Scaffolding Kindergarten Writing For English Language Learners

Decades of research has documented the achievement gap between English Learners (ELs) and non-ELs in U.S. schools. According to some measures, one of the areas in which ELs struggle most is writing, an area which is often ignored especially in early childhood and kindergarten classrooms. Furthermore, many teachers report that they feel underprepared to meet the needs of ELs in their classroom. This capstone project addresses the particular challenge of teaching writing to kindergarten ELs and answers the question: What information and strategies would support elementary teachers when scaffolding writing curriculum and instruction to meet the unique needs of ELs? To answer this question, this capstone delves into the current literature surrounding ELs, instructional scaffolding, and kindergarten writing. From information gathered from researched best practices, the resulting capstone project is a PDF of instructional resources designed to scaffold the writing process and support ELs’ during their writing block. These resources specifically are designed to add additional supports to the kindergarten persuasive writing unit in the Lucy Calkins’ writer’s workshop curriculum. Limitations of this capstone include the project’s lack of testing, limited audience and scope, and lack of control over implementation of project resources. There are a number of implications for this capstone project as well, including an impact on teacher professional development, writing instruction resources, efficiency, and writing outcomes for ELs. Overall, this capstone provides instructional scaffolding techniques as a solution to help narrow the pervasive achievement gap between ELs and non-ELs and to promote writing success for ELs in kindergarten

A Delphi‑based expert judgment method applied to the validation of a mature Agile framework for Web development projects

The validation of any new methodological proposal demands several real-life implementations. However, organizations are reluctant to invest without the firm guarantee that they will be returned the entire expended amount of money. For this purpose, expert judgment techniques are very useful to provide a less-costly initial validation that, when positive, may encourage organizations to use these new proposals. Therefore, the primary goal of the paper will be to assess how expert judgment techniques based on the Delphi method can be applied to Web Engineering field and, more in particular, to assess the validity of the NDT-Agile framework. NDT-Agile is a framework that combines Agile and Web Engineering techniques to meet Capability Maturity Model Integration development goals. The paper presents a real example of an application of a Delphi-based expert judgment method to assess NDT-Agile framework validity, explaining the design as well as the selection and usage of the different techniques it involves. The application of the method will allow assessing benefits and limitations of use in Web Engineering. As a main conclusion, we will state the utility of the proposed methods to obtain a low-resource initial validation of a certain proposal. Finally, we will identify further lines of research related to the analyzed topics.Ministerio de Ciencia e Innovación TIN2013-46928-C3-3-RMinisterio de Ciencia e Innovación TIN2015-71938-RED

VCU voice (1995-10-30)

VCU Today, the University’s first official administrative organ, began as a somewhat irregular monthly publication but moved to a bi-weekly newspaper format in the 1980s. The newspaper changed its name to VCU Voice in 1988 and ten years later it appeared under the title UniverCity News. As it neared the end of its run as a physical newspaper, the publication became simply VCU News. These four publications were essentially the same periodical published under different titles by the Office of University Relations. VCU News appeared online for the first time in 2002.https://scholarscompass.vcu.edu/vcv/1140/thumbnail.jp

Teaching Solidarity: Popular Education in Grassroots U.S. Social Movements

Fifty years after he wrote Pedagogy of the Oppressed (1970), Brazilian educator Paulo Freire’s work is as relevant as ever. But while many of Freire’s ideas are well known in the United States, there is limited research on their application in social movement settings, a practice commonly known as popular education. This comparative case study draws on Freire’s theory of popular education to analyze two U.S.-based grassroots education programs, one with low-income residents in the Tenderloin neighborhood of San Francisco and one with front-line hospital and public school employees on the East Coast. Through six months of participant observation and over 50 interviews with facilitators and participants, the study finds that the two programs carved out spaces that were relatively independent from union and non-profit hierarchies, which enabled them to apply popular education’s radically democratic principles within their organizing work and larger social struggles. These findings point to the possibility that popular education can offer participants not only knowledge and skills but also–and perhaps more importantly–strengthened connections across divisions, confidence that they can make change, and the courage to organize. The dissertation also expands on commonly understood meanings of “critical consciousness,” arguing that what moves people to action may be not only their intellectual understanding of power, but also an increased solidarity that gives them an awareness of their collective historical agency. Finally, the study identifies tensions in the programs, for example related to funding constraints, that at times interfered with facilitators’ abilities to apply the radical principles of popular education. These findings speak to the value of a reflective practice on the part of practitioners, and highlight the ongoing significance of Freirean popular education in U.S. social movement contexts

Annual Report 2020-2021

LETTER FROM THE DEAN As I write this letter during the beginning of the 2021–22 academic year, we have started to welcome the majority of our students to campus— many for the very first time, and some for the first time in a year and a half. It has been wonderful to be together, in-person, again. Four quarters of learning and working remotely was challenging, to be sure, but I have been consistently amazed by the resilience, innovation, and hard work of our students, faculty, and staff, even in the most difficult of circumstances. This annual report, covering the 2020–21 academic year—one that was entirely virtual—highlights many of those examples: from a second place national ranking by our Security Daemons team to hosting a blockbuster virtual screenwriting conference with top talent; from gaming grants helping us reach historically excluded youth to alumni successes across our three schools. Recently, I announced that, after 40 years at DePaul and 15 years as the Dean of CDM, I will be stepping down from the deanship at the end of the 2021–22 academic year. I began my tenure at DePaul in 1981 as an assistant professor, with the founding of the Department of Computer Science, joining seven faculty members who were leaving the mathematics department for this new venture. It has been amazing to watch our college grow during that time. We now have more than 40 undergraduate and graduate degree programs, over 22,000 college alumni, and a catalog of nationally ranked programs. And we plan to keep going. If there is anything I’ve learned at CDM, it’s that a lot can be accomplished in a year (as this report shows), and I’m committed to working hard and continuing the progress we’ve made together in 2021–22. David MillerDeanhttps://via.library.depaul.edu/cdmannual/1004/thumbnail.jp

A community-powered search of machine learning strategy space to find NMR property prediction models

The rise of machine learning (ML) has created an explosion in the potential strategies for using data to make scientific predictions. For physical scientists wishing to apply ML strategies to a particular domain, it can be difficult to assess in advance what strategy to adopt within a vast space of possibilities. Here we outline the results of an online community-powered effort to swarm search the space of ML strategies and develop algorithms for predicting atomic-pairwise nuclear magnetic resonance (NMR) properties in molecules. Using an open-source dataset, we worked with Kaggle to design and host a 3-month competition which received 47,800 ML model predictions from 2,700 teams in 84 countries. Within 3 weeks, the Kaggle community produced models with comparable accuracy to our best previously published "in-house" efforts. A meta-ensemble model constructed as a linear combination of the top predictions has a prediction accuracy which exceeds that of any individual model, 7-19x better than our previous state-of-the-art. The results highlight the potential of transformer architectures for predicting quantum mechanical (QM) molecular properties

Automatic generation of natural language descriptions of visual data: describing images and videos using recurrent and self-attentive models

Humans are faced with a constant flow of visual stimuli, e.g., from the environment or when looking at social media. In contrast, visually-impaired people are often incapable to perceive and process this advantageous and beneficial information that could help maneuver them through everyday situations and activities. However, audible feedback such as natural language can give them the ability to better be aware of their surroundings, thus enabling them to autonomously master everyday's challenges. One possibility to create audible feedback is to produce natural language descriptions for visual data such as still images and then read this text to the person. Moreover, textual descriptions for images can be further utilized for text analysis (e.g., sentiment analysis) and information aggregation. In this work, we investigate different approaches and techniques for the automatic generation of natural language of visual data such as still images and video clips. In particular, we look at language models that generate textual descriptions with recurrent neural networks: First, we present a model that allows to generate image captions for scenes that depict interactions between humans and branded products. Thereby, we focus on the correct identification of the brand name in a multi-task training setting and present two new metrics that allow us to evaluate this requirement. Second, we explore the automatic answering of questions posed for an image. In fact, we propose a model that generates answers from scratch instead of predicting an answer from a limited set of possible answers. In comparison to related works, we are therefore able to generate rare answers, which are not contained in the pool of frequent answers. Third, we review the automatic generation of doctors' reports for chest X-ray images. That is, we introduce a model that can cope with a dataset bias of medical datasets (i.e., abnormal cases are very rare) and generates reports with a hierarchical recurrent model. We also investigate the correlation between the distinctiveness of the report and the score in traditional metrics and find a discrepancy between good scores and accurate reports. Then, we examine self-attentive language models that improve computational efficiency and performance over the recurrent models. Specifically, we utilize the Transformer architecture. First, we expand the automatic description generation to the domain of videos where we present a video-to-text (VTT) model that can easily synchronize audio-visual features. With an extensive experimental exploration, we verify the effectiveness of our video-to-text translation pipeline. Finally, we revisit our recurrent models with this self-attentive approach

The authority of indeterminate law

In this dissertation I identify various sources of legal indeterminacy and scrutinize the functions that indeterminacy can play in law. In particular, I focus on the authority of indeterminate law: how it can be that laws of which it is not clear which obligations they impose, nonetheless impose obligations. I argue that there are more sources of legal indeterminacy than is commonly assumed in the literature, and that the role that context plays in the occurrence, functionality and authority of indeterminate legal norms has been largely overlooked. I argue further that indeterminate legal norms can be authoritatively binding just so long as we accept that the nature of the obligation imposed by the norm changes according to whether the legal norm generates a hard case as applied to a particular context

A Bi-Level Multi-Objