ethanporcaro/eric-fulltext-meta huggingface dataset:
ALL-IN-ONE: Multi-Task Learning BERT Models for Evaluating Peer Assessments
Author: Jia, Qinjin
Date: 2021-00-00
Type: Reports - Research
Subjects: Peer Evaluation,Models,Artificial Intelligence,Evaluation Methods,Automation,Natural Language Processing,Graduate Students,Engineering Education,Computer Uses in Education

Peer assessment has been widely applied across diverse academic fields over the last few decades, and has demonstrated its effectiveness. However, the advantages of peer assessment can only be achieved with high-quality peer reviews. Previous studies have found that high-quality review comments usually comprise several features (e.g., contain suggestions, mention problems, use a positive tone). Thus, researchers have attempted to evaluate peer-review comments by detecting different features using various machine learning and deep learning models. However, there is no single study that investigates using a multi-task learning (MTL) model to detect multiple features simultaneously. This paper presents two MTL models for evaluating peer-review comments by leveraging the state-of-the-art pre-trained language representation models BERT and DistilBERT. Our results demonstrate that BERT-based models significantly outperform previous GloVe-based methods by around 6% in F1-score on tasks of detecting a single feature, and MTL further improves performance while reducing model size. [For the full proceedings, see ED615472.]
A Novel Algorithm for Aggregating Crowdsourced Opinions
Author: Prihar, Ethan
Date: 2021-00-00
Type: Reports - Research
Subjects: Computer Software,Middle School Teachers,Mathematics Teachers,College Students,Teacher Attitudes,Student Attitudes,Course Content,Guidelines,Reliability,Simulation,Grade 7,Problem Solving,Learning Processes,Measurement Techniques,Online Courses,Intelligent Tutoring Systems,Accuracy,Task Analysis,Comparative Analysis

Similar content has tremendous utility in classroom and online learning environments. For example, similar content can be used to combat cheating, track students' learning over time, and model students' latent knowledge. These different use cases for similar content all rely on different notions of similarity, which make it difficult to determine contents' similarities. Crowdsourcing is an effective way to identify similar content in a variety of situations by providing workers with guidelines on how to identify similar content for a particular use case. However, crowdsourced opinions are rarely homogeneous and therefore must be aggregated into what is most likely the truth. This work presents the Dynamically Weighted Majority Vote method. A novel algorithm that combines aggregating workers' crowdsourced opinions with estimating the reliability of each worker. This method was compared to the traditional majority vote method in both a simulation study and an empirical study, in which opinions on seventh grade mathematics problems' similarity were crowdsourced from middle school math teachers and college students. In both the simulation and the empirical study the Dynamically Weighted Majority Vote method outperformed the traditional majority vote method, suggesting that this method should be used instead of majority vote in future crowdsourcing endeavors. [For the full proceedings, see ED615472.]
Combining Cognitive and Machine Learning Models to Mine CPR Training Histories for Personalized Predictions
Author: Sense, Florian
Date: 2021-00-00
Type: Speeches/Meeting Papers
Subjects: First Aid,Training,Artificial Intelligence,Prediction,Computer Uses in Education,Models,Computer Simulation,Nursing Students

Cardiopulmonary resuscitation (CPR) is a foundational lifesaving skill for which medical personnel are expected to be proficient. Frequent refresher training is needed to prevent the involved skills from decaying. Regular low-dose, high-frequency training for staff at fixed intervals has proven successful at maintaining CPR competence but does not take into account inherent performance variability across learners. Tailoring refreshers to an individual's past performance would minimize personnel being trained too (in)frequently and would ensure faster knowledge acquisition for new learners. To maximize the benefits of individualized schedules, learning needs gleaned from past training history need to be identified. A recent field study conducted among nursing students showed that a cognitive model-based approach was able to successfully trace the knowledge acquisition and decay of learners and prescriptively devise personalized training regimes that outperformed fixed schedules with regards to both training efficiency and learners' performance. Here, we report a post-hoc analysis of the collected data to investigate whether an alternative modeling approach, blending cognitive modeling and machine learning, could have resulted in even higher quality predictions. Our results reveal modest improvements in predictive accuracy for ensemble models, in which machine learning models predict the prediction errors (i.e., residuals) of the standalone cognitive model. These promising findings reveal strong applied utility for future use in domains where sustained proficiency is a requirement. [For the full proceedings, see ED615472.]
Identification of Gifted and Talented English Learner Students in Grades K-5 in Texas: Lessons from Student-Level Data. Policy Brief
Author: Ruiz de Castilla, Verónica
Date: 2017-11-00
Type: Reports - Descriptive
Subjects: Talent Identification,Gifted Education,Academically Gifted,English Language Learners,Children,Elementary School Students,Kindergarten,African American Students,Hispanic American Students,Economically Disadvantaged,Student Participation,Disproportionate Representation,Statewide Planning,School Demography,Public Schools,Instructional Program Divisions

The members of Regional Educational Laboratory (REL) Southwest's Texas English Learners Research Alliance requested this project because they were interested in learning more about the participation of current English learner students in gifted and talented (GT) programs in Texas, and how their participation compared with that of former English learner students and students who were never English learner students. A series of studies have been conducted to find out more about GT participation rates of current and former English learner students and students who were never English learner students. The purpose of this first component of the project was to use descriptive, nonexperimental analyses to find out if current English learner students were underrepresented in GT programs in Texas public schools, and to further explore data on current and former English learner students in schools serving grades K-5. This brief shares results from four primary research questions: (1) How does the participation of current English learner students in GT programs compare with their participation in the total student population in Texas public schools?; (2) What percentage of Texas schools serving grades K-5 have proportional representation of current and former English learner students in their general student population and in their GT population?; (3) In what elementary grades are current, former, and never English learner students first identified as gifted?; and (4) How do the rates of GT participation at each grade level compare among current, former, and never English learner students?
Can Feature Predictive Power Generalize? Benchmarking Early Predictors of Student Success across Flipped and Online Courses
Author: Marras, Mirko
Date: 2021-00-00
Type: Speeches/Meeting Papers
Subjects: Benchmarking,Predictor Variables,Academic Achievement,Flipped Classroom,Online Courses,Audience Response Systems,Learning Analytics,Mass Instruction,Teaching Methods,At Risk Students,Computer Software,Mathematics Instruction,Undergraduate Students,Advanced Courses

Early predictors of student success are becoming a key tool in flipped and online courses to ensure that no student is left behind along course activities. However, with an increased interest in this area, it has become hard to keep track of what the state of the art in early success prediction is. Moreover, prior work on early success prediction based on clickstreams has mostly focused on implementing features and models for a specific online course (e.g., a MOOC). It remains therefore under-explored how different features and models enable early predictions, based on the domain, structure, and educational setting of a given course. In this paper, we report the results of a systematic analysis of early success predictors for both flipped and online courses. In the first part, we focus on a specific flipped course. Specifically, we investigate eight feature sets, presented at top-level educational venues over the last few years, and a novel feature set proposed in this paper and tailored to this setting. We benchmark the performance of these feature sets using a RF classifier, and we provide and discuss an ensemble feature set optimized for the target flipped course. In the second part, we extend our analysis to courses with different educational settings (i.e., MOOCs), domains, and structure. Our results show that (i) the ensemble of optimal features varies depending on the course setting and structure, and (ii) the predictive performance of the optimal ensemble feature set highly depends on the course activities. [For the full proceedings, see ED615472.]
Keeping Children Safe in Education, 2021: Statutory Guidance for Schools and Colleges
Author: Department for Education (DfE) (United Kingdom)
Date: 2021-09-00
Type: Legal/Legislative/Regulatory Materials
Subjects: School Safety,Child Safety,Legal Responsibility,School Administration,Child Abuse,Sexual Abuse,Violence,Sexual Harassment,Recruitment,School Personnel,Elementary Secondary Education,Postsecondary Education,Children,Adolescents,Teacher Student Relationship,Peer Relationship,Disclosure,Foreign Countries

