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The use of data to inform instruction has been linked to improved student outcomes, early identification of intervention needs, and teacher decision-making and efficacy. Additionally, data are used as a means of accountability within educational settings. However, little is known about data use practices among early grades teachers. The purpose of the current study is to describe the data use of PreK to third grade teachers and to investigate differences in data use and support across grade levels. Participants were 307 early childhood teachers in PreK and early elementary school. Analysis of survey data revealed, overall, most teachers across grade levels collected observational data and direct assessments and data were predominantly used to inform instruction and determine if students are ready to learn new skills. In general, teachers indicated that support for data use is available. Results also indicate significant variation in data types, use, and support across PreK to third grade.

Purpose Although current research has investigated how open research data (ORD) are published, researchers' behaviour of ORD sharing on academic social networks (ASNs) remains insufficiently explored. The purpose of this study is to investigate the connections between ORDs publication and social activity to uncover data literacy gaps.Design/methodology/approach This work investigates whether the ORDs publication leads to social activity around the ORDs and their linked published articles to uncover data literacy needs. The social activity was characterised as reads and citations, over the basis of a non-invasive approach supporting this preliminary study. The eventual associations between the social activity and the researchers' profile (scientific domain, gender, region, professional position, reputation) and the quality of the ORD published were investigated to complete this picture. A random sample of ORD items extracted from ResearchGate (752 ORDs) was analysed using quantitative techniques, including descriptive statistics, logistic regression and K-means cluster analysis.Findings The results highlight three main phenomena: (1) Globally, there is still an underdeveloped social activity around self-archived ORDs in ResearchGate, in terms of reads and citations, regardless of the published ORDs quality; (2) disentangling the moderating effects over social activity around ORD spots traditional dynamics within the “innovative” practice of engaging with data practices; (3) a somewhat similar situation of ResearchGate as ASN to other data platforms and repositories, in terms of social activity around ORD, was detected.Research limitations/implications Although the data were collected within a narrow period, the random data collection ensures a representative picture of researchers' practices.Practical implications As per the implications, the study sheds light on data literacy requirements to promote social activity around ORD in the context of open science as a desirable frontier of practice.Originality/value Researchers data literacy across digital systems is still little understood. Although there are many policies and technological infrastructure providing support, the researchers do not make an in-depth use of them.Peer reviewThe peer-review history for this article is available at: https://publons.com/publon/10.1108/OIR-05-2021-0255.

\"Subjective Well-Being and Social Media shows how, by exploiting the unprecedented amount of information provided by the social networking sites, it is possible to build new composite indicators of subjective well-being. These new social media indicators are complementary to official statistics and surveys, whose data are collected at very low temporary and geographical resolution. The book also explains in full details how to solve the problem of selection bias coming from social media data. Mixing textual analysis, machine learning and time series analysis, the book also shows how to extract both the structural and the temporary components of subjective well-being. Cross-country analysis confirms that well-being is a complex phenomenon that is governed by macroeconomic and health factors, ageing, temporary shocks and cultural and psychological aspects. As an example, the last part of the book focuses on the impact of the prolonged stress due to the COVID-19 pandemic on subjective well-being in both Japan and Italy. Through a data science approach, the results show that a consistent and persistent drop occurred throughout 2020 in the overall level of well-being in both countries. The methodology presented in this book: enables social scientists and policy makers to know what people think about the quality of their own life, minimizing the bias induced by the interaction between the researcher and the observed individuals; being language-free, it allows for comparing the well-being perceived in different linguistic and socio-cultural contexts, disentangling differences due to objective events and life conditions from dissimilarities related to social norms or language specificities; provides a solution to the problem of selection bias in social media data through a systematic approach based on time-space small area estimation models. The book comes also with replication R scripts and data. Stefano M. Iacus is full professor of Statistics at the University of Milan, on leave at the Joint Research Centre of the European Commission. Former R-core member (1999-2017) and R Foundation Member. Giuseppe Porro is full professor of Economic Policy at the University of Insubria. An earlier version of this project was awarded the Italian Institute of Statistics-Google prize for \"official statistics and big data\"\"-- Provided by publisher.

