ethanporcaro/eric-fulltext-meta huggingface dataset:
Student Discipline and School Climate in U.S. Public Schools. 2020-21 Civil Rights Data Collection. New Data Release 2023
Author: Department of Education (ED), Office for Civil Rights (OCR)
Date: 2023-11-00
Type: Numerical/Quantitative Data
Subjects: Discipline,Educational Environment,Public Schools,Behavior Problems,Bullying,Antisocial Behavior,Racial Differences,Ethnicity,Elementary Secondary Education,Preschool Education,English Language Learners,Students with Disabilities,School Districts,COVID-19,Pandemics,Educational Technology,Electronic Learning,Blended Learning,In Person Learning,Suspension,Expulsion,Gender Differences,Law Enforcement,Punishment,Student Characteristics

The 2020-21 Civil Rights Data Collection (CRDC) is a mandatory survey of all public school districts and schools serving students in preschool through grade 12 in the 50 states, Washington, D.C., and the Commonwealth of Puerto Rico and therefore includes data from 17,821 public school districts and 97,575 public schools. The CRDC measures student access to courses, programs, Internet and devices, instructional and other staff, and resources -- as well as school climate factors such as student discipline, use of restraint and seclusion, harassment or bullying, and offenses occurring at schools -- that impact education opportunity for students. This report highlights student discipline and school climate data public school districts submitted to the U.S. Department of Education's (ED) Office for Civil Rights (OCR) for the 2020-21 school year.
Abandon Statistical Significance
Author: McShane, Blakeley B.
Date: 2019-00-00
Type: Journal Articles
Subjects: Statistics,Replication (Evaluation),Biomedicine,Social Sciences,Scientific Research,Statistical Significance

We discuss problems the null hypothesis significance testing (NHST) paradigm poses for replication and more broadly in the biomedical and social sciences as well as how these problems remain unresolved by proposals involving modified p-value thresholds, confidence intervals, and Bayes factors. We then discuss our own proposal, which is to abandon statistical significance. We recommend dropping the NHST paradigm--and the p-value thresholds intrinsic to it--as the default statistical paradigm for research, publication, and discovery in the biomedical and social sciences. Specifically, we propose that the p-value be demoted from its threshold screening role and instead, treated continuously, be considered along with currently subordinate factors (e.g., related prior evidence, plausibility of mechanism, study design and data quality, real world costs and benefits, novelty of finding, and other factors that vary by research domain) as just one among many pieces of evidence. We have no desire to &quot;ban&quot; p-values or other purely statistical measures. Rather, we believe that such measures should not be thresholded and that, thresholded or not, they should not take priority over the currently subordinate factors. We also argue that it seldom makes sense to calibrate evidence as a function of p-values or other purely statistical measures. We offer recommendations for how our proposal can be implemented in the scientific publication process as well as in statistical decision making more broadly.
AI-Driven Learning Analytics in STEM Education
Author: Nuangchalerm, Prasart
Date: 2023-00-00
Type: Journal Articles
Subjects: Artificial Intelligence,Learning Analytics,STEM Education,Technology Uses in Education,Ethics

In recent years, the integration approach of Artificial Intelligence (AI) is called for many disciplines, it also STEM education has paved the way for transformative advancements. This paper provides an example of AI-driven learning analytics within the context of STEM education. It provides a thorough analysis of the AI-driven STEM curriculum and its associated paradigm. Additionally, it highlights the obstacles and possible threats that educators and institutions face when implementing technological innovations in the classroom. The serves as a valuable resource for educators, researchers, and policymakers seeking to harness the power of AI-driven learning analytics to enhance STEM education. The transformative potential of AI is now shaping the future of STEM learning environments while advocating for a responsible and ethical approach to data-driven education. Ethical concerns and moral considerations should be discussed in school AI and STEM education.
Expectation Propagation as a Way of Life: A Framework for Bayesian Inference on Partitioned Data
Author: Vehtari, Aki
Date: 2020-00-00
Type: Journal Articles
Subjects: Bayesian Statistics,Algorithms,Computation,Generalization,Statistical Inference

