Online Teaching in K-12 Education in the United States: A Systematic Review

The transition to fully or partially online instruction for K–12 students necessitated by the 2020 COVID-19 pandemic highlighted the current lack of understanding of practices that support K–12 student learning in online settings in emergency situations but also, more troublingly, in K–12 online teaching and learning more generally. A systematic review of literature regarding K–12 online teaching and learning in the United States was therefore conducted to begin to fill this gap and to inform the work of policy makers, researchers, teacher educators, teachers, and administrators as they negotiate the changing role of online instruction in our nation's educational systems. The review revealed a set of contextual conditions that are foundational to student learning in K–12 online settings (prepared educators, technology access and autonomy, students' developmental needs and abilities, and students' self-regulated learning skills). The literature also pointed to seven pillars of instructional practice that support student learning in these settings (evidence-based course organization and design, connected learners, accessibility, supportive learning environment, individualization, active learning, and real-time assessment). [ FROM AUTHOR] Copyright of Review of Educational Research is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Evaluation of a Virtual Training to Enhance Public Health Capacity for COVID-19 Infection Prevention and Control in Nursing Homes.

CONTEXT: Between April 2020 and May 2021, the Centers for Disease Control and Prevention (CDC) awarded more than $40 billion to health departments nationwide for COVID-19 prevention and response activities. One of the identified priorities for this investment was improving infection prevention and control (IPC) in nursing homes. PROGRAM: CDC developed a virtual course to train new and less experienced public health staff in core healthcare IPC principles and in the application of CDC COVID-19 healthcare IPC guidance for nursing homes. IMPLEMENTATION: From October 2020 to August 2021, the CDC led training sessions for 12 cohorts of public health staff using pretraining reading materials, case-based scenarios, didactic presentations, peer-learning opportunities, and subject matter expert-led discussions. Multiple electronic assessments were distributed to learners over time to measure changes in self-reported knowledge and confidence and to collect feedback on the course. Participating public health programs were also assessed to measure overall course impact. EVALUATION: Among 182 enrolled learners, 94% completed the training. Most learners were infection preventionists (42%) or epidemiologists (38%), had less than 1 year of experience in their health department role (75%), and had less than 1 year of subject matter experience (54%). After training, learners reported increased knowledge and confidence in applying the CDC COVID-19 healthcare IPC guidance for nursing homes (≥81%) with the greatest increase in performing COVID-19 IPC consultations and assessments (87%). The majority of participating programs agreed that the course provided an overall benefit (88%) and reduced training burden (72%). DISCUSSION: The CDC's virtual course was effective in increasing public health capacity for COVID-19 healthcare IPC in nursing homes and provides a possible model to increase IPC capacity for other infectious diseases and other healthcare settings. Future virtual healthcare IPC courses could be enhanced by tailoring materials to health department needs, reinforcing training through applied learning experiences, and supporting mechanisms to retain trained staff.

Online Teaching in K-12 Education in the United States: A Systematic Review

The transition to fully or partially online instruction for K?12 students necessitated by the 2020 COVID-19 pandemic highlighted the current lack of understanding of practices that support K?12 student learning in online settings in emergency situations but also, more troublingly, in K?12 online teaching and learning more generally. A systematic review of literature regarding K?12 online teaching and learning in the United States was therefore conducted to begin to fill this gap and to inform the work of policy makers, researchers, teacher educators, teachers, and administrators as they negotiate the changing role of online instruction in our nation?s educational systems. The review revealed a set of contextual conditions that are foundational to student learning in K?12 online settings (prepared educators, technology access and autonomy, students? developmental needs and abilities, and students? self-regulated learning skills). The literature also pointed to seven pillars of instructional practice that support student learning in these settings (evidence-based course organization and design, connected learners, accessibility, supportive learning environment, individualization, active learning, and real-time assessment).

End-to-End Encryption and Prediction of Medical Insurance Cost

Health-care costs are rising on a daily basis after the advent of Covid. Most importantly, health issues are becoming more prevalent and critical. As a result, predicting medical insurance cost has become unavoidable as many people choose insurance. However, for a secure system, the entire prediction model for each customer should be encrypted end-to-end. To create a better prediction model, Machine learning regression algorithms are used. The prediction model will be encrypted end-to-end. This paper will give the steps of developing a reliable medical insurance cost prediction model. © 2022 IEEE.

Forecasting COVID-19 Caseloads Using Unsupervised Embedding Clusters of Social Media Posts (preprint)

We present a novel approach incorporating transformer-based language models into infectious disease modelling. Text-derived features are quantified by tracking high-density clusters of sentence-level representations of Reddit posts within specific US states' COVID-19 subreddits. We benchmark these clustered embedding features against features extracted from other high-quality datasets. In a threshold-classification task, we show that they outperform all other feature types at predicting upward trend signals, a significant result for infectious disease modelling in areas where epidemiological data is unreliable. Subsequently, in a time-series forecasting task we fully utilise the predictive power of the caseload and compare the relative strengths of using different supplementary datasets as covariate feature sets in a transformer-based time-series model.

Pre-trained ensemble model for identification of emotion during COVID-19 based on emergency response support system dataset.

The COVID-19 precautions, lockdown, and quarantine implemented throughout the epidemic resulted in a worldwide economic disaster. People are facing unprecedented levels of intense threat, necessitating professional, systematic psychiatric intervention and assistance. New psychological services must be established as quickly as possible to support the mental healthcare needs of people in this pandemic condition. This study examines the contents of calls landed in the emergency response support system (ERSS) during the pandemic. Furthermore, a combined analysis of Twitter patterns connected to emergency services could be valuable in assisting people in this pandemic crisis and understanding and supporting people's emotions. The proposed Average Voting Ensemble Deep Learning model (AVEDL Model) is based on the Average Voting technique. The AVEDL Model is utilized to classify emotion based on COVID-19 associated emergency response support system calls (transcribed) along with tweets. Pre-trained transformer-based models BERT, DistilBERT, and RoBERTa are combined to build the AVEDL Model, which achieves the best results. The AVEDL Model is trained and tested for emotion detection using the COVID-19 labeled tweets and call content of the emergency response support system. This is the first deep learning ensemble model using COVID-19 emotion analysis to the best of our knowledge. The AVEDL Model outperforms standard deep learning and machine learning models by attaining an accuracy of 86.46 percent and Macro-average F1-score of 85.20 percent.

Teaching Mathematics with Technology: TPACK and Effective Teaching Practices

This paper examines how 17 secondary mathematics teacher candidates (TCs) in four university teacher preparation programs implemented technology in their classrooms to teach for conceptual understanding in online, hybrid, and face to face classes during COVID-19. Using the Professional Development: Research, Implementation, and Evaluation (PrimeD) framework, TCs, classroom mentor teachers, field experience supervisors, and university faculty formed a Networked Improvement Community (NIC) to discuss a commonly agreed upon problem of practice and a change idea to implement in the classroom. Through Plan-Do-Study-Act cycles, participants documented their improvement efforts and refinements to the change idea and then reported back to the NIC at the subsequent monthly meeting. The Technology Pedagogical Content Knowledge framework (TPACK) and the TPACK levels rubric were used to examine how teacher candidates implemented technology for Mathematics conceptual understanding. The Mathematics Classroom Observation Protocol for Practices (MCOP2) was used to further examine how effective mathematics teaching practices (e.g., student engagement) were implemented by TCs. MCOP2 results indicated that TCs increased their use of effective mathematics teaching practices. However, growth in TPACK was not significant. A relationship between TPACK and MCOP2 was not evident, indicating a potential need for explicit focus on using technology for mathematics conceptual understanding.