ethanporcaro/eric-fulltext-meta huggingface dataset:
A Skilled and Educated Workforce, 2017 Update: An Analysis of Postsecondary Education, Workforce Preparation, and Employer Demand in Washington
Author: Washington Student Achievement Council
Date: 2017-00-00
Type: Reports - Descriptive
Subjects: Labor Force Development,Labor Needs,Academic Degrees,Educational Attainment,Job Skills,Apprenticeships,Certification,Associate Degrees,Postsecondary Education,Higher Education,Wages,Unemployment,Occupations,Supply and Demand,Graduate Study,Undergraduate Study,Teacher Shortage

The purpose of this report is to provide an overview of the current status of workforce preparation in Washington; identify high employer demand occupations, as well as fields in which academic degree production is failing to keep pace with demand; and highlight occupation fields in which students may find expanding employment opportunities. This report focuses on projected workforce needs for the period from 2020 to 2025. The Washington Student Achievement Council (WSAC) prepares this analysis in collaboration with the State Board for Community and Technical Colleges (SBCTC) and the Workforce Training and Education Coordinating Board (Workforce Board), in accordance with statutory responsibilities specified in RCW 28B.77.080. This 2012 statute directs these agencies, as part of a broader educational needs assessment process, to analyze &quot;the number of forecasted net job openings at each level of higher education and training, and the number of credentials needed to match the forecast of net job openings.&quot; The report is also used in the state's broader educational planning. In 2013, WSAC issued its Roadmap report, a ten-year guide for the development of a coordinated, long-term strategy to increase educational attainment in the state. It identified key challenges and priorities the state must address in the development of the plan. Among these key challenges were closing existing workforce skills gaps and meeting the demand for an educated workforce to complement Washington's modern, dynamic economy. Similarly, SBCTC and the Workforce Board use the report in their strategic planning, focusing on meeting Washington's needs for mid-level education. This includes apprenticeships, certificate programs, and associate degrees. [This report was co-produced by Washington State Board for Community and Technical Colleges.]
Skills and Earnings in the Full-Time Labor Market. The Impact of Human Capital in the American Labor Market Series
Author: Fogg, Neeta
Date: 2018-09-00
Type: Reports - Research
Subjects: Job Skills,Income,Labor Market,Human Capital,Higher Education,Education Work Relationship,College Students,Educational Benefits,Educational Attainment,Work Experience,Adults,Employees,Literacy,Numeracy,Skilled Workers,Semiskilled Workers,White Collar Occupations,Blue Collar Occupations,Demography

This paper is the first in a series of policy reports dealing with the impact of human capital investments on the U.S. labor market. It is part of a larger series of papers from the ETS Center for Research on Human Capital and Education that uses data from the &quot;Programme for the International Assessment of Adult Competencies&quot; (PIAAC) to better understand the relationship between human capital and opportunity in America today. Research on labor market gains associated with investments in human capital most frequently relies on years of schooling completed, or educational credentials, as a measure of human capital. Indeed, it is commonplace in speeches by college presidents to touch upon the increased earnings associated with a college education. Over the past four decades, the size of the average earnings advantage for completing a bachelor's degree has grown substantially. Large increases in college enrollment rates among young people have resulted--as have large increases in college tuition and fees and skyrocketing student debt. Yet while the gains from a college education can be quite large, that's not the whole story. Like any investment activity, the economic payoffs do not accrue to everyone. This paper raises the specter that, under some conditions, college enrollment may offer no economic benefit at all. Using newly available data on the literacy and numeracy proficiencies of American adults produced by the Survey of Adult Skills of PIAAC, this paper examines the independent effects of three key measures of human capital--basic skills, educational attainment, and work experience--on the earnings of prime-age (25 to 54 years old), full-time employed workers in the United States. In line with human capital theory, this study found a strong connection between earnings and each measure, with the details revealing strong gains for those with the most skills and education, but little or no gain, in one particular example, for those who fail to gain a bachelor's degree.
Guide to Effective Employment Programs: Vocational Rehabilitation Service Models for Individuals with Autism Spectrum Disorders
Author: Williams, Ann
Date: 2013-00-00
Type: Guides - General
Subjects: Autism,Pervasive Developmental Disorders,Vocational Rehabilitation,Program Effectiveness,Employment Level,Employment Services,Job Skills,Supported Employment,Costs,Program Design