A common divide-and-conquer approach for Bayesian computation with big data is to partition the data, perform local inference for each piece separately, and combine the results to obtain a global posterior approximation. While being conceptually and computationally appealing, this method involves the problematic need to also split the prior for the local inferences; these weakened priors may not provide enough regularization for each separate computation, thus eliminating one of the key advantages of Bayesian methods. To resolve this dilemma while still retaining the generalizability of the underlying local inference method, we apply the idea of expectation propagation (EP) as a framework for distributed Bayesian inference. The central idea is to iteratively update approximations to the local likelihoods given the state of the other approximations and the prior. The present paper has two roles: we review the steps that are needed to keep EP algorithms numerically stable, and we suggest a general approach, inspired by EP, for approaching data partitioning problems in a way that achieves the computational benefits of parallelism while allowing each local update to make use of relevant information from the other sites. In addition, we demonstrate how the method can be applied in a hierarchical context to make use of partitioning of both data and parameters. The paper describes a general algorithmic framework, rather than a specific algorithm, and presents an example implementation for it.
Stacking for Non-Mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriors
Author: Yao, Yuling
Date: 2022-00-00
Type: Journal Articles
Subjects: Bayesian Statistics,Computation,Markov Processes,Monte Carlo Methods,Algorithms,Statistical Distributions,Statistical Inference,Regression (Statistics),Artificial Intelligence

When working with multimodal Bayesian posterior distributions, Markov chain Monte Carlo (MCMC) algorithms have difficulty moving between modes, and default variational or mode-based approximate inferences will understate posterior uncertainty. And, even if the most important modes can be found, it is difficult to evaluate their relative weights in the posterior. Here we propose an approach using parallel runs of MCMC, variational, or mode-based inference to hit as many modes or separated regions as possible and then combine these using Bayesian stacking, a scalable method for constructing a weighted average of distributions. The result from stacking efficiently samples from multimodal posterior distribution, minimizes cross validation prediction error, and represents the posterior uncertainty better than variational inference, but it is not necessarily equivalent, even asymptotically, to fully Bayesian inference. We present theoretical consistency with an example where the stacked inference approximates the true data generating process from the misspecified model and a non-mixing sampler, from which the predictive performance is better than full Bayesian inference, hence the multimodality can be considered a blessing rather than a curse under model misspecification. We demonstrate practical implementation in several model families: latent Dirichlet allocation, Gaussian process regression, hierarchical regression, horseshoe variable selection, and neural networks.
Student Loans: Education Should Proactively Manage Fraud Risks in Any Future Debt Relief Efforts. Report to Congressional Committees. GAO-24-107142
Author: Emrey-Arras, Melissa
Date: 2023-11-16
Type: Reports - Research
Subjects: Student Loan Programs,Deception,Risk,Loan Repayment,Educational Legislation,Higher Education,Federal Legislation

In August 2022, the Department of Education announced that, to address the heightened risk of delinquency and default caused by the COVID-19 pandemic, it would provide up to $20,000 of student loan debt relief to borrowers who met certain income thresholds. Borrowers eligible for this relief were to receive up to the full $20,000 in relief if they received a Pell Grant while in college, and up to $10,000 if they did not. In total, the Congressional Budget Office estimated that Education would have relieved around $430 billion of student loan debt for more than 31 million federal student loan borrowers. To be eligible, borrowers with qualifying federal loans must have had an annual adjusted gross income in 2020 or 2021 below certain thresholds. In November 2022, as a result of court orders, Education ceased work on the student loan debt relief program. In June 2023, the U.S. Supreme Court held that the debt relief program was not authorized under the Higher Education Relief Opportunities for Students Act of 2003 (HEROES Act). As a result, Education was not able to implement the program. The Biden administration subsequently announced plans to use the department's authority under the Higher Education Act of 1965, as amended, (HEA) to pursue an alternative path to debt relief for borrowers. The goal of this report is to provide feedback on Education's originally proposed debt relief program to inform any future efforts. Specifically, it assesses the extent to which Education's policies and procedures would have mitigated certain types of fraud risk in the originally proposed student loan debt relief program and reviewed key aspects of Education's fraud risk management in the program
Civil Rights Data Quality from Start to Finish. 2020-21 Civil Rights Data Collection (CRDC). Data Snapshot
Author: Department of Education (ED), Office for Civil Rights (OCR)
Date: 2023-11-00
Type: Reports - Descriptive
Subjects: National Surveys,Public Schools,School Districts,Federal Government,Public Agencies,Preschool Education,Elementary Secondary Education,Data Collection,Accuracy,Data Analysis,Quality Assurance

The U.S. Department of Education's (ED) Office for Civil Rights (OCR) administers the Civil Rights Data Collection (CRDC), which is a mandatory survey of all public schools and school districts in the 50 states, Washington, D.C., and the Commonwealth of Puerto Rico. To support school districts' timely submission of accurate data, OCR takes steps before, during, and after the submission process to support the collection of quality data.
An Overview of the U.S. Department of Education&apos;s 2020-21 Civil Rights Data Collection. 2020-21 Civil Rights Data Collection (CRDC). Data Snapshot
Author: Department of Education (ED), Office for Civil Rights (OCR)
Date: 2023-11-00
Type: Reports - Descriptive
Subjects: Data Collection,Public Agencies,Federal Government,Public Schools,School Districts,National Surveys,Preschool Education,Elementary Secondary Education

The U.S. Department of Education's (ED) Office for Civil Rights (OCR) administers the Civil Rights Data Collection (CRDC), which is a mandatory survey of all public schools and school districts in the 50 states, Washington, D.C., and the Commonwealth of Puerto Rico. This Data Snapshot provides an overview of the CRDC for the 2020-21 school year.
Referrals to Law Enforcement and School-Related Arrests in U.S. Public Schools during the 2020-21 School Year. 2020-21 Civil Rights Data Collection (CRDC)
Author: Department of Education (ED), Office for Civil Rights (OCR)
Date: 2023-11-00
Type: Reports - Descriptive
Subjects: Referral,Law Enforcement,Crime,Public Schools,Data Collection,Students with Disabilities,Race,Ethnicity,School Statistics

The Civil Rights Data Collection (CRDC) is a mandatory survey of all public schools and school districts in the 50 states, Washington, D.C., and the Commonwealth of Puerto Rico. Since the 2009-10 CRDC, schools and school districts have reported the number of students who were referred to law enforcement and arrested at the nation's public schools. This report covers responses for the 2020-21 school year.
Sexual Violence and Sex-Based Harassment or Bullying in U.S. Public Schools during the 2020-21 School Year. 2020-21 Civil Rights Data Collection (CRDC). Data Snapshot
Author: Department of Education (ED), Office for Civil Rights (OCR)
Date: 2023-11-00
Type: Reports - Descriptive
Subjects: Sexual Abuse,Bullying,Public Schools,Sexual Orientation,Gender Differences,Rape,School Districts,Sexual Harassment,Discipline,Racial Differences,Ethnicity,Intervention,Prevention,Elementary Secondary Education

The Civil Rights Data Collection (CRDC) is a mandatory survey of all public schools and school districts in the 50 states, Washington, D.C., and the Commonwealth of Puerto Rico. This Data Snapshot provides the reports of school districts about sexual violence and harassment or bullying on the basis of sex or sexual orientation for the 2020-21 school year.
Understanding Teacher Retention at Teacher-Powered Schools
Author: Marks, Daniel
Date: 2023-08-00
Type: Reports - Research
Subjects: Teacher Persistence,Teacher Leadership,Participative Decision Making,Teacher Collaboration,Teacher Student Relationship,Family School Relationship,School Community Relationship,School Culture

Annual teacher departure rates in the United States have held steady at about 16% for most of the 21st century. This high turnover has serious consequences at both the school and student levels (Carver-Thomas and Darling-Hammond, 2017; Ingersoll, 2004; NCES, 2014; Ronfeldt et al., 2012). Moreover, as we emerge from the pandemic, teachers are reporting levels of burnout and intention to leave the profession at the highest rates in recent history (Kurtz, 2022; NEA, 2022; Steiner et al., 2022). The problems that drive teachers out of schools are not immutable. Research on teacher leaving has consistently found that the primary reasons for departure are work environments and experiences that are unbearably unpleasant (e.g. Goldring et al., 2014; Ingersoll, 2004; Ingersoll et al., 2019; Podolsky et al., 2017; Simon and Johnson, 2015; Steiner et al., 2022). Teacher-powered schools--that is, schools collaboratively designed and run by teams of teachers in partnership with the students, families, and communities they serve--elevate the role of teachers by design. Because teachers at these schools make important school-level decisions, it is theorized that these schools are seeing improved teacher retention outcomes relative to other schools in the US. This report documents the findings of an exploratory survey- and interview-based research project in 2022 to examine this theory and explore school conditions that mediate teacher departure. [The report was written with Lars Esdal.]
Student Enrollment and School Characteristics in U.S. Public Schools. 2020-21 Civil Rights Data Collection. Data Snapshot
Author: Department of Education (ED), Office for Civil Rights (OCR)
Date: 2023-11-00
Type: Reports - Descriptive
Subjects: Enrollment,Institutional Characteristics,Public Schools,Student Characteristics,Elementary Secondary Education,Preschool Education,COVID-19,Pandemics,Teacher Student Ratio

The Civil Rights Data Collection (CRDC) is a mandatory survey of all public schools and school districts in the 50 states, Washington, D.C., and the Commonwealth of Puerto Rico. This Data Snapshot provides student demographics and school characteristics and staff in the CRDC for the 2020-21 school year.
Wyoming Community College System Term Enrollment Report. Spring 2022
Author: Wyoming Community College Commission
Date: 2022-07-20
Type: Numerical/Quantitative Data
Subjects: Enrollment Trends,Community College Students,Community Colleges,Geographic Location,Full Time Equivalency,Full Time Students,Age Differences,Gender Differences,College Credits,College Transfer Students,Academic Degrees,Vocational Education,Racial Differences,Ethnicity,Dual Enrollment,Remedial Instruction,Distance Education

This report provides a look at the Spring 2022 term enrollment in categories such as student load, location, and demographics of the community college student population in Wyoming. The content and format of this report have been developed through a collaborative effort between the Wyoming Community College Commission (WCCC), the Executive Council and the Data Governance/Institutional Research Council members representing the seven community colleges. Effective with the summer 2016 reporting term, data were extracted from the Central Station Instance (CSI) using one report rather than consolidating individual customized reports from all seven colleges.
Wyoming Community College System Term Enrollment Report. Summer 2022
Author: Wyoming Community College Commission
Date: 2022-11-28
Type: Numerical/Quantitative Data
Subjects: Enrollment Trends,Community College Students,Community Colleges,Geographic Location,Full Time Equivalency,Full Time Students,Age Differences,Gender Differences,College Credits,College Transfer Students,Academic Degrees,Vocational Education,Racial Differences,Ethnicity,Dual Enrollment,Remedial Instruction,Distance Education

This report provides a look at the Summer 2022 term enrollment in categories such as student load, location, and demographics of the community college student population. The content and format of this report have been developed through a collaborative effort between the Wyoming Community College Commission (WCCC), the Executive Council and the Data Governance/Institutional Research Council members representing the seven community colleges. Effective with the summer 2016 reporting term, data were extracted from the Central Station Instance (CSI) using one report rather than consolidating individual customized reports from all seven colleges. [For &quot;Wyoming Community College System Term Enrollment Report. Academic Year 2021-2022,&quot; see ED625121.]
Wyoming Community College System Term Enrollment Report. Fall 2022
Author: Wyoming Community College Commission
Date: 2023-02-21
Type: Numerical/Quantitative Data
Subjects: Enrollment Trends,Community College Students,Community Colleges,Geographic Location,Full Time Equivalency,Full Time Students,Age Differences,Gender Differences,College Credits,College Transfer Students,Academic Degrees,Vocational Education,Racial Differences,Ethnicity,Dual Enrollment,Remedial Instruction,Distance Education