This guidance applies to all schools and colleges and is for: headteachers, teachers and staff governing bodies, proprietors and management committees It sets out the legal duties that must be followed to safeguard and promote the welfare of children and young people under the age of 18 in schools and colleges. It is divided into five parts: (1) Safeguarding information for all staff; (2) The management of safeguarding; (3) Safer recruitment; (4) Allegations made against/Concerns raised in relation to teachers, including supply teachers, other staff, volunteers and contractors; and (5) Child on child sexual violence and sexual harassment. [For the 2018 report, see ED590224.]
Science and Engineering Labor Force. Science and Engineering Indicators 2020. NSB-2019-8
Author: Burke, Amy
Date: 2019-09-26
Type: Numerical/Quantitative Data
Subjects: Labor Force,Technical Occupations,Engineering,Scientists,Educational Background,Academic Degrees,Employment,Industry,Doctoral Degrees,College Faculty,Research and Development,Unemployment,Income,College Graduates,Entry Workers,Postdoctoral Education,Skilled Workers,Labor Market,Demography,Employed Women,Minority Groups,Salaries,Salary Wage Differentials,Immigration,Foreign Workers

The science and engineering (S&amp;E) labor force helps to create and advance our scientific and technological knowledge, transform these advances into goods and services, and fuel America's economy, security, and quality of life. This report details several aspects of the U.S. S&amp;E workforce, including growth, demographic makeup, earnings, and unemployment. Nearly 7 million individuals work in S&amp;E jobs, and almost 25 million hold an S&amp;E bachelor's degree or higher. In addition, over 17 million people constitute the skilled technical workforce--individuals who use S&amp;E skills in their job but do not have a bachelor's degree. [SRI International, Center for Innovation Strategy and Policy, assisted with report preparation.]
Research and Development: U.S. Trends and International Comparisons. Science and Engineering Indicators 2020. NSB-2020-3
Author: Boroush, Mark
Date: 2020-01-15
Type: Numerical/Quantitative Data
Subjects: Research and Development,Financial Support,Trend Analysis,Performance,Federal Aid,Scientific Research,Business,Higher Education,Public Agencies,Research and Development Centers,Nonprofit Organizations,Foreign Countries

The U.S. R&amp;D enterprise relies on businesses, federal and nonfederal governments, higher education institutions, and other nonprofit organizations. This report identifies trends in R&amp;D performance and funding and compares them to those of the world's other major economies. The U.S. annual total of R&amp;D has expanded steadily since 2010 (totaling $548 billion in 2017), mostly due to sizable annual increases in business R&amp;D performance. In 2017, the United States remained the world's top R&amp;D performer. However, the global concentration of R&amp;D continues to shift from the United States and Europe to South Asia and East-Southeast Asia. [SRI International, Center for Innovation Strategy and Policy, assisted with report preparation. Research and development performed by higher education institutions is treated in more detail in the &quot;Indicators 2020&quot; reports &quot;Publications Output: U.S. Trends and International Comparisons. Science &amp; Engineering Indicators 2020. NSB-2020-6&quot; (ED615534) and &quot;Academic Research and Development. Science &amp; Engineering Indicators 2020. NSB-2020-2&quot; (ED615447).]
Championing Early Childhood Policies That Prevent Social, Economic, and Educational Inequities: Proposed Public Policy Approach, Principles, and Priorities
Author: Chase, Richard
Date: 2015-05-00
Type: Reports - Descriptive
Subjects: Early Childhood Education,Educational Policy,Equal Education,Achievement Gap,Child Development,Educational Legislation,Financial Support,Educational Finance,Economic Status,Social Status,Disadvantaged

In response to the 2015 Minnesota legislative debate focused on increasing access to early education, this brief proposes a new policy approach that would prevent opportunity and achievement gaps and social, economic, and educational inequities.
The Economic Impacts of the Child Care Shortage in Northeastern Minnesota: Opportunities to Partner on Solutions
Author: Chase, Richard
Date: 2018-07-00
Type: Reports - Research
Subjects: Child Care,Supply and Demand,Economic Impact,Costs,Geographic Regions,Regional Characteristics,Child Care Centers,Best Practices,Employers