The Southeast Educational Development Laboratory's (SEDL's) Vocational Rehabilitation Service Models for Individuals with Autism Spectrum Disorders project, funded by the National Institute for Disability and Rehabilitation Research (NIDRR), launched in October 2008 and ended in September 2013. The mission of this project was to conduct high quality research and knowledge translation activities in order to improve employment services and outcomes for individuals with autism spectrum disorders (ASD). One activity of this project was identifying effective programs. This study focused on effective practices implemented by Vocational Rehabilitation (VR) service providers for people with ASD. Outcomes from this study included empirical assessments of practices perceived as effective by VR professionals, and identification and dissemination of those practices that were linked to successful outcomes (i.e., helping people with ASD find and maintain competitive employment). This guide discusses the results of this study and outlines effective practices of VR vendors. Information found here can be used to identify effective programs or in quality improvement initiatives. The intended audience of this guide is people with ASD, their families, VR professionals, and employment specialists. This project ultimately hopes to contribute to the continuing improvement in employment programs for people with ASD.
What Cypriot Children Think about the Concept of Peace?
Author: Ummanel, Azize
Date: 2018-00-00
Type: Journal Articles
Subjects: Peace,Student Attitudes,Conflict Resolution,Nongovernmental Organizations,Figurative Language,Ethnic Groups,Greek,Turkish,Native Language,Elementary School Students,Cloze Procedure,Aesthetics,Emotional Response,Behavior Patterns,Activism,Trust (Psychology),Program Descriptions,Foreign Countries

Cyprus is a Mediterranean island inhabited by two communities with a common culture, although they speak different languages. Efforts to reconcile these two communities, living apart from each other in different parts of the island since 1974, have become more important in recent years and efforts have been made to ensure a lasting solution on the island. Within the scope of these efforts, non-governmental organizations in Cyprus are also carrying out various studies with the support of the European Union. This is one of those studies, which was carried out with the support of the Grow Civil Program. The purpose of this study is to examine the metaphors of the Turkish Cypriot and Greek Cypriot children created about the concept of peace. The study group consists of 23 children, aged between six and ten. Children were given a form written in Turkish and Greek languages with an expression on it as &quot;Peace is like........ because..........&quot; and they were asked to fill in the gaps. The obtained data were analyzed and reported by the content analysis method. Results indicate that the metaphors produced by Turkish Cypriot children are related to emotions, behaviors and individuals, while the Greek Cypriots' are related to space, aesthetic perception, and individuals. While politicians are seeking for a permanent solution on the island, non-governmental organizations and activists have been organizing various activities to increase mutual trust between the two communities. It is thought that this study will contribute to the bi-communal studies in Cyprus.
District Readiness to Support School Turnaround: A Guide for State Education Agencies and Districts, 2nd Edition. The Center on School Turnaround Four Domains Series
Author: Hitt, Dallas Hambrick
Date: 2018-00-00
Type: Reports - Descriptive
Subjects: Readiness,School Districts,School Turnaround,State Departments of Education,Leadership Responsibility,Educational Change,Talent Development,Change Strategies,Cooperative Planning

This document provides state education agencies (SEAs) and districts with guidance about how to assess a district's readiness to support school turnaround initiatives. First published in 2013, the guide has been updated in this edition to highlight how its approach to assessing district readiness embeds and reflects key components of &quot;Four Domains for Rapid School Improvement,&quot; a framework developed by the Center on School Turnaround (CST, 2017). Using this framework, the guide aims to help policymakers or practitioners consider school turnaround as a &quot;system-level&quot; issue -- fundamental district-level practices must be in place to establish the conditions for school turnaround to succeed. This perspective counters the tendency for school turnaround efforts to focus on only the &quot;school's&quot; structure and leadership. By contrast, this guide provides an introduction to district-level turnaround readiness and the conditions that will help districts best position resources to enable turnaround schools to succeed and to sustain that success. SEA leaders can also use the guide to reflect on where and how to support districts in ways that have been shown to matter by the experience of practitioners entrenched in supporting turnaround as well as by research in the field of rapid improvement. The Every Student Succeeds Act gives SEAs more discretion in where to invest precious resources, and a readiness-assessment process aligned with what matters can help SEAs determine what commitments may be needed from a district if that district is to receive a major investment of resources from the SEA. A vignette is included to illustrate how such district-led turnaround might unfold in practice and show what the district and SEA can do to create the conditions for school leaders to have the greatest chance for success. [For &quot;District Readiness to Support School Turnaround: A Users' Guide to Inform the Work of State Education Agencies and Districts,&quot; see ED559742.]
A Partnership Model Approach to Understanding Challenges in Collaboration around College Readiness
Author: Mokher, Christine G.
Date: 2019-00-00
Type: Reports - Research
Subjects: College Readiness,Partnerships in Education,College School Cooperation,State Policy,Social Capital,Educational Change,Secondary Schools,Universities,Accountability,Leadership Role,Alignment (Education),Educational Objectives,Academic Standards,Curriculum,Adoption (Ideas),Advocacy,Educational Finance