This report provides a look at the Fall 2022 term enrollment in categories such as student load, location and demographics of the community college student population. The content and format of this report have been developed through a collaborative effort between the Wyoming Community College Commission (WCCC), the Executive Council and the Data Governance/Institutional Research Council members representing the seven community colleges. Effective with the summer 2016 reporting term, data were extracted from the Central Station Instance (CSI) using one report rather than consolidating individual customized reports from all seven colleges.
Issues in KSU Faculty Publication Archiving and Access
Author: Al-Jarf, Reima
Date: 2008-00-00
Type: Journal Articles
Subjects: Foreign Countries,Universities,College Faculty,Faculty Publishing,Archives,Access to Information,Electronic Publishing,Metadata,Information Retrieval,Online Searching

The present study examines the King Saud University Repository (KSUR) and explores how documents are collected, classified, curated and preserved, and how the digital content is accessed. Although the repository has a total of 48789 file URL's, the proportion of faculty's full-text publications contained in the repository is very small. Faculty publications are stored in folders and subfolders, which are given serial numbers, not fields and subfields. Documents in the KSUR cannot be accessed through the university library website and no link to a special website with a main page, simple and advanced searching tools and multiple keyword narrowing options is available. Faculty publications can be only accessed through Google or Google Scholar by using an author's name as a search term. The Arabic document content is unreadable and 70% of an abstract is simply deleted. When articles uploaded by the faculty in PDF format are located in Google or Google Scholar, they appear in WORD not PDF format. Use of faculty publications is not protected by copyright law. Detailed results are reported and recommendations for storing, retrieving, and accessing the KSUR are given.
Emerging Political Expressions in Arab Spring Media with Implications for Translation Pedagogy
Author: Al-Jarf, Reima
Date: 2022-00-00
Type: Journal Articles
Subjects: Translation,Teaching Methods,Arabs,Political Attitudes,Activism,Second Language Learning,Second Language Instruction,News Reporting,Mass Media,Language Usage,Linguistic Borrowing,Islam,Verbs,Phonology,Nouns,Religious Factors,Naming,Public Officials,Semantics,Minority Groups,Social Systems,Weapons,Armed Forces,Violence,Criticism,Social Attitudes,Majors (Students),Arabic,English (Second Language),Computer Software,Computational Linguistics,Discourse Analysis

A sample of political expressions that have been common in Arab media since the Arab Spring in 2011 was collected from TV newscasts, online news websites and social media pages. Analysis of their structure, denotative and connotative meanings revealed the following features: (i) Use of lexical hybrids ([foreign characters omitted]; (ii) revival of ancient Islamic expressions [foreign characters omitted]); (iii) use of blends ([foreign characters omitted]); (iv) few borrowings (BRICS); (v) use of new acronyms (ISIS) with verbs, nouns and agents derived from them ([foreign characters omitted]); and (vi) phonological and lexical substitutions in names of political and religious personalities that the users oppose (&quot;Kerdogan for Erdogan&quot;; [foreign characters omitted] for &quot;Hezbollah&quot;). Semantically, Arab Spring political terms refer to types of governments, places (towns), minority groups, religious sects, names of militia groups, weapons and military tactics, revolution squares that were not widely used before. They are also characterized by their non-literal use, i.e., use of loaded expressions, dysphemisms, and slurs that express disparagement, derogation, criticism, and disrespect towards those they oppose and towards the social and political situations in [foreign characters omitted]. Translation and interpreting instructors need to integrate emerging political terms commonly used in the media in political and media translation courses. Students majoring in translation and interpreting need to be familiar with new political terms and should be able to translate them from Arabic to English and vice versa. Students need to keep their own lists of new terms used in the media together with their equivalents in English or Arabic. They need to use Google Translate with caution as Google Translate usually gives incorrect word order in compounds. It also gives equivalents that do not match source terms in part of speech and in the type of derivative.