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.

Personalisation of learning is a recurring trend in our society, referred to in government speeches, popular media, conference and research papers and technological innovations. This latter aspect-of using personalisation in technology-enhanced learning (TEL)-has promised much but has not always lived up to the claims made. Personalisation is often perceived to be a positive phenomenon, but it is often difficult to know how to implement it effectively within educational technology.In order to address this problem, we propose a framework for the analysis and creation of personalised TEL. This article outlines and explains this framework with examples from a series of case studies. The framework serves as a valuable resource in order to change or consolidate existing practice and suggests design guidelines for effective implementations of future personalised TEL. IntroductionPersonalization is a key topic of current interest in technology-oriented learning design and discussion for government policy makers, but less so in educational research. This paper develops a framework to support the design of technology-enhanced learning (TEL) resources and environment.

Technology makes possible abundant new opportunities to capture and display data in online learning environments. We describe here an example of using these opportunities to improve students' use of the rich supports available in online learning environments. We describe an example of a blended learning experience that uses an online inquiry‐based middle‐school science curriculum in which we provided sixth graders (n = 126) data aligned with the Universal Design for Learning instructional framework. Students were provided and asked to reflect on their own data not only about performance but also about use of optional embedded supports vis‐à‐vis their perceptions of difficulty of key concepts. We determined that students were generally able to understand and interpret these rich data and that providing these data influenced subsequent help seeking in the online environment. We discuss implications for supporting help seeking and designing assessment and feedback within online learning environments.

The influence of internationalised versus local content on online intercultural collaboration in groups: A randomised control trial study in a statistics course. Computers & Education, 118 pp. 82-95. For guidance on citations see FAQs.

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.