ethanporcaro/eric-fulltext-meta huggingface dataset:
Transforming Education in Africa: An Evidence-Based Overview and Recommendations for Long-Term Improvements. A Report by UNICEF and the African Union Commission
Author: United Nations Children's Fund (UNICEF)
Date: 2021-09-00
Type: Reports - Evaluative
Subjects: Foreign Countries,Educational Change,COVID-19,Pandemics,Equal Education,Elementary Secondary Education,Population Trends,Illiteracy,Adult Literacy,Access to Education,Out of School Youth,Graduation,Vocational Education,Educational Quality,Outcomes of Education,Teacher Supply and Demand,Teacher Employment,Educational Finance,Financial Support,Barriers,Gender Differences,Rural Urban Differences,Advantaged,Access to Computers,Disadvantaged,Electronic Learning,Water,Sanitation,Hygiene,Distance Education,Class Size,Information Systems,Data

This report, which has been developed through a partnership between the African Union Commission (AUC) and UNICEF, aims to: (1) track the progress that African nations have made in education, especially in relation to Sustainable Development Goal 4 (SDG4) and the Continental Education Strategy for Africa (CESA) goals; (2) identify the challenges that African leaders and decision makers face in providing inclusive quality education, especially those related to the COVID-19 pandemic; and (3) make recommendations that could help accelerate progress in education, and prevent the COVID-19 pandemic from eroding the gains that have been made to date. The report relies primarily on data from the UNESCO Institute for Statistics (UIS) and household surveys such as the Multiple Indicator Cluster Surveys (MICS) and Demographic Health Surveys (DHS). It is based on robust statistical analysis to illustrate the progress and challenges of education in Africa. The goal is to offer African leaders and decision makers a valuable, evidence-based snapshot of the state of education across the continent of Africa, and practical ideas that they can implement in their particular countries. [This report was jointly prepared with the African Union Commission.]
Mission: Recovering Education in 2021
Author: United Nations Educational, Scientific, and Cultural Organization (UNESCO) (France)
Date: 2021-05-00
Type: Reports - Descriptive
Subjects: COVID-19,Pandemics,School Closing,Student Needs,At Risk Students,Children,Well Being,Social Emotional Learning,Technology Integration,Remedial Instruction,Teacher Education,Integrated Services,Needs Assessment,Foreign Countries,International Organizations,Partnerships in Education

The future of an entire generation is at stake. Disruptions to education systems since early 2020 have already driven substantial losses and inequalities in learning, and this is compounded by the COVID-19 pandemic that has caused abrupt and profound changes around the world. More than 1.6 billion children have lost instructional time for many months at a time, if not for much of the last year, and many children are still not back in school. However, simply reopening schools is not enough. Students will need tailored and sustained support to help them readjust and catch-up after the pandemic. UNICEF, UNESCO and World Bank have joined forces focusing on three priorities that will enable all children to return to school safely and to learn in a supportive environment, which also addresses their health and psychosocial well-being and other needs. The three priorities include: (1) All children and youth are back in school and receive the tailored services needed to meet their learning, health, psychosocial well-being, and other needs; (2) All children receive support to catch up on lost learning; and (3) All teachers are prepared and supported to address learning losses among their students and to incorporate digital technology into their teaching. UNICEF, UNESCO and World Bank, as partners, will help countries achieve this mission by the end of 2021.
Promoting and Measuring Family and Community Engagement for Healthy Early Childhood Development
Author: Emarita, Betty
Date: 2014-04-00
Type: Reports - Descriptive
Subjects: Family Involvement,Community Involvement,Child Development,Program Evaluation,Parent Surveys

