describe our applied improvisation training and how it builds skills pertinent to implementing learner-centered facilitation, how graduates of our training program connected applied improvisation training to their facilitation, and how other institutions can incorporate it into preparing educators for working in either informal or formal settings.
more »
« less
Digital Clinical Simulation Suite: Specifications and Architecture for Simulation-Based Pedagogy at Scale
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.
more »
« less
- Award ID(s):
- 1917668
- PAR ID:
- 10330908
- Date Published:
- Journal Name:
- L@S '22: Proceedings of the Ninth ACM Conference on Learning @ Scale
- Page Range / eLocation ID:
- 212 to 221
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
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.more » « less
-
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.more » « less
-
Seagroves, Scott; Barnes, Austin; Metevier, Anne; Porter, Jason; Hunter, Lisa (Ed.)In teaching for experiential learning, we measure our success not by how well we presented the material or designed an activity but by how well our students learned. Facilitation, the moment-to-moment twists and turns of live interactions between educators and learners, is a critical tool for student learning. Over the 20 years of the Professional Development Program (PDP), we have refined our articulation of the desired learning outcomes and have developed a set of strategies and “moves” that contribute to attaining those outcomes. Here, we examine these as well as describe materials and training developed in the PDP to build the skills of novice facilitators.more » « less
-
Abstract To date, many AI initiatives (eg, AI4K12, CS for All) developed standards and frameworks as guidance for educators to create accessible and engaging Artificial Intelligence (AI) learning experiences for K‐12 students. These efforts revealed a significant need to prepare youth to gain a fundamental understanding of how intelligence is created, applied, and its potential to perpetuate bias and unfairness. This study contributes to the growing interest in K‐12 AI education by examining student learning of modelling real‐world text data. Four students from an Advanced Placement computer science classroom at a public high school participated in this study. Our qualitative analysis reveals that the students developed nuanced and in‐depth understandings of how text classification models—a type of AI application—are trained. Specifically, we found that in modelling texts, students: (1) drew on their social experiences and cultural knowledge to create predictive features, (2) engineered predictive features to address model errors, (3) described model learning patterns from training data and (4) reasoned about noisy features when comparing models. This study contributes to an initial understanding of student learning of modelling unstructured data and offers implications for scaffolding in‐depth reasoning about model decision making. Practitioner notesWhat is already known about this topicScholarly attention has turned to examining Artificial Intelligence (AI) literacy in K‐12 to help students understand the working mechanism of AI technologies and critically evaluate automated decisions made by computer models.While efforts have been made to engage students in understanding AI through building machine learning models with data, few of them go in‐depth into teaching and learning of feature engineering, a critical concept in modelling data.There is a need for research to examine students' data modelling processes, particularly in the little‐researched realm of unstructured data.What this paper addsResults show that students developed nuanced understandings of models learning patterns in data for automated decision making.Results demonstrate that students drew on prior experience and knowledge in creating features from unstructured data in the learning task of building text classification models.Students needed support in performing feature engineering practices, reasoning about noisy features and exploring features in rich social contexts that the data set is situated in.Implications for practice and/or policyIt is important for schools to provide hands‐on model building experiences for students to understand and evaluate automated decisions from AI technologies.Students should be empowered to draw on their cultural and social backgrounds as they create models and evaluate data sources.To extend this work, educators should consider opportunities to integrate AI learning in other disciplinary subjects (ie, outside of computer science classes).more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3491140.3528276
