Using Twitter to Track Unplanned School Closures: Georgia Public Schools, 2015-17

2020 ◽  
pp. 1-5 ◽  
Author(s):  
Jennifer O. Ahweyevu ◽  
Ngozi P. Chukwudebe ◽  
Brittany M. Buchanan ◽  
Jingjing Yin ◽  
Bishwa B. Adhikari ◽  
...  
Keyword(s):  
Public Schools ◽  
Emergency Response ◽  
School Closures ◽  
Policy Makers ◽  
Marginal Benefit ◽  
Twitter Data ◽  
Control And Prevention ◽  
Education Statistics ◽  
Data Source ◽  
Georgia Public Schools

ABSTRACT Objectives: To aid emergency response, Centers for Disease Control and Prevention (CDC) researchers monitor unplanned school closures (USCs) by conducting online systematic searches (OSS) to identify relevant publicly available reports. We examined the added utility of analyzing Twitter data to improve USC monitoring. Methods: Georgia public school data were obtained from the National Center for Education Statistics. We identified school and district Twitter accounts with 1 or more tweets ever posted (“active”), and their USC-related tweets in the 2015-16 and 2016-17 school years. CDC researchers provided OSS-identified USC reports. Descriptive statistics, univariate, and multivariable logistic regression were computed. Results: A majority (1,864/2,299) of Georgia public schools had, or were in a district with, active Twitter accounts in 2017. Among these schools, 638 were identified with USCs in 2015-16 (Twitter only, 222; OSS only, 2015; both, 201) and 981 in 2016-17 (Twitter only, 178; OSS only, 107; both, 696). The marginal benefit of adding Twitter as a data source was an increase in the number of schools identified with USCs by 53% (222/416) in 2015-16 and 22% (178/803) in 2016-17. Conclusions: Policy-makers may wish to consider the potential value of incorporating Twitter into existing USC monitoring systems.

scholarly journals The Demographic Transformation of the Teaching Force in the United States

2021 ◽  
Vol 11 (5) ◽  
pp. 234
Author(s):  
Richard Ingersoll ◽  
Elizabeth Merrill ◽  
Daniel Stuckey ◽  
Gregory Collins ◽  
Brandon Harrison
Keyword(s):  
The United States ◽  
Department Of Education ◽  
Exploratory Research ◽  
Policy Makers ◽  
The Public ◽  
Secondary Teaching ◽  
Education Statistics ◽  
Data Source ◽  
The U.S ◽  
Teaching Force

This article summarizes the results of an exploratory research project that investigated what demographic trends and changes have, or have not, occurred in the elementary and secondary teaching force in the U.S. over the past three decades, from 1987 to 2018. Our main data source was the Schools and Staffing Survey and its successor, the National Teacher Principal Survey, collectively the largest and most comprehensive source of data on teachers available in the U.S. These surveys are conducted by the National Center for Education Statistics (NCES), the statistical arm of the U.S. Department of Education. The results show that the teaching force has been, and is, greatly changing; yet, even the most dramatic trends appear to have been little noticed or understood by researchers, policy makers, and the public. This article summarizes seven of the most prominent trends and changes that we found. The U.S. teaching force is: larger; older; less experienced; more female; more diverse, by race/ethnicity; consistent in academic ability; unstable. For each of the trends, we explore two broad questions: 1. What are the reasons for and sources of the trend? 2. What are the implications and consequences of the trend?

scholarly journals Challenges when identifying migration from geo-located Twitter data

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Caitrin Armstrong ◽  
Ate Poorthuis ◽  
Matthew Zook ◽  
Derek Ruths ◽  
Thomas Soehl
Keyword(s):  
Classification Rate ◽  
Policy Makers ◽  
Travel History ◽  
Demographic Research ◽  
Migrant Populations ◽  
Twitter Data ◽  
History Of ◽  
Data Source ◽  
Migration Flows ◽  
Twitter Users

