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Background and Context: Professional development (PD) programs for K-12 computer science teachers use surveys to measure teachers’ knowledge and attitudes while recognizing daily sentiment and emotion changes can be crucial for providing timely teacher support. Objective: We investigate approaches to compute sentiment and emotion scores automatically and identify associations between the scores and teachers’ performance. Method: We compute the scores from teachers’ assignments using a machine-assisted tool and measure score changes with standard deviation and linear regression slopes. Further, we compare the scores to teachers’ performance and post-PD qualitative survey results. Findings: We find significant associations between teachers’ sentiment and emotion scores and their performance across demographics. Additionally, we find significant associations that are not captured by post-PD qualitative surveys. Implications: The sentiment and emotion scores can viably reflect teachers’ performance and enrich our understanding of teachers’ learning behaviors. Further, the sentiment and emotion scores can complement conventional surveys with additional insights related to teachers’ learning performance. 

This study provides practical suggestions for the features to be prioritized in spending limited resources to create and improve educational technology like Cell Collective. The results suggest a need to prioritize features improving the learning rather than the teaching side to motivate instructors more effectively to adopt and use the technology.

 

Broadening participation in computer science (CS) for primary/elementary students is a growing movement, spurred by computing workforce demands and the need for younger students to develop skills in problem solving and critical/computational thinking. However, offering computer science instruction at this level is directly related to the availability of teachers prepared to teach the subject. Unfortunately, there are relatively few primary/elementary school teachers who have received formal training in computer science, and they often self-report a lack of CS subject matter expertise. Teacher development is a key factor to address these issues, and this paper describes professional development strategies and empirical impacts of a summer institute that included two graduate courses and a series of Saturday workshops during the subsequent academic year. Key elements included teaching a high-level programing language (Python and JavaScript), integrating CS content and pedagogy instruction, and involving both experienced K-12 CS teachers and University faculty as instructors. Empirical results showed that this carefully structured PD that incorporated evidence-based elements of sufficient duration, teacher active learning and collaboration, modeling, practice, and feedback can successfully impact teacher outcomes. Results showed significant gains in teacher CS knowledge (both pedagogy and content), self-efficacy, and perception of CS value. Moderating results - examining possible differential effects depending on teacher gender, years of teaching CS, and geographic locale - showed that the PD was successful with experienced and less experienced teachers, with teachers from both rural and urban locales, and with both males and females. 

Increasingly professional development (PD) programs have been designed and implemented for pre-service and in-service teachers to acquire CS content knowledge and CS pedagogy and instructional strategies for K-12 students. This paper reports on our adaptation, implementation and research program for K-8 CS teachers across a Midwestern state. More specifically, its PD program for K-8 CS teachers consists of a summer institute with two graduate courses and a series of Saturday workshops during the subsequent academic year. This paper focuses on the two summer courses: one on CS knowledge content including computational thinking, variables, conditionals, loops, arrays, functions, and algorithms, and one instructional strategies, student pedagogy, computer-aided education resources, and community building. We report our SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis of the two summer institutes involving the two courses to identify what went well and what needed improvement. This paper also reviews best practices for summer PD. 

The Adapt, Implement, and Research at Nebraska (AIR@NE) project, funded by the NSF CSforAll Researcher-Practitioner Partnership (RPP) program, examines the adaptation of a validated K-8 Computer Science (CS) curriculum in diverse school districts statewide. Our Research-Practitioner Partnership is primarily between the University of Nebraska-Lincoln, the Lincoln Public Schools, and other diverse school districts across Nebraska. Our primary goal is to study and document how different districts, including rural, predominantly minority, and Native American reservation, adopt the curriculum and broaden participation in CS. In addition, the project is developing instructional capacity for K-8 CS education with diverse learners. Our research also adapts and develops teacher and student CS assessments, and documents case studies using design-based research methodology to show how an adaptive curriculum broadens CS participation. Our Professional Development (PD) program for K-8 CS teachers is comprehensive. It consists of three summer courses for each cohort and a series of workshops during the academic year. Of the three summer courses, two are administered in the first year for a cohort: (1) an introduction to computer science course where teachers learn fundamental CS topics and programming in a high-level programming language (e.g., Python), and engage in problem solving and practice computational thinking, and (2) a course in pedagogy for teachers to learn how to teach K-8 CS, including lesson designs, use of instructional resources such as dot-and-dash robots, and assessments. Then, the following academic year after the summer, the PD program holds a series of workshops on five separate Saturdays to support teacher implementation of their lesson modules during the academic year, reflect and improve on their lessons, reinforce on CS concepts and pedagogy techniques, review and adopt alternative instructional resources, and share insights. These Saturday workshops also facilitate further community building and resource sharing. The third course occurs in the second year for a cohort, involving dissemination of research results from the team to the teachers, opportunities to discuss new resources and approaches on teaching CS concepts and computational thinking, and sharing of experiences and insights after teachers have completed one academic year of teaching CS. Unlike the first two courses that are required of teachers, this third course is an opt-in course that combines more in- depth pedagogy and elements of leadership. Thus far, we have had two cohorts and used the design methodology to revise our PD program, making our design more robust based on the lessons learned over the two years. The course materials, assessment, and survey instruments have also been improved. While the project is on-going we have data to that indicates the impact of the work so far. There were significant pre-post gains for both cohorts in teachers’ knowledge of computer science concepts and computational thinking. Scores on the computational thinking assessment were higher than those for CS concepts, which was to be expected given their CS teaching experience. Moreover, in both cohorts, the teachers’ confidence in teaching CS improved significantly. 

Abstract Background Wastewater-based epidemiology (WBE) for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can be an important source of information for coronavirus disease 2019 (COVID-19) management during and after the pandemic. Currently, governments and transportation industries around the world are developing strategies to minimize SARS-CoV-2 transmission associated with resuming activity. This study investigated the possible use of SARS-CoV-2 RNA wastewater surveillance from airline and cruise ship sanitation systems and its potential use as a COVID-19 public health management tool. Methods Aircraft and cruise ship wastewater samples (n = 21) were tested for SARS-CoV-2 using two virus concentration methods, adsorption–extraction by electronegative membrane (n = 13) and ultrafiltration by Amicon (n = 8), and five assays using reverse-transcription quantitative polymerase chain reaction (RT-qPCR) and RT-droplet digital PCR (RT-ddPCR). Representative qPCR amplicons from positive samples were sequenced to confirm assay specificity. Results SARS-CoV-2 RNA was detected in samples from both aircraft and cruise ship wastewater; however concentrations were near the assay limit of detection. The analysis of multiple replicate samples and use of multiple RT-qPCR and/or RT-ddPCR assays increased detection sensitivity and minimized false-negative results. Representative qPCR amplicons were confirmed for the correct PCR product by sequencing. However, differences in sensitivity were observed among molecular assays and concentration methods. Conclusions The study indicates that surveillance of wastewater from large transport vessels with their own sanitation systems has potential as a complementary data source to prioritize clinical testing and contact tracing among disembarking passengers. Importantly, sampling methods and molecular assays must be further optimized to maximize detection sensitivity. The potential for false negatives by both wastewater testing and clinical swab testing suggests that the two strategies could be employed together to maximize the probability of detecting SARS-CoV-2 infections amongst passengers.