This paper explores challenges in starting, developing, and incorporating partnerships between secondary and postsecondary education sectors. Site visits data from high schools, districts, and colleges across six Florida counties is used to identify the types of challenges educators faced in various phases of partnerships. We provide empirical evidence to support a conceptual framework based on a partnership model, which demonstrates that social capital and organizational capital are needed to create &quot;partnership capital&quot; to initiate and sustain partnerships. Whereas considerable collaborative resources are available to help higher-performing students prepare for selective universities, there is much less collaboration around improving college readiness. [This article was published in &quot;Educational Policy&quot; 2019.]
Understanding Student Procrastination via Mixture Models
Author: Park, Jihyun
Date: 2018-07-00
Type: Speeches/Meeting Papers
Subjects: Time Management,Online Courses,Electronic Learning,Probability,Learning Activities,Models,Student Attitudes,Student Behavior,Undergraduate Students

Time management is crucial to success in online courses in which students can schedule their learning on a flexible basis. Procrastination is largely viewed as a failure of time management and has been linked to poorer outcomes for students. Past research has quantified the extent of students' procrastination by defining single measures directly from raw logs of student activity. In this work, we use a probabilistic mixture model to allow different types of behavioral patterns to naturally emerge from clickstream data and analyze the resulting patterns in the context of procrastination. Moreover, we extend our analysis to include measures of student regularity--how consistent the procrastinating behaviors are--and construct a composite Time Management Score (&quot;TM&quot;). Our results show that mixture modeling is able to unveil latent types of behavior, each of which is associated with a level of procrastination and its regularity. Overall, students identified as non-procrastinators tend to perform significantly better. Within non-procrastinators, higher levels of regularity signify better performance, while this may be the opposite for procrastinators. [For the full proceedings, see ED593090.]
Principles for Assessing Adaptive Online Courses
Author: Chen, Weiyu
Date: 2018-07-00
Type: Speeches/Meeting Papers
Subjects: Online Courses,Comparative Analysis,Statistics,Correlation,Learner Engagement,Electronic Learning,Scores

Adaptive online courses are designed to automatically customize material for different users, typically based on data captured during the course. Assessing the quality of these adaptive courses, however, can be difficult. Traditional assessment methods for (machine) learning algorithms, such as comparison against a ground truth, are often unavailable due to education's unique goal of affecting both internal user knowledge, which cannot be directly measured, as well as external, measurable performance. Traditional metrics for education like quiz scores, on the other hand, do not necessarily capture the adaptive course's ability to present the right material to different users. In this work, we present a mathematical framework for developing scalable, efficiently computable metrics for these courses that can be used by instructors to gauge the efficacy of the adaptation and their course content. Our metric framework takes as input a set of quantities describing user activities in the course, and balances definitions of user consistency and overall efficacy as inferred by the quantity distributions. We support the metric definitions by comparing the results of a comprehensive statistical analysis with a sample metric evaluation on a dataset of roughly 5,000 users from an online chess platform. In doing so, we find that our metrics yield important insights about the course that are embedded in the larger statistical analysis, as well as additional insights into student drop-off rates. [For the full proceedings, see ED593090.]
Who They Are and What They Want: Understanding the Reasons for MOOC Enrollment
Author: Crues, R. Wes
Date: 2018-07-16
Type: Speeches/Meeting Papers
Subjects: Large Group Instruction,Online Courses,Enrollment,Student Attitudes,Course Selection (Students),Age Differences,Gender Differences,Decision Making

The diversity in reasons that students have for enrolling in massive open online courses (MOOCs) is an often-overlooked aspect while modeling learners' behaviors in MOOCs. Using survey data from 11,202 students in five MOOCs spanning different academic disciplines, this study evaluates the reasons that students enrolled in MOOCs, using an unsupervised learning method, Latent Dirichlet Allocation (LDA). After fitting an LDA model, we used correspondence analysis to understand whether these reasons were general, and could be invoked across the five MOOCs, or whether the reasons were course-specific. Furthermore, log-linear models were employed to understand the relations between the reasons students enrolled, the course they took, and their background characteristics. We found that students enrolled for many different reasons, and that their age was statistically related to the reasons they gave for taking a MOOC, but their gender was not. The paper concludes with a discussion of how instructors and course designers can use this information when creating new--or redesigning existing-- MOOCs. [For the full proceedings, see ED593090.]
Gender Differences in Undergraduate Engineering Applicants: A Text Mining Approach
Author: Chopra, Shivangi
Date: 2018-07-16
Type: Speeches/Meeting Papers
Subjects: Gender Differences,Undergraduate Students,Engineering Education,STEM Education,Science Education,College Faculty,Technology Education,Mathematics Education,Data Processing,College Students