Richard Chase and Betty Emarita developed and tested two tools by listening to the voices of parents and early childhood development professionals whose practices support whole child development and well-being in diverse communities. A 14-item survey tool for parents assesses healthy early childhood development from the perspective of family and community knowledge systems. It provides a vehicle for measuring and discussing extended family support and connections; family and community transmission of moral and cultural values; respect for self and others; and the extent to which families' experiences with institutions, programs, and agencies are responsive and supportive. A 15-item survey tool for providers and program staff provides a means for reflecting upon, measuring, and discussing new dimensions of quality as they relate to family and community knowledge systems, including building character and integrity in children, helping children develop a sense of self, and understanding and valuing all types of families within a community context. These two tools are shared in this article, and can be used separately or together. They can be used once to assess the current level of child development, program quality, and family and community engagement from the perspective of family and community knowledge systems. They can also be used to produce repeated measures to assess any improvements over time.
pyBKT: An Accessible Python Library of Bayesian Knowledge Tracing Models
Author: Badrinath, Anirudhan
Date: 2021-00-00
Type: Speeches/Meeting Papers
Subjects: Models,Markov Processes,Mathematics,Intelligent Tutoring Systems,Educational Technology,Programming Languages,Accuracy,Validity

Bayesian Knowledge Tracing, a model used for cognitive mastery estimation, has been a hallmark of adaptive learning research and an integral component of deployed intelligent tutoring systems (ITS). In this paper, we provide a brief history of knowledge tracing model research and introduce pyBKT, an accessible and computationally efficient library of model extensions from the literature. The library provides data generation, fitting, prediction, and cross-validation routines, as well as a simple to use data helper interface to ingest typical tutor log dataset formats. We evaluate the runtime with various dataset sizes and compare to past implementations. Additionally, we conduct sanity checks of the model using experiments with simulated data to evaluate the accuracy of its EM parameter learning and use real-world data to validate its predictions, comparing pyBKT's supported model variants with results from the papers in which they were originally introduced. The library is open source and open license for the purpose of making knowledge tracing more accessible to communities of research and practice and to facilitate progress in the field through easier replication of past approaches. [For the full proceedings, see ED615472.]
Recommending Knowledge Concepts on MOOC Platforms with Meta-Path-Based Representation Learning
Author: Piao, Guangyuan
Date: 2021-00-00
Type: Speeches/Meeting Papers
Subjects: Concept Formation,Online Courses,Navigation (Information Systems),Learning Analytics,Models

Massive Open Online Courses (MOOCs) which enable large-scale open online learning for massive users have been playing an important role in modern education for both students as well as professionals. To keep users' interest in MOOCs, recommender systems have been studied and deployed to recommend courses or videos that a user might be interested in. However, recommending courses and videos which usually cover a wide range of knowledge concepts does not consider user interests or learning needs regarding some specific concepts. This paper focuses on the task of recommending knowledge concepts of interest to users, which is challenging due to the sparsity of user-concept interactions given a large number of concepts. In this paper, we propose an approach by modeling information on MOOC platforms (e.g., teacher, video, course, and school) as a Heterogeneous Information Network (HIN) to learn user and concept representations using Graph Convolutional Networks based on user-user and concept-concept relationships via meta-paths in the HIN. We incorporate those learned user and concept representations into an extended matrix factorization framework to predict the preference of concepts for each user. Our experiments on a real-world MOOC dataset show that the proposed approach outperforms several baselines and state-of-the-art methods for predicting and recommending concepts of interest to users. [For the full proceedings, see ED615472.]
2012-2013 PBIS Mid-Year School Status Report: Cohorts 1-8
Author: Karcher-Ramos, Muneer
Date: 2013-01-00
Type: Numerical/Quantitative Data
Subjects: Positive Behavior Supports,Intervention,Program Evaluation,Program Implementation,Early Childhood Education,Elementary Secondary Education,Faculty Development,Fidelity,Student Behavior

This PBIS school status report provides a summary of the current status of all schools implementing PBIS in Minnesota with regard to participating in the Minnesota Department of Education's training sequence, implementation fidelity assessment scores, and outcomes (as appropriate). In particular, this report highlights the patterns and trends in PBIS implementation and outcomes in Minnesota, and helps to understand the relationships between training, implementation fidelity, and outcomes. This report is intended for all stakeholders to better understand the status of PBIS in Minnesota. [For the &quot;2012-2013 PBIS School Status Report: Cohorts 1-8,&quot; see ED612512.]
Back to Basics: Quality in Digital Learning
Author: Acosta, Alejandra
Date: 2021-07-00
Type: Reports - Descriptive
Subjects: COVID-19,Pandemics,School Closing,Educational Technology,Technology Uses in Education,Distance Education,Higher Education,Online Courses,Classroom Environment,Interpersonal Communication,Educational Objectives,Feedback (Response),Access to Computers,Interaction,Educational Policy,Data Collection,Research Needs,Grants,Federal Aid,Faculty Development

The pandemic was, in some ways, a sneak preview of higher education's future, one in which technology-mediated and -enabled education is ubiquitous, but with the potential for deep inequities in access to high-quality learning. This report describes some of the ways in which higher education succeeded and fell short during the remarkable 2020-21 year of distance learning. It proposes a trio of federal policy changes to build the ecosystem needed for online learning to ensure it is more equitable for students of color and low-income students, and to improve the quality for everyone.
Grow Your Own Educators in Minnesota: State Supports and Local Programs
Author: Hood, Joseph
Date: 2021-06-00
Type: Reports - Evaluative
Subjects: State Policy,Teacher Recruitment,Alternative Teacher Certification,Career Pathways,Paraprofessional School Personnel,Program Evaluation,Minority Group Teachers,Secondary School Students,Grants,Diversity (Faculty),Experiential Learning

Sometimes aided by state grants and guidance, Minnesota community partnerships are implementing Grow Your Own educator (GYO) programs to diversify the teacher workforce. The following is an examination of the growth and development of Minnesota GYO. While statewide grants fund some programs for either paraprofessionals or secondary students, the bulk of Minnesota GYO is local in nature, with partners crafting programs contextualized to the communities where they are found. Three such programs--two in the Twin Cities region and one in the rural southwest--are further highlighted. We can learn from the progress grassroots activists, policymakers, and educators have achieved advancing Minnesota GYO, with an eye toward facing the challenges that still prevent far too many children from getting the educators they deserve.
Speeding up without Loss of Accuracy: Item Position Effects on Performance in University Exams
Author: Vida, Leonardo J.
Date: 2021-00-00
Type: Speeches/Meeting Papers
Subjects: Accuracy,Test Items,Tests,Student Behavior,College Students,Test Format,Reaction Time,Foreign Countries,Hierarchical Linear Modeling

The quality of exams drives test-taking behavior of examinees and is a proxy for the quality of teaching. As most university exams have strict time limits, and speededness is an important measure of the cognitive state of examinees, this might be used to assess the connection between exams' quality and examinees' performance. The practice of randomization within university exams enables the analysis of item position effects within individual exams as a measure of speededness, and as such it enables the creation of a measure of the quality of an exam. In this research, we use generalized linear mixed models to evaluate item position effects on response accuracy and response time in a large dataset of randomized exams from Utrecht University. We find that there is an effect of item position on response time for most exams, but the same is not true for response accuracy, which might be a starting point for identifying factors that influence speededness and can affect the mental state of examinees. [For the full proceedings, see ED615472.]
Learning at Home While Under-Connected: Lower-Income Families during the COVID-19 Pandemic
Author: Katz, Vikki
Date: 2021-06-00
Type: Reports - Research
Subjects: COVID-19,Pandemics,Parent Attitudes,Low Income Groups,School Closing,Distance Education,Educational Technology,Technology Uses in Education,Barriers,Self Efficacy,Parent Role,Parent Teacher Cooperation,School Schedules,Access to Computers,Internet,Educational Media,Reading Habits,Family Role,Poverty,Ownership,Hispanic Americans,African Americans

This report presents the findings of a nationally representative, probability-based telephone survey of more than 1,000 parents of children ages three to 13, all with household incomes below the national median for families in the United States (i.e., $75,000). The survey was conducted in March and April of 2021: one year into the pandemic, and a crucial turning point. Parents could reflect on a full year of remote learning and pandemic parenting, and also look forward--thanks to the proliferation of vaccines--to their children's full and safe return to in-person schooling in the fall. But this survey goes beyond documenting families' challenges. We also uncover what parents feel they have learned through this pandemic year, from increased confidence in their ability to help their child with schoolwork to greater comfort communicating with teachers and developing a deeper understanding of their child's learning patterns. And we look ahead to the next school year, delving into what parents think schools' priorities should be for smoothing their children's transitions to, or back into, the classroom in the fall of 2021. [Additional funding for this report is from Noggin.]
Targeting Design-Loop Adaptivity
Author: Fancsali, Stephen E.
Date: 2021-00-00
Type: Speeches/Meeting Papers
Subjects: Instructional Development,Instructional Improvement,Data Use,Educational Technology,Intelligent Tutoring Systems,Artificial Intelligence,Mathematics Instruction,Mastery Learning,Middle School Students,Grade 6,Grade 7,Grade 8,Prediction

Recent work describes methods for systematic, data-driven improvement to instructional content and calls for diverse teams of learning engineers to implement and evaluate such improvements. Focusing on an approach called &quot;design-loop adaptivity,&quot; we consider the problem of how developers might use data to target or prioritize particular instructional content for improvement processes when faced with large portfolios of content and limited engineering resources to implement improvements. To do so, we consider two data-driven metrics that may capture different facets of how instructional content is &quot;working.&quot; The first is a measure of the extent to which learners struggle to master target skills, and the second is a metric based on the difference in prediction performance between deep learning and more &quot;traditional&quot; approaches to knowledge tracing. This second metric may point learning engineers to workspaces that are, effectively, &quot;too easy.&quot; We illustrate aspects of the diversity of learning content and variability in learner performance often represented by large educational datasets. We suggest that &quot;monolithic&quot; treatment of such datasets in prediction tasks and other research endeavors may be missing out on important opportunities to drive improved learning within target systems. [For the full proceedings, see ED615472.]
The Effect of an Intelligent Tutor on Performance on Specific Posttest Problems
Author: Sales, Adam
Date: 2021-00-00
Type: Speeches/Meeting Papers
Subjects: Intelligent Tutoring Systems,Academic Achievement,Educational Technology,Algebra,Item Response Theory,Hierarchical Linear Modeling,Program Effectiveness,Pretests Posttests,Achievement Tests,Elementary Secondary Education,Standardized Tests

This paper drills deeper into the documented effects of the Cognitive Tutor Algebra I and ASSISTments intelligent tutoring systems by estimating their effects on specific problems. We start by describing a multilevel Rasch-type model that facilitates testing for differences in the effects between problems and precise problem-specific effect estimation without the need for multiple comparisons corrections. We find that the effects of both intelligent tutors vary between problems-- the effects are positive for some, negative for others, and undeterminable for the rest. Next we explore hypotheses explaining why effects might be larger for some problems than for others. In the case of ASSISTments, there is no evidence that problems that are more closely related to students' work in the tutor displayed larger treatment effects. [For the full proceedings, see ED615472.]
Math Operation Embeddings for Open-Ended Solution Analysis and Feedback
Author: Zhang, Mengxue
Date: 2021-00-00
Type: Reports - Research
Subjects: Mathematics Instruction,Teaching Methods,Intelligent Tutoring Systems,Error Patterns,Feedback (Response),Error Correction,Cognitive Processes,Models,Homework,Learning Analytics,Problem Solving,Classification,Visual Aids,Generalization,Difficulty Level

Feedback on student answers and even during intermediate steps in their solutions to open-ended questions is an important element in math education. Such feedback can help students correct their errors and ultimately lead to improved learning outcomes. Most existing approaches for automated student solution analysis and feedback require manually constructing cognitive models and anticipating student errors for each question. This process requires significant human effort and does not scale to most questions used in homeworks and practices that do not come with this information. In this paper, we analyze students' step-by-step solution processes to equation solving questions in an attempt to scale up error diagnostics and feedback mechanisms developed for a small number of questions to a much larger number of questions. Leveraging a recent math expression encoding method, we represent each math operation applied in solution steps as a transition in the math embedding vector space. We use a dataset that contains student solution steps in the Cognitive Tutor system to learn implicit and explicit representations of math operations. We explore whether these representations can: (1) identify math operations a student intends to perform in each solution step, regardless of whether they did it correctly or not; and (2) select the appropriate feedback type for incorrect steps. Experimental results show that our learned math operation representations generalize well across different data distributions. [For the full proceedings, see ED615472.]
Methods for Language Learning Assessment at Scale: Duolingo Case Study
Author: Portnoff, Lucy
Date: 2021-00-00
Type: Reports - Research
Subjects: Second Language Learning,Language Tests,Educational Technology,Electronic Learning,Data Collection,Learning Analytics,Handheld Devices

Students using self-directed learning platforms, such as Duolingo, cannot be adequately assessed relying solely on responses to standard learning exercises due to a lack of control over learners' choices in how to utilize the platform: for example, how learners choose to sequence their studying and how much they choose to revisit old material. To provide accurate and well-controlled measurement of learner achievement, Duolingo developed two methods for injecting test items into the platform, which combined with Educational Data Mining techniques yield insights important for product development and curriculum design. We briefly discuss the unique characteristics and advantages of these two systems - Checkpoint Quiz and Review Exercises. We then present a case study investigating how different study approaches on Duolingo relate to learning outcomes as measured by these assessments. We demonstrate some of the unique benefits of these systems and show how educational data mining approaches are central to making use of this assessment data. [For the full proceedings, see ED615472.]
Online Estimation of Student Ability and Item Difficulty with Glicko-2 Rating System on Stratified Data
Author: Park, Jaesuk
Date: 2021-00-00
Type: Reports - Evaluative
Subjects: Academic Ability,Difficulty Level,Evaluation Methods,Elementary Secondary Education,Mathematics Education,Educational Technology,Item Response Theory

We propose an adaptation of the Glicko-2 rating system in a K-12 math learning software setting, where variable time intervals between solution attempts and the stratification of student-item pairings by grade levels necessitate modification of the original model. The discrete-time stochastic process underlying the original system has been modified into a continuous-time process to account for the irregularity of intervals between solution attempts. Also, conceptual prerequisite relationships between items were used to provide initial rating estimates that allow for rating values to be meaningfully compared across grade levels. Fitting the model using real student learning data results in rating value distributions successfully exhibiting a gradation with the increase of grade level. A potential area of application in a personalized education setting is also briefly discussed. [For the full proceedings, see ED615472.]
Sentiment Analysis of Student Surveys -- A Case Study on Assessing the Impact of the COVID-19 Pandemic on Higher Education Teaching
Author: Jiménez, Haydée G.
Date: 2021-00-00
Type: Reports - Research
Subjects: Natural Language Processing,Data Analysis,Classification,Student Surveys,College Students,Student Evaluation of Teacher Performance,Conventional Instruction,Online Courses,COVID-19,Pandemics,Models,Foreign Countries

Sentiment Analysis is a field of Natural Language Processing which aims at classifying the author's sentiment in text. This paper first describes a sentiment analysis model for students' comments about professor performance. The model achieved impressive results for comments collected from student surveys conducted at a private university in 2019/20. Then, it applies the model to different scenarios: (1) in-person classes taught in 2019 (pre-COVID); (2) the emergency shift to online, synchronous classes taught in the first semester of 2020 (early-COVID); and (3) the planned online classes taught in the second semester of 2020 (late-COVID). The results show that students acknowledged the effort professors did to keep classes running during the first semester of 2020, and that the enthusiasm continued throughout the second semester. Furthermore, the results show that students evaluated professors' performance for online courses better than for in-person courses. [For the full proceedings, see ED615472.]