AbstractGiven the challenges in collecting up-to-date, comparable data on migrant populations the potential of digital trace data to study migration and migrants has sparked considerable interest among researchers and policy makers. In this paper we assess the reliability of one such data source that is heavily used within the research community: geolocated tweets. We assess strategies used in previous work to identify migrants based on their geolocation histories. We apply these approaches to infer the travel history of a set of Twitter users who regularly posted geolocated tweets between July 2012 and June 2015. In a second step we hand-code the entire tweet histories of a subset of the accounts identified as migrants by these methods. Upon close inspection very few of the accounts that are classified as migrants appear to be migrants in any conventional sense or international students. Rather we find these approaches identify other highly mobile populations such as frequent business or leisure travellers, or people who might best be described as “transnationals”. For demographic research that draws on this kind of data to generate estimates of migration flows this high mis-classification rate implies that findings are likely sensitive to the adjustment model used. For most research trying to use these data to study migrant populations, the data will be of limited utility. We suspect that increasing the correct classification rate substantially will not be easy and may introduce other biases.

scholarly journals Associations of Sociodemographic Factors and Health Behaviors with the Emotional Well-Being of Adolescents during the COVID-19 Pandemic in Brazil

2021 ◽  
Vol 18 (11) ◽  
pp. 6160
Author(s):  
Célia Landmann Szwarcwald ◽  
Deborah Carvalho Malta ◽  
Marilisa Berti de Azevedo Barros ◽  
Paulo Roberto Borges de Souza Júnior ◽  
Dália Romero ◽  
...  
Keyword(s):  
Public Schools ◽  
Fruits And Vegetables ◽  
Sleep Problems ◽  
Well Being ◽  
Sociodemographic Factors ◽  
Positive Influence ◽  
Snowball Sampling ◽  
School Closures ◽  
Cross Sectional ◽  
Socioeconomic Vulnerability

This cross-sectional study utilizes data from a nationwide web-based survey aimed to identify the factors affecting the emotional well-being of Brazilian adolescents aged 12–17 during the period of school closures and confinement. Data collection took place from 27 June to 17 September 2020. We used the “virtual snowball” sampling method, and students from private and public schools were included. A total of 9470 adolescents were analyzed. A hierarchical logistic regression model was used to find the factors associated with reporting at least two of three self-reported problems—sadness, irritability, and sleep problems. The main proximal factor was loneliness (AdjOR = 8.12 p < 0.001). Problems related to school closures also played an important role. Regular intake of fruits and vegetables, as well as physical activity, demonstrated a positive influence on emotional well-being, while excessive screen time (AdjOR = 2.05, p < 0.001) and alcohol consumption negatively affected outcomes (AdjOR = 1.73, p < 0.001). As for distal variables, less affluent adolescents were the most affected, and males reported fewer emotional problems than females. Uncertainty regarding the disease in a context of socioeconomic vulnerability, together with rises in unhealthy behaviors and isolation from their immediate social circles, have negatively affected adolescents’ emotional status throughout the COVID-19 pandemic.

Nonprobability Sampling and Twitter

2017 ◽  
Vol 36 (2) ◽  
pp. 195-211 ◽  
Author(s):  
Patrick Rafail
Keyword(s):  
Social Sciences ◽  
Application Programming Interface ◽  
Sampling Strategies ◽  
Twitter Data ◽  
Large Databases ◽  
The Social ◽  
Application Programming ◽  
Data Source ◽  
Twitter Activity ◽  
Programming Interface

Twitter data are widely used in the social sciences. The Twitter Application Programming Interface (API) allows researchers to build large databases of user activity efficiently. Despite the potential of Twitter as a data source, less attention has been paid to issues of sampling, and in particular, the implications of different sampling strategies on overall data quality. This research proposes a set of conceptual distinctions between four types of populations that emerge when analyzing Twitter data and suggests sampling strategies that facilitate more comprehensive data collection from the Twitter API. Using three applications drawn from large databases of Twitter activity, this research also compares the results from the proposed sampling strategies, which provide defensible representations of the population of activity, to those collected with more frequently used hashtag samples. The results suggest that hashtag samples misrepresent important aspects of Twitter activity and may lead researchers to erroneous conclusions.