Insights derived from classroom data can help teachers improve their practice and students’ learning. However, a number of obstacles stand in the way of widespread adoption of data use. Teachers are often sceptical about the usefulness of data. Even when willing to work with data, they often do not have the relevant skills. Tools for analysis of learning data can, theoretically, aid teachers in data use, but often fall short of their potential as they are commonly designed without reference to educational theory and rarely consider end-user’s needs. Keeping these challenges in mind, we designed a professional development program that aimed at, among other things, improving teachers’ beliefs regarding data and their data literacy skills. After the training, we found that teachers had more positive attitudes regarding data. However, some data literacy skills proved quite difficult to learn. We present and analyse our intervention here and forward a proposal for improving the effectiveness of data use interventions by leveraging theory-based Learning Analytics (LA) dashboards as mediating tools that scaffold teachers’ acquisition of new knowledge and skills during and beyond the intervention.

\"User-friendly guide explains intellectual property law as it applies to fiction, screenwriting, all forms of filmmaking from celluloid to digital, animation, video gaming and other creative media\"-- Provided by publisher.

Background Acute Care Surgery (ACS) was developed as a structured, team-based approach to providing round-the-clock emergency general surgery (EGS) care for adult patients needing treatment for diseases such as cholecystitis, gastrointestinal perforation, and necrotizing fasciitis. Lacking any prior evidence on optimizing outcomes for EGS patients, current implementation of ACS models has been idiosyncratic. We sought to use a Donabedian approach to elucidate potential EGS structures and processes that might be associated with improved outcomes as an initial step in designing the optimal model of ACS care for EGS patients. Methods We developed and implemented a national survey of hospital-level EGS structures and processes by surveying surgeons or chief medical officers regarding hospital-level structures and processes that directly or indirectly impacted EGS care delivery in 2015. These responses were then anonymously linked to 2015 data from the American Hospital Association (AHA) annual survey, Medicare Provider Analysis and Review claims (MedPAR), 17 State Inpatient Databases (SIDs) using AHA unique identifiers (AHAID). This allowed us to combine hospital-level data, as reported in our survey or to the AHA, to patient-level data in an effort to further examine the role of EGS structures and processes on EGS outcomes. We describe the multi-step, iterative process utilizing the Donabedian framework for quality measurement that serves as a foundation for later work in this project. Results Hospitals that responded to the survey were primarily non-governmental and located in urban settings. A plurality of respondent hospitals had fewer than 100 inpatient beds. A minority of the hospitals had medical school affiliations. Discussion Our results will enable us to develop a measure of preparedness for delivering EGS care in the US, provide guidance for regionalized care models for EGS care, tiering of ACS programs based on the robustness of their EGS structures and processes and the quality of their outcomes, and formulate triage guidelines based on patient risk factors and severity of EGS disease. Conclusions Our work provides a template for team science applicable to research efforts combining primary data collection (i.e., that derived from our survey) with existing national data sources (i.e., SIDs and MedPAR).

Disasters disrupt communication channels, infrastructure, and overburden health systems. This creates unique challenges to the functionality of surveillance tools, data collection systems, and information sharing platforms. The WHO Health Emergency and Disaster Risk Management (Health-EDRM) framework highlights the need for appropriate data collection, data interpretation, and data use from individual, community, and global levels. The COVID-19 crisis has evolved the way hazards and risks are viewed. No longer as a linear event but as a protracted hazard, with cascading and compound risks that affect communities facing complex risks such as climate-related disasters or urban growth. The large-scale disruptions of COVID-19 show that disaster data must evolve beyond mortality and frequency of events, in order to encompass the impact on the livelihood of communities, differentiated between population groups. This includes relative economic losses and psychosocial damage. COVID-19 has created a global opportunity to review how the scientific community classifies data, and how comparable indicators are selected to inform evidence-based resilience building and emergency preparedness. A shift into microlevel data, and regional-level information sharing is necessary to tailor community-level interventions for risk mitigation and disaster preparedness. Real-time data sharing, open governance, cross-organisational, and inter-platform collaboration are necessary not just in Health-EDRM and control of biological hazards, but for all natural hazards and man-made disasters.

While research on Learning Analytics (LA) is plentiful, it often prioritises perspectives on LA systems over the practical ways instructors use data to analyse and refine the learning process per se. The present study addresses this inadequacy by investigating how student data is employed by educators in UK Higher Education Institutions (HEIs) and how it could be optimised. Specifically, a mixed-methods approach was employed combining survey data, mainly from one institution (N = 85) with insights gleaned from interviews with academics (N = 11). The findings reveal a real desire for better data capabilities and access, underscoring the need for HEIs to enhance data capture, better integrate systems and invest in professional development to enhance data literacy and foster a culture of data-driven decision-making. Importantly, a similar emphasis to that given to assessment and attendance needs to be given to data for the differentiation and personalisation of learning.