This report presents the number of child care providers and children enrolled in northeastern Minnesota in 2018, the economic impact of the child care industry, estimates of the unmet need for child care and costs to add capacity to meet demand, the economic impact of the child care shortage, and examples of best-practice business involvement in child care solutions. [The Blandin Foundation, the Northland Foundation, and the Minnesota Department of Iron Range Resources and Rehabilitation (IRRR) commissioned Wilder Research and the Center for Rural Policy and Development (CRPD) to prepare this report.]
Prenatal to Age 3: A Comprehensive, Racially-Equitable Policy Plan for Universal Healthy Child Development
Author: Chase, Richard
Date: 2016-09-00
Type: Reports - Descriptive
Subjects: Infants,Public Policy,Child Health,Child Development,Family Programs,Community Programs,Economic Development,Financial Support,Access to Health Care,Holistic Approach,Racial Bias,Ethnicity,Social Justice,Equal Education,State Policy,Prenatal Care,Access to Education,At Risk Persons,Intervention,Minority Groups,Poverty,Intergenerational Programs,Cultural Influences

This policy plan offers an approach to early childhood policies that get to the roots of racial equity. It describes how to promote social, economic, health, and educational equity by broadening the current early childhood public policy focus in Minnesota on early education and service programs to also include funding efforts that promote positive early experiences; safe, stable, and nurturing relationships; and economic security and by building upon the assets and capacities of all families and communities. The interconnected components for public policy action included in this plan include supporting formal and informal multigenerational family and community support networks; investment in family and community economic development and security; the establishment of a flexible funding stream for community-led solutions; and the continuation of advancement in prenatal to age 3 holistic service and funding strategies, coordinated across systems and agencies. [Development and Training, Inc. co-produced this report.]
Analyzing Student Success and Mistakes in Virtual Microscope Structure Search Tasks
Author: PaaBen, Benjamin
Date: 2021-00-00
Type: Speeches/Meeting Papers
Subjects: Anatomy,Science Instruction,Computer Simulation,Computer Software,Laboratory Experiments,Laboratory Equipment,Prediction,Item Response Theory,Task Analysis,Academic Ability,Difficulty Level,Error Patterns,Misconceptions,Undergraduate Students,Foreign Countries

Many modern anatomy curricula teach histology using virtual microscopes, where students inspect tissue slices in a computer program (e.g. a web browser). However, the educational data mining (EDM) potential of these virtual microscopes remains under-utilized. In this paper, we use EDM techniques to investigate three research questions on a virtual microscope dataset of N = 1, 460 students. First, which factors predict the success of students locating structures in a virtual microscope? We answer this question with a generalized item response theory model (with 77% test accuracy and 0.82 test AUC in 10-fold cross-validation) and find that task difficulty is the most predictive parameter, whereas student ability is less predictive, prior success on the same task and exposure to an explanatory slide are moderately predictive, and task duration as well as prior mistakes are not predictive. Second, what are typical locations of student mistakes? And third, what are possible misconceptions explaining these locations? A clustering analysis revealed that student mistakes for a difficult task are mostly located in plausible positions ('near misses') whereas mistakes in an easy task are more indicative of deeper misconceptions. [For the full proceedings, see ED615472.]
Behavioral Testing of Deep Neural Network Knowledge Tracing Models
Author: Kim, Minsam
Date: 2021-00-00
Type: Speeches/Meeting Papers
Subjects: Models,Artificial Intelligence,Knowledge Level,Evaluation Methods