Moving beyond Brown: Race and Education After Parents v. Seattle School District No. 1

2011 ◽  
Vol 113 (4) ◽  
pp. 735-754 ◽  
Author(s):  
Jamel K. Donnor
Keyword(s):  
Public Schools ◽  
School District ◽  
Supreme Court ◽  
Students Of Color ◽  
Global Economy ◽  
Community Schools ◽  
Policy Makers ◽  
Educational Advancement ◽  
Quality Learning ◽  
The U.S

Background By a 5–4 margin, the U.S. Supreme Court in Parents Involved in Community Schools v. Seattle School District No. 1 declared that voluntary public school integration programs were unconstitutional. Citing the prospective harm that students and their families might incur from being denied admission to the high school of their choice, the Supreme Court declared that the plaintiffs, Parents Involved in Community Schools (PICS), had a valid claim of injury by asserting a interest in not being forced to compete for seats at certain high schools in a system that uses race as a deciding factor in many of its admissions decisions. Purpose The goal of the article is to discuss how conceptions of harm and fairness as articulated in Parents Involved in Community Schools v. Seattle School District No. 1 privilege the self-interests of White students and families over the educational needs of students of color. Research Design This article is a document analysis. Conclusions By referencing the Brown v. Board of Education of Topeka decision of 1954 (Brown I) to buttress its decision, the U.S. Supreme Court has determined that programmatic efforts to ensure students of color access to quality learning environments are inherently ominous. The dilemma moving forward for policy makers and scholars concerned with the educational advancement of students of color is not to develop new ways to integrate America's public schools or reconcile the gaps in the Supreme Court's logic, but rather to craft programs and policies for students of color around the human development and workforce needs of the global economy.

Democracy Now? Race, Education, and Black Self-Determination

2011 ◽  
Vol 113 (4) ◽  
pp. 811-830
Author(s):  
Adrienne D. Dixson
Keyword(s):  
Public Schools ◽  
Educational Equity ◽  
School Administrators ◽  
Research Question ◽  
Supreme Court Decision ◽  
The United States ◽  
Community Schools ◽  
Self Determination ◽  
Policy Makers ◽  
School Assignment

Background/Context The Supreme Court's June 2007 decision on the Parents Involved in Community Schools v. Seattle School District No.1 (PICS) provides an important context for school districts and educational policy makers as they consider the role of race in school assignment. The PICS decision has been described as essentially “undoing” the 1954 Supreme Court decision in the Brown v. Board of Education of Topeka case that ended de jure racial segregation. Purpose/Objective/Research Question/Focus of Study Given the rhetoric that education in the United States is the “great equalizer,” this conceptual article considers how the PICS decisions impact notions of educational equity and self-determination for African Americans. Research Design This article provides a conceptual analysis of the PICS decision and educational equity. Conclusions/Recommendations The author recommends that despite the PICS decision, school administrators and policy makers continue to consider how race impacts school assignment to ensure that public schools are democratic institutions that are racially and educationally equitable.

Crowdsourcing Incident Information for Emergency Response using Open Data Sources in Smart Cities

2018 ◽  
Vol 2672 (1) ◽  
pp. 198-208 ◽  
Author(s):  
Fan Zuo ◽  
Abdullah Kurkcu ◽  
Kaan Ozbay ◽  
Jingqin Gao
Keyword(s):  
Social Media ◽  
Prior Knowledge ◽  
Emergency Response ◽  
Large Scale ◽  
Latent Dirichlet Allocation ◽  
Smart Cities ◽  
Hurricane Sandy ◽  
Support Vector ◽  
Twitter Data ◽  
Emergency Events

Emergency events affect human security and safety as well as the integrity of the local infrastructure. Emergency response officials are required to make decisions using limited information and time. During emergency events, people post updates to social media networks, such as tweets, containing information about their status, help requests, incident reports, and other useful information. In this research project, the Latent Dirichlet Allocation (LDA) model is used to automatically classify incident-related tweets and incident types using Twitter data. Unlike the previous social media information models proposed in the related literature, the LDA is an unsupervised learning model which can be utilized directly without prior knowledge and preparation for data in order to save time during emergencies. Twitter data including messages and geolocation information during two recent events in New York City, the Chelsea explosion and Hurricane Sandy, are used as two case studies to test the accuracy of the LDA model for extracting incident-related tweets and labeling them by incident type. Results showed that the model could extract emergency events and classify them for both small and large-scale events, and the model’s hyper-parameters can be shared in a similar language environment to save model training time. Furthermore, the list of keywords generated by the model can be used as prior knowledge for emergency event classification and training of supervised classification models such as support vector machine and recurrent neural network.

