Garron Hillaire

As schools shift to online instruction during the COVID-19 pandemic, it is important to support disenfranchised populations and keep issues of equity at the centre of our response. In this study, the authors focus on supporting one of the few urban-based Indigenous language schools in the United States because language revitalisation is critical for Native American communities. The authors explore the extent to which video conferencing and flipped classrooms support the development of a community of speakers. The study focuses on a single classroom of 16 students in first through third grade. The authors use a digital decolonisation framework focused on empowering local communities in conjunction with design-based research methodology to explore contextualised remote instruction solutions. They report on benefits for the development of a community of speakers from remote instruction that come with costs in reduced efficacy of language learning. Finally, they distil those results into preliminary design principles.

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

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.

We analyze the state education agency policy guidance concerning remote learning published by all 50 U.S. states by the end of March 2020. We find several areas of consensus, including cancellation of testing, recommendations to continue some form of remote learning, attention to digital and non-digital options, and a concerns for providing a fair and appropriate education for students with disabilities. The primary area of policy divergence that we found regarded the purpose of continuous learning during a pandemic: whether to pursue forward progress in standards-aligned new material or whether to pursue skills review and enrichment learning. We recommend that states continue to emphasize equity, consider the particular challenges of home-based learning, and produce concise communications for multiple target audiences.

This paper outlines an innovative approach to evaluating the emotional content of three online courses using the affective computing approach of prosody detection on two different text-to-speech (TTS) voices in conjunction with human raters judging the emotional content of the text. This work intends to establish the potential variation on the emotional delivery of online educational resources through the use of a synthetic voice, which automatically articulates text into audio. Preliminary results from this pilot research suggest that about one out of every three sentences (35%) in a Massive Open Online Course (MOOC) contained emotional text and two existing assistive technology voices had poor emotional alignment when reading this text. Synthetic voices were more likely to be overly negative when considering their expression as compared to the emotional content of the text they are reading, which was most frequently neutral. We also analysed a synthetic voice for which we configured the emotional expression to align with course text, which showed promising improvements.

The wide-scale adoption of massive open online courses (MOOCs) comes with learners that have variable needs. While MOOCs may be attracting a wide range of learners, there is a need to provide those learners with a means to evaluate what is working in MOOCs and what areas of learning design can be improved. While learners may have compliments and criticisms of course designs, there is a need to organize feedback from such a wide range of participants into a coherent and actionable structure. This chapter describes the YourMOOC4all project, which offers the possibility for any learner to freely judge and provide feedback on the design of MOOCs in accordance with how it meets learner needs and Universal Design for Learning (UDL) principles. This kind of user feedback can be of great value for the future development of MOOC platforms, courses, and associated educational resources. YourMOOC4all gathers valuable information directly from the learners themselves to improve aspects such as the educational quality, accessibility, and usability of the learning environment.