Knowledge Tracing (KT) is a task to model students' knowledge based on their coursework interactions within an Intelligent Tutoring System (ITS). Recently, Deep Neural Networks (DNN) showed superb performance over classical methods on multiple dataset benchmarks. While most Deep Learning based Knowledge Tracing (DLKT) models are optimized for general objective metrics such as accuracy or AUC on benchmark data, proper deployment of the service requires additional qualities. Moreover, the black-box nature of DNN models makes them particularly difficult to diagnose or improve when unexpected behaviors are encountered. In this context, we adopt the idea of black-box testing / behavioral testing from Software Engineering and (1) define desirable KT model behaviors to (2) propose a KT model analysis framework to diagnose the model's behavioral quality. We test-run the framework using three state-of-the-art DLKT models on seven datasets based on the proposed framework. The result highlights the impact of dataset size and model architecture upon the model's behavioral quality. The assessment results from the proposed framework can be used as an auxiliary measure of the model performance by itself, but can also be utilized in model improvements via data-augmentation, architecture design, and loss formulation. [For the full proceedings, see ED615472.]
FY 2022 Supplemental Nutrition Assistance Program Education (SNAP-Ed) Plan Guidance. Nutrition Education and Obesity Prevention Program
Author: Food and Nutrition Service (FNS) (USDA), Supplemental Nutrition Assistance Program Education (SNAP-Ed)
Date: 2022-00-00
Type: Guides - General
Subjects: Federal Programs,Nutrition Instruction,Obesity,Prevention,Health Promotion,Grants,State Programs,Intervention,Marketing,Evidence Based Practice,State Aid,American Indians,Technical Writing,Reports,Information Management,Program Development,Planning,Annual Reports,State Agencies,Resource Allocation,Recordkeeping,Costs,Audiences,Objectives,Budgets,Eating Habits,Food,Agency Cooperation,Cooperative Planning,Welfare Services,Nutrition

This Fiscal Year (FY) 2022 Supplemental Nutrition Assistance Program Education (SNAP-Ed) Plan Guidance provides instructions for developing and submitting State nutrition education and obesity prevention grant program plans, commonly referred to as State SNAP-Ed Plans. It describes Food and Nutrition Service (FNS) expectations regarding State SNAP-Ed requirements and includes examples of activities that can be used in SNAP-Ed programming. Some sections have been broadened to provide additional instructions and in some places, new information has been added. The SNAP-Ed Plan Guidance implements provisions of the FNA, Section 28, as amended by the Agriculture Improvement Act of 2018 (7 U.S.C. 2036a) and 7 CFR(d)(2). [This report replaces &quot;FY 2021 Supplemental Nutrition Assistance Program Education Plan Guidance&quot; (ED615514).]
FY 2021 Supplemental Nutrition Assistance Program Education Plan Guidance. Nutrition Education and Obesity Prevention Program
Author: Food and Nutrition Service (FNS) (USDA), Supplemental Nutrition Assistance Program Education (SNAP-Ed)
Date: 2021-00-00
Type: Guides - General
Subjects: Federal Programs,Nutrition Instruction,Obesity,Prevention,Health Promotion,Grants,State Programs,Intervention,Marketing,Evidence Based Practice,State Aid,American Indians,Technical Writing,Reports,Information Management,Program Development,Planning,Annual Reports,State Agencies,Resource Allocation,Recordkeeping,Costs,Audiences,Objectives,Budgets,Eating Habits,Food,Agency Cooperation,Cooperative Planning

This Fiscal Year (FY) 2021 Supplemental Nutrition Assistance Program Education (SNAP-Ed) Plan Guidance provides instructions for developing and submitting State nutrition education and obesity prevention grant program plans, commonly referred to as State SNAP-Ed Plans. It describes Food and Nutrition Service (FNS) expectations regarding State SNAP-Ed requirements and includes examples of activities that can be used in SNAP-Ed programming. Some sections have been broadened to provide additional instructions and in some places, new information has been added. The SNAP-Ed Plan Guidance implements provisions of the FNA, Section 28, as amended by the Agriculture Improvement Act of 2018 (7 U.S.C. 2036a) and 7 CFR(d)(2). [This report replaces &quot;FY 2020 Supplemental Nutrition Assistance Program Education Plan Guidance.&quot;]