Teacher Layoffs: An Empirical Illustration of Seniority versus Measures of Effectiveness

2011 ◽  
Vol 6 (3) ◽  
pp. 439-454 ◽  
Author(s):  
Donald Boyd ◽  
Hamilton Lankford ◽  
Susanna Loeb ◽  
James Wyckoff
Keyword(s):  
New York ◽  
Public Schools ◽  
School Districts ◽  
Teacher Effectiveness ◽  
District Leaders ◽  
Policy Makers ◽  
School District Leaders ◽  
Alternative Approaches ◽  
Teacher Layoffs ◽  
Measures Of Effectiveness

School districts are confronting difficult choices in the aftermath of the financial crisis. Today, the financial imbalance in many school districts is so large that there may be few alternatives to teacher layoffs. In nearly all school districts, layoffs are currently determined by some version of teacher seniority. Yet, alternative approaches to personnel reductions may substantially reduce the harm to students from staff reductions relative to layoffs based on seniority. As a result, many school district leaders and other policy makers are raising important questions about whether~other criteria, such as measures of teacher effectiveness, should inform layoffs. This policy brief, a quick look at some aspects of the debate, illustrates the differences in New York City public schools that would result if layoffs were determined by seniority in comparison to a measure of teacher effectiveness.

scholarly journals Why did some parents not send their children back to school following school closures during the COVID-19 pandemic: a cross-sectional survey

2021 ◽  
Vol 5 (1) ◽  
pp. e001014
Author(s):  
Lisa Woodland ◽  
Louise E Smith ◽  
Rebecca K Webster ◽  
Richard Amlôt ◽  
Antonia Rubin ◽  
...  
Keyword(s):  
Well Being ◽  
School Closure ◽  
Special Educational Needs ◽  
Cross Sectional Survey ◽  
School Closures ◽  
Vulnerable Children ◽  
Cross Sectional ◽  
Policy Makers ◽  
Educational Potential ◽  
Voluntary Basis

BackgroundOn 23 March 2020, schools closed to most children in England in response to COVID-19 until September 2020. Schools were kept open to children of key workers and vulnerable children on a voluntary basis. Starting 1 June 2020, children in reception (4–5 years old), year 1 (5–6 years old) and year 6 (10–11 years old) also became eligible to attend school.Methods1373 parents or guardians of children eligible to attend school completed a cross-sectional survey between 8 and 11 June 2020. We investigated factors associated with whether children attended school or not.Results46% (n=370/803) of children in year groups eligible to attend school and 13% (n=72/570) of children of key workers had attended school in the past 7 days. The most common reasons for sending children to school were that the child’s education would benefit, the child wanted to go to school and the parent needed to work. A child was significantly more likely to attend if the parent believed the child had already had COVID-19, they had special educational needs or a person in the household had COVID-19 symptoms.ConclusionsFollowing any future school closure, helping parents to feel comfortable returning their child to school will require policy makers and school leaders to communicate about the adequacy of their policies to: (A) ensure that the risk to children in school is minimised; (B) ensure that the educational potential within schools is maximised; and (C) ensure that the benefits of school for the psychological well-being of children are prioritised.

scholarly journals Southern Rural Public Schools: A Study of Teacher Perspectives

2015 ◽  
Author(s):  
Leah McCoy
Keyword(s):  
Public Schools ◽  
Teacher Perspectives ◽  
Public High Schools ◽  
Deep South ◽  
Cultural Issues ◽  
Qualitative Aspect ◽  
Policy Makers ◽  
Low Expectations ◽  
Teachers Perspectives ◽  
Cycle Of Poverty

This ethnography explores teachers’ perspectives of the cultural issues affecting academic performance in twelve public high schools in rural Mississippi and Louisiana. Fr om a thematic analysis of the tape-recorded interviews of forty-one mathematics teachers, five categories emerged, each comprising a qualitative aspect of teaching high school in an economically depressed area of the deep South: society, race, students, families, and schools. Each of these categories is discussed and explicated using exemplars from the interviews to show how each category emerged from the data. In addition, the relationships among these categories, which form a destructive cycle of poverty, low expectations, poor academic achievement, and inadequate opportunity, are discussed. Implications of this research for teachers and policy makers are explored.

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