It is well known that post-secondary science and engineering programs attract fewer female students. In this paper, we analyze gender differences through text mining of over 30,000 applications to the engineering faculty of a large North American university. We use syntactic and semantic analysis methods to highlight differences in motivation, interests and background. Our analysis leads to three main findings. First, female applicants demonstrate a wider breadth of experience, whereas male applicants put a greater emphasis on technical depth. Second, more female applicants demonstrate a greater desire to serve society. Third, female applicants are more likely to mention personal influences for studying engineering. [For the full proceedings, see ED593090.]
Impact of Corpus Size and Dimensionality of LSA Spaces from Wikipedia Articles on AutoTutor Answer Evaluation
Author: Cai, Zhiqiang
Date: 2018-07-00
Type: Speeches/Meeting Papers
Subjects: Semantics,Discourse Analysis,Computational Linguistics,Intelligent Tutoring Systems,Collaborative Writing,Web Sites,Information Retrieval,Comparative Analysis,Phrase Structure,Cues,Role,Educational Environment,Physics,Science Instruction

Latent Semantic Analysis (LSA) plays an important role in analyzing text data from education settings. LSA represents meaning of words and sets of words by vectors from a k-dimensional space generated from a selected corpus. While the impact of the value of k has been investigated by many researchers, the impact of the selection of documents and the size of the corpus has never been systematically investigated. This paper tackles this problem based on the performance of LSA in evaluating learners' answers to AutoTutor, a conversational intelligent tutoring system. We report the impact of document sources (Wikipedia vs TASA), selection algorithms (keyword based vs random), corpus size (from 2000 to 30000 documents) and number of dimensions (from 2 to 1000). Two AutoTutor tasks are used to evaluate the performance of different LSA spaces: a phrase level answer assessment (responses to focal prompt questions) and a sentence level answer assessment (responses to hints). We show that a sufficiently large (e.g., 20,000 to 30,000 documents) randomly selected Wikipedia corpus with high enough dimensions (about 300) could provide a reasonably good space. A specifically selected domain corpus could have significantly better performance with a relatively smaller corpus size (about 8000 documents) and much lower dimensionality (around 17). The widely used TASA corpus (37,651 documents scientifically sampled) performs equally well as a randomly selected large Wikipedia corpus (20,000 to 30,000) with a sufficiently high dimensionality (e.g., k&gt;=300). [For the full proceedings, see ED593090.]
Improving Stealth Assessment in Game-Based Learning with LSTM-Based Analytics
Author: Akram, Bita
Date: 2018-07-00
Type: Speeches/Meeting Papers
Subjects: Educational Games,Problem Solving,Educational Environment,Short Term Memory,Middle School Students,Computation,Thinking Skills,Interaction,Learner Engagement,Models

A key affordance of game-based learning environments is their potential to unobtrusively assess student learning without interfering with gameplay. In this paper, we introduce a temporal analytics framework for stealth assessment that analyzes students' problem-solving strategies. The strategy-based temporal analytic framework uses long short-term memory network-based evidence models and clusters sequences of students' problem-solving behaviors across consecutive tasks. We investigate this strategy based temporal analytics framework on a dataset of problem solving behaviors collected from student interactions with a game-based learning environment for middle school computational thinking. The results of an evaluation indicate that the strategy-based temporal analytics framework significantly outperforms competitive baseline models with respect to stealth assessment predictive accuracy. [For the full proceedings, see ED593090.]
Intelligent Instructional Hand Offs
Author: Fancsali, Stephen E.
Date: 2018-07-00
Type: Speeches/Meeting Papers
Subjects: Intelligent Tutoring Systems,Tutors,Higher Education,Online Courses,Remedial Mathematics,Prediction

Learners in various contemporary settings (e.g., K-12 classrooms, online courses, professional/vocational training) find themselves in situations in which they have access to multiple technology-based learning platforms and often one or more non-technological resources (e.g., human instructors or on-demand human tutors). Instructors, similarly, find themselves in situations in which they can provide learners with a variety of options for instruction, practice, homework, and other activities. We seek data-driven guidance to help facilitate intelligent instructional &quot;hand offs&quot; between learning resources. To begin this work, we focus on an important element of self-regulated learning, namely help seeking. We build classifier models based on proxies for learner prior knowledge and data-driven inferences about learners' disengaged behavior (e.g., gaming the system) and affective states (e.g., confusion) to determine the extent to which (and when) learners tended to seek out help via human tutoring while using an intelligent tutoring system for mathematics. Insights into cognitive, behavioral, and affective factors associated with help seeking outside of a system will drive future work into providing automated, intelligent guidance to both learners and instructors. We close with discussion of the limitations of the present analysis and avenues for future work on intelligently guiding instructional hand offs. [For the full proceedings, see ED593090.]