ethanporcaro/eric-fulltext-meta huggingface dataset:
Automatic Identification of Nutritious Contexts for Learning Vocabulary Words
Author: Mostow, Jack
Date: 2015-06-00
Type: Speeches/Meeting Papers
Subjects: Nutrition,Vocabulary Development,Accuracy,Scoring,Models,Regression (Statistics),Identification,Context Effect,Evaluation Methods,Data Analysis,Comparative Analysis,Internet

Vocabulary knowledge is crucial to literacy development and academic success. Previous research has shown learning the meaning of a word requires encountering it in diverse informative contexts. In this work, we try to identify &quot;nutritious&quot; contexts for a word--contexts that help students build a rich mental representation of the word's meaning. Using crowdsourced ratings of vocabulary contexts retrieved from the web, AVER learns models to score unseen contexts for unseen words. We specify the features used in the models, measure their individual informativeness, evaluate AVER's cross-validated accuracy in scoring contexts for unseen words, and compare its agreement with the human ratings against the humans' agreement with each other. The automated scores are not good enough to replace human ratings, but should reduce human effort by identifying contexts likely to be worth rating by hand, subject to a tradeoff between the number of contexts inspected by hand, and how many of them a human judge will consider nutritious. [For complete proceedings, see ED560503.]
Influence Analysis by Heterogeneous Network in MOOC Forums: What Can We Discover?
Author: Jiang, Zhuoxuan
Date: 2015-06-00
Type: Speeches/Meeting Papers
Subjects: Online Courses,Large Group Instruction,Educational Technology,Social Networks,Network Analysis,Distance Education,Interpersonal Relationship,Computer Mediated Communication,Technology Uses in Education,Student Behavior,Statistical Analysis,Visualization,Foreign Countries

With the development of Massive Open Online Courses (MOOC) in recent years, discussion forums there have become one of the most important components for both students and instructors to widely exchange ideas. And actually MOOC forums play the role of social learning media for knowledge propagation. In order to further understand the emerging learning settings, we explore the social relationship there by modeling the forum as a heterogeneous network with theories of social network analysis. We discover a specific group of students, named representative students, who feature large engagement in discussions and large aggregation of the majority of the whole forum participation, except the large learning behavior or the best performance. Based on these discoveries, to answer representative students' threads preferentially could not only save time for instructors to choose target posts from all, but also could propagate the knowledge as widespread as possible. Furthermore if extra attention is paid to representative students in the sight of their behavior, performance and posts, instructors could readily get feedback of the teaching quality, realize the major concerns in forums, and then make measures to improve the teaching program. We also develop a real-time and effective visualization tool to help instructors achieve these. [For complete proceedings, see ED560503.]
Mining a Written Values Affirmation Intervention to Identify the Unique Linguistic Features of Stigmatized Groups
Author: Riddle, Travis
Date: 2015-06-00
Type: Reports - Research
Subjects: Intervention,Self Concept,Essays,Gender Bias,Racial Discrimination,Social Values,Stereotypes,Writing (Composition),Models,Natural Language Processing,Achievement Gap,Computational Linguistics,Content Analysis,College Students,Classification,Ethnicity,Statistical Analysis,Discourse Analysis

Social identity threat refers to the process through which an individual underperforms in some domain due to their concern with confirming a negative stereotype held about their group. Psychological research has identified this as one contributor to the underperformance and underrepresentation of women, Blacks, and Latinos in STEM fields. Over the last decade, a brief writing intervention known as a values affirmation, has been demonstrated to reduce these performance deficits. Presenting a novel dataset of affirmation essays, we address two questions. First, what linguistic features discriminate gender and race? Second, can topic models highlight distinguishing patterns of interest between these groups? Our data suggest that participants who have different identities tend to write about some values (e.g., social groups) in fundamentally different ways. These results hold promise for future investigations addressing the linguistic mechanism responsible for the effectiveness of values affirmation interventions. [For complete proceedings, see ED560503.]
Tackling Youth Unemployment through TVET. Report of the UNESCO-UNEVOC Online Conference 25 June to 9 July 2013
Author: UNESCO-UNEVOC International Centre for Technical and Vocational Education and Training (Germany)
Date: 2013-00-00
Type: Reports - Descriptive
Subjects: Youth,Unemployment,Vocational Education,Technical Education,Job Skills,Supply and Demand,Developing Nations,Teleconferencing,Intervention,Barriers,Transitional Programs,Readiness,Equal Opportunities (Jobs),At Risk Persons,Reputation,Progress Monitoring,Secondary School Students,Postsecondary Education

From 25 June to 9 July 2013, over 300 participants from 80 countries gathered on the UNEVOC e-Forum to discuss how TVET can address growing numbers of youth unemployment. TVET experts, policy makers, researchers, practitioners and most importantly young students and graduates joined the discussion.The discussion was organized around the following sub-themes: key barriers that young people face in finding work; what TVET institutions can do to ease the school-to-work transition; labour market information and skills forecasting; entrepreneurship education; widening access: engaging disadvantaged and marginalized groups; upgrading TVET's image: challenges and opportunities; and monitoring and evaluation of youth-focused TVET. The discussion was moderated by Dr Gita Subrahmanyam, Research Associate with the London School of Economics Public Policy Group and a Senior Consultant at the African Development Bank, and co-moderated by Katerina Ananiadou, Programme Specialist at UNESCO-UNEVOC and focal point for UNEVOC's youth-related activities. Through these discussions, UNESCO-UNEVOC aims to enhance awareness and encourage wider debate and understanding, including the sharing of on-going practices and the formulation of new ideas in the field of strategy and policy development. Guided by an expert in the field, the discussions seek experiences, expertise and feedback and wish to inspire people to take further action. A list of resources is provided, along with a list of the conference participants.
Adult Trade Apprentices: Exploring the Significance of Recognition of Prior Learning and Skill Sets for Earlier Completion. Research Report
Author: Hargreaves, Jo
Date: 2015-00-00
Type: Reports - Research
Subjects: Adults,Adult Learning,Building Trades,Apprenticeships,Prior Learning,Recognition (Achievement),Outcomes of Education,Education Work Relationship,Job Skills,Workplace Learning,On the Job Training,Foreign Countries,Qualifications,Vocational Education,Salary Wage Differentials,Financial Support,Competency Based Education,Acceleration (Education),Training

The nature of apprenticeships is changing. Increasing proportions of adult apprentices are prompting demand for various alternative pathways to completion. One option for an alternative pathway to accelerate completion is the use of recognition of prior learning (RPL) to identify existing skills and knowledge in combination with gap training. This study investigates the extent to which recognition is occurring for adult trade apprentices. The impacts of earlier completion are explored, as is the pay-off to completion in terms of employment outcomes and wages for an adult trade apprentice by comparison with trade apprentices under the age of 25 years. Overall, this study suggests that shortened pathways are not adversely affecting the outcomes of the individual trade apprentices. Cultural attitudes to alternative pathways to completion during an apprenticeship and inconsistent treatment and funding of RPL, as well as a lack of understanding about the role and purpose of skill sets, may be having an impact on the full benefits arising from these flexibilities for both employers and individuals. Appended are: (1) Literature Review Search Strategy; (2) The National Apprenticeships Program; (3) Training Package Data; (4) Specific Projects Focusing on Early Completions and Mature Age Workers; (5) Further Analyses on Adults Completing Earlier; (6) Jurisdictional Data; (7) Detailed Occupational Data for Apprentices with RPL Activity; (8) NAP Case Study: Detailed Tables on Program Demand; (9) Apprentice Outline of CBWP and CBTP across States and Territories (from Dunn et al. 2011); and (10) Analysis of &quot;Competency-Based Wage Progression&quot; Appearing in Awards Containing Apprentice Conditions.
Alternative Pathways to Apprenticeships. Good Practice Guide
Author: National Centre for Vocational Education Research
Date: 2015-00-00
Type: Guides - Non-Classroom
Subjects: Apprenticeships,Educational Practices,Nontraditional Education,Competency Based Education,Prior Learning,Recognition (Achievement),Barriers

Apprenticeships are changing. The increasing proportions of people entering apprenticeships at various levels of ability and backgrounds are stimulating demand for alternative pathways to completions. This good practice guide assembles the key findings for education practitioners and workplace supervisors from three related research reports on facilitators of earlier completion such as competency-based progression and recognition of prior learning (RPL). The reports provide a nuanced and multifaceted view of today's apprenticeship reality, including the enablers of and barriers to alternative apprenticeship pathways and advice for improved practice. Advice for practitioners and workplace supervisors is provided, along with suggestions for improved practice. [This good practice guide is based on the following reports: (1) &quot;Competency progression and completion: how is the policy being enacted in three trades?&quot; by Berwyn Clayton, Hugh Guthrie, Pam Every and Regan Harding; (2) &quot;Adult trade apprentices: exploring the significance of recognition of prior learning and skill sets for earlier completion&quot; by Jo Hargreaves and Davinia Blomberg; and (3) &quot;The returns to completion or partial completion of a qualification in the trades&quot; by Tham Lu.
Federal Privacy Laws That Apply to Children and Education. Safeguarding Data
Author: Data Quality Campaign
Date: 2014-07-00
Type: Reports - Descriptive
Subjects: Privacy,Laws,Information Security,Federal Legislation,Public Policy,Confidentiality,Childrens Rights,Health Insurance,Accountability,Internet,Student Records,Medical Evaluation,Records (Forms),Health,Educational Legislation,Parent Rights,Student Rights

This table identifies and briefly describes the following federal policies that safeguard and protect the confidentiality of personal information: (1) Family Educational Rights and Privacy Act (FERPA); (2) Protection of Pupil Rights Amendment (PPRA); (3) Health Insurance Portability and Accountability Act (HIPAA); (4) Children's Online Privacy Protection Act (COPPA); (5) Children's Internet Protection Act (CIPA); and (6) Privacy Act of 1974. Although this list is not exhaustive, it highlights key federal policies related to education records, health information, and the online activities of children. For detailed information about each law, just click on the title within the document.
Who Uses Student Data?
Author: Data Quality Campaign
Date: 2014-11-24
Type: Reports - Descriptive
Subjects: Data Collection,Information Utilization,Graphs,Access to Information,Student Characteristics,School Districts,State Departments of Education,Federal Government,Parents,Teachers,Principals,Human Services,Researchers

Most personal student information stays local. Districts, states, and the federal government all collect data about students for important purposes like informing instruction and providing information to the public. But the type of data collected, and who can access them, is different at each point. This graphic shows how student data--from schools to the US Department of Education--are and are not accessed and used.
On the Performance Characteristics of Latent-Factor and Knowledge Tracing Models
Author: Klingler, Severin
Date: 2015-06-00
Type: Speeches/Meeting Papers
Subjects: Intelligent Tutoring Systems,Knowledge Level,Item Response Theory,Prediction,Comparative Analysis,Models,Performance,Regression (Statistics),Data Analysis,Probability,Simulation,Performance Factors,Error Patterns

Modeling student knowledge is a fundamental task of an intelligent tutoring system. A popular approach for modeling the acquisition of knowledge is Bayesian Knowledge Tracing (BKT). Various extensions to the original BKT model have been proposed, among them two novel models that unify BKT and Item Response Theory (IRT). Latent Factor Knowledge Tracing (LFKT) and Feature Aware Student knowledge Tracing (FAST) exhibit state of the art prediction accuracy. However, only few studies have analyzed the characteristics of these different models. In this paper, we therefore evaluate and compare properties of the models using synthetic data sets. We sample from a combined student model that encompasses all four models. Based on the true parameters of the data generating process, we assess model performance characteristics for over 66'000 parameter configurations and identify best and worst case performance. Using regression we analyze the influence of different sampling parameters on the performance of the models and study their robustness under different model assumption violations. [For complete proceedings, see ED560503.]
Moving from Data for Compliance to Data for Action. Federal Spotlight
Author: Data Quality Campaign
Date: 2014-07-24
Type: Reports - Descriptive
Subjects: Student Records,Data Collection,Compliance (Legal),Federal Programs,Federal Aid,Information Utilization,Public Agencies,Academic Achievement,College Readiness,Career Readiness,Academic Degrees,Credentials,Employment Level,Health,Income,Family Characteristics,Postsecondary Education,Academic Persistence,Enrollment,Program Effectiveness,Educational Environment,Leadership,Evaluation Methods,Minority Group Students,Disadvantaged,Teacher Education,Teacher Effectiveness

A wide array of federal funding opportunities exists to help states build a secure infrastructure to support data use and ensure that students are prepared for college and careers. Because the reporting requirements for these programs are frequently developed without input from other programs and agencies, grantees are required to report data that are often uninformative or duplicative. This spotlight offers an overview of data that are available as a result of the compliance reporting requirements of federal programs
Using Partial Credit and Response History to Model User Knowledge
Author: Van Inwegen, Eric G.
Date: 2015-06-00
Type: Reports - Research
Subjects: Knowledge Level,Performance Factors,Error Patterns,Mathematics,Intelligent Tutoring Systems,Statistical Significance,Prediction,Technology Uses in Education,Models,Statistical Analysis,Responses,Regression (Statistics)

User modelling algorithms such as Performance Factors Analysis and Knowledge Tracing seek to determine a student's knowledge state by analyzing (among other features) right and wrong answers. Anyone who has ever graded an assignment by hand knows that some answers are &quot;more wrong&quot; than others; i.e. they display less of an understanding of the skill(s) involved. This investigation seeks to understand the effects of progression through wrong answers to right answers in a way to determine how the &quot;level&quot; of wrongness affects future performance. The key findings are that: A) where in a series of opportunities a student reaches the goal impacts future performance, as does B) the &quot;level&quot; of previous wrongness, even two questions before the current opportunity. [For complete proceedings, see ED560503.]
YouEDU: Addressing Confusion in MOOC Discussion Forums by Recommending Instructional Video Clips
Author: Agrawal, Akshay
Date: 2015-06-00
Type: Speeches/Meeting Papers
Subjects: Large Group Instruction,Distance Education,Technology Uses in Education,Educational Technology,Electronic Learning,Group Discussion,Computer Mediated Communication,Video Technology,Higher Education,Classification,Information Management,Coding,Correlation,Questioning Techniques,Intervention

In Massive Open Online Courses (MOOCs), struggling learners often seek help by posting questions in discussion forums. Unfortunately, given the large volume of discussion in MOOCs, instructors may overlook these learners' posts, detrimentally impacting the learning process and exacerbating attrition. In this paper, we present YouEDU, an instructional aid that automatically detects and addresses confusion in forum posts. Leveraging our Stanford MOOC-Posts corpus, we train a set of classifiers to classify forum posts across multiple dimensions. In particular, classifiers that target sentiment, urgency, and other descriptive variables inform a single classifier that detects confusion. We then employ information retrieval techniques to map confused posts to minute-resolution clips from course videos; the ranking over these clips accounts for textual similarity between posts and closed captions. We measure the performance of our classification model in multiple educational contexts, exploring the nature of confusion within each; we also evaluate the relevancy of materials returned by our ranking algorithm. Experimental results demonstrate that YouEDU achieves both its goals, paving the way for intelligent intervention systems in MOOC discussion forums. [For complete proceedings, see ED560503.]
&quot;Your Model Is Predictive-- but Is It Useful?&quot; Theoretical and Empirical Considerations of a New Paradigm for Adaptive Tutoring Evaluation
Author: González-Brenes, José P.
Date: 2015-06-00
Type: Speeches/Meeting Papers
Subjects: Intelligent Tutoring Systems,Evaluation Methods,Program Evaluation,Student Behavior,Mathematics,Student Participation,Responses,Drills (Practice),Skill Development,Statistical Analysis,Middle School Students,Mathematics Instruction,Mathematics Skills,Prediction,Models,Classification,Simulation

Classification evaluation metrics are often used to evaluate adaptive tutoring systems-- programs that teach and adapt to humans. Unfortunately, it is not clear how intuitive these metrics are for practitioners with little machine learning background. Moreover, our experiments suggest that existing convention for evaluating tutoring systems may lead to suboptimal decisions. We propose the Learner Effort-Outcomes Paradigm (Leopard), a new framework to evaluate adaptive tutoring. We introduce Teal and White, novel automatic metrics that apply Leopard and quantify the amount of effort required to achieve a learning outcome. Our experiments suggest that our metrics are a better alternative for evaluating adaptive tutoring. [For complete proceedings, see ED560503.]
Paving the Path to Success. Data for Action 2014. Key Findings
Author: Data Quality Campaign
Date: 2014-00-00
Type: Reports - Evaluative
Subjects: Data,Data Collection,Information Utilization,Educational Policy,State Policy,Evaluation Utilization,Governance,Data Analysis,State Departments of Education,Elementary Secondary Education,Kindergarten,Reports,Stakeholders,Preservice Teacher Education,Information Dissemination,Longitudinal Studies

Data are more than just test scores, and by effectively accessing and using different types of data--such as attendance, grades, and course-taking--teachers, parents, and school and district leaders can help ensure that every student is on a path for success every day, not just at the end of the school year. The 10 State Actions to Ensure Effective Data Use for 2011 and 2014, which are presented in this report, provide a roadmap for state policymakers to ensure that quality data are not only collected but also used by everyone with a stake in education. [For the full report, &quot;Paving the Path to Success. Data for Action 2014,&quot; see ED560597. For the summary report, &quot;Paving the Path to Success. Data for Action 2014. Summary,&quot; see ED560598.]
Teacher Data Literacy: It's about Time. A Brief for State Policymakers
Author: Data Quality Campaign
Date: 2014-02-00
Type: Reports - Descriptive
Subjects: State Policy,Teacher Competencies,Data,Technological Literacy,Teaching Skills,Definitions,Access to Information,Skill Development,Teacher Certification,Partnerships in Education,Change Strategies,Federal Regulation,Teacher Improvement,Educational Practices,Educational Strategies,Capacity Building,Technology Education

One element of quality teaching for improving student outcomes is effective data use. To date, however, policies have not gone far enough to promote the skills teachers need to be data literate. This brief for state policymakers offers a proposed definition of data literacy along with recommendations for state and federal policymakers. [The contents of this paper were developed collaboratively over one year with a group of national and state-level experts. The members of the working group are listed on page 9 of this document.]
A Stoplight for Student Data Use
Author: Data Quality Campaign
Date: 2015-00-00
Type: Guides - Non-Classroom
Subjects: Educational Legislation,Privacy,Parent Rights,Student Records,Information Utilization,Disclosure,Informed Consent

The foundational federal law on student privacy, the Family Educational Rights and Privacy Act (FERPA), establishes student privacy rights by restricting with whom and under what circumstances schools may share students' personally identifiable information. This tool summarizes some of the main provisions of FERPA, and identifies when students' personally identifiable information may be shared under the law. However, this tool should be used as a guide to help you understand when you need to take a closer look at the law or consult an expert. It should not be considered a comprehensive review of FERPA-authorized disclosures and should not be considered a substitute for appropriate legal counsel.
Student Data Collection, Access, and Storage: Separating Fact from Fiction. Safeguarding Data: Briefs for Policymakers
Author: Data Quality Campaign
Date: 2014-10-00
Type: Reports - Descriptive
Subjects: Data Collection,Student Records,Data Processing,Access to Information,Information Security,Privacy,State Policy,Information Policy,Accountability,Change Strategies,Information Management,Confidentiality

District and state data systems are constructed to ensure that individuals can access only the data that are appropriate for their role. Still, questions from the public about how these data systems work and how student privacy is protected have been increasing. A recurring concern is the feared existence of a &quot;permanent record&quot;--a single, lasting repository of a student's academic, behavioral, and administrative data that potentially could be used to harm the student. &quot;Student Data Collection, Access, and Storage: Separating Fact from Fiction&quot; is part of the Data Quality Campaign's &quot;Safeguarding Data: Briefs for Policymakers,&quot; which provide key facts and recommendations that address high-priority issues that have characterized student data privacy conversations. A list of related resources is included. [This brief was developed with contributions from: John Bailey, Keith Krueger, Chip Slaven, Richard Contartesi, Reg Leichty, Omer Tene, Dan Domagala, Greg Mortimer, Amelia Vance, Joseph Jerome, Jules Polonetsky, Elana Zeide, and Jim Siegl.]