Afterschool in Action: Innovative Afterschool Programs Supporting Middle School Youth

This report, released by Afterschool Alliance in partnership with MetLife Foundation, highlights the work of quality afterschool programs that support children, families and communities across the nation.This compendium is a compilation of four issue briefs examining critical issues facing middle school youth and the vital role afterschool programs play in addressing these issues. This series explores afterschool and: arts enrichment, parent engagement, school improvement and digital learning. The compendium also includes in-depth profiles of the 2012 Afterschool Innovator Award winners, as well as highlights from 2008-2011 award winners.The 2012 MetLife Foundation Afterschool Award winners are:The Wooden Floor, Santa Ana, CALatino Arts Strings & Mariachi Juvenil, Milwaukee, WIKid Power Inc., The VeggieTime Project, Washington, D.C.Parma Learning Center, Parma, IDGreen Energy Technologies in the City, Lansing, M

Kresge Foundation 2010-2011 Annual Report

Contains an introduction to Kresge's strategy; board chair's letter; president's letter; foundation timeline; program information; grant summary, including geographic distribution; grants lists; financial summary; and lists of board members and staff

Lloyd A. Fry Foundation - 2003 Annual Report

Contains mission statement, board chair's and executive director's messages, program information, grantee profiles, grants list, financial statements, grant guidelines, and lists of board members and staff

Evaluation of the New York City Department of Youth and Community Development Out-of-School Time Programs for Youth Initiative: Implementation of Programs for High School Youth

Evaluates the Out-of-School Time Programs for Youth initiative's academic enhancement and recreational programs for high school youth, including enrollment, staff expertise, age-appropriate activities, and program partnerships to increase resources

Predicting Student Performance on Virtual Learning Environment

Virtual learning has gained increased importance because of the recent pandemic situation. A mass shift to virtual means of education delivery has been observed over the past couple of years, forcing the community to develop efficient performance assessment tools. Prediction of students performance using different relevant information has emerged as an efficient tool in educational institutes towards improving the curriculum and teaching methodologies. Automated analysis of educational data using state of the art Machine Learning (ML) and Artificial Intelligence (AI) algorithms is an active area of research. The research presented in this thesis addresses the problem of students performance prediction comprehensively by applying multiple machine learning models (i.e., Multilayer Perceptron (MLP), Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGBoost), CATBoost, K-Nearest Neighbour (KNN) and Support Vector Classifier (SVC)) on the two benchmark VLE datasets (i.e., Open University Learning Analytics Dataset (OULAD), Coursera). In this context, a series of experiments are performed and important insights are reported. First, the classification performance of machine learning models has been investigated on both OULAD and Coursera datasets. In the second experiment, performance of machine learning models is studied for each course of Coursera dataset and comparative analysis are performed. From the Experiment 1 and Experiment 2, the class imbalance is reported as the highlighted factor responsible for degraded performance of machine learning models. In this context, Experiment 3 is designed to address the class imbalance problem by making use of multiple Synthetic Minority Oversampling Technique (SMOTE) and generative models (i.e., Generative Adversial Networks (GANs)). From the results, SMOTE NN approach was able to achieve best classification performance among the implemented SMOTE techniques. Further, when mixed with generative models, the SMOTENN-GAN generated Coursera dataset was the best on which machine learning models were able to achieve the classification accuracy around 90%. Overall, MLP, XGBoost and CATBoost machine learning models were emerged as the best performing in context to different experiments performed in this thesis

Exploring the Semblant Effects of COVID-19 on Minnesota High School Band Programs

At the time of this writing, the impact of the recent COVID-19 pandemic on music education at the secondary level is yet unknown. This study aims to collect and review available data to determine if the COVID-19 pandemic may have had an effect on participation in high school band in Minnesota. This convergent parallel mixed methods research study examines statistical data available through the Freedom of Information Act from a sampling of 167 out of a possible 476 public high schools in Minnesota to determine a potential impact between the COVID-19 pandemic and enrollment in high school band. This is achieved by reviewing high school band enrollment trends from the past five years and comparing them to the difference in enrollment levels in the falls of 2019, 2020, and 2021. A survey was also sent to Minnesota band directors to assess their perspectives regarding the effects of the COVID-19 pandemic on their band programs. The survey was distributed electronically, and seventy-seven Minnesota high school band directors completed the survey. The results of the study observed a decrease in high school band participation from the fall of 2019, before the COVID-19 pandemic, to the fall of 2021. The data show a decrease in enrollment in Minnesota high school band programs following the onset of the COVID-19 pandemic. Band director feedback is consistent with these findings. This project serves as an early benchmark in understanding how the COVID-19 pandemic may have affected participation in band in Minnesota high schools, and as an early metric on which future research regarding band participation and the COVID-19 pandemic may be based

Proceedings of the ANR ​#CreaMaker​ workshop: co-creativity, robotics and maker education.

International audienc

ANR #CreaMaker workshop : Co-creativity, robotics and maker educationProceedings

International audienceWe’re living exciting but also challenging times at the worldwide level. From one side, there are environmental challenges that can compromise our future as humanity and the socio economic tensions generated in a context of mass consumption within a model of fossil and nuclear energy which endangers a sustainable development. From the other side, we have a growing number of citizen-based initiatives aiming to improve the society and the technological infrastructures making possible to cooperate at large scale and not only at a small-group level. Younger becomes empowered for their future. In their initiatives such #FridaysForFuture they are no longer (interactive) media consumers but move forward as creative activists to make older generations change the system in order to save the planet. At the same time, we have observed in the last years the emergence of a wide diversity of third places (makerspace, fablab, living lab…) aiming to empower communities to design and develop their own creative solutions. In this context, maker-based projects have the potential to integrate tinkering, programming and educational robotics to engage the learner in the development of creativity both in individual and collaborative contexts (Kamga, Romero, Komis, & Mirsili, 2016). In this context, the ANR #CreaMaker project aims to analyse the development of creativity in the context of team-based maker activities combining tinkering and digital fabrication (Barma, Romero, & Deslandes, 2017; Fleming, 2015). This first workshop of the ANR #CreaMaker project aims to raise the question on the concept, activities and assessment of creativity in the context of maker education and its different approaches : computational thinking (Class’Code, AIDE), collective innovation (Invent@UCA), game design (Creative Cultures), problem solving (CreaCube), child-robot interactions and sustainable development activities. Researchers from Canada, Brazil, Mexico, Germany, Italy and Spain will reunite with LINE researchers and the MSc SmartEdTech students in order to advance in how we can design, orchestrate and evaluate co-creativity in technology enhanced learning (TEL) contexts, and more specifically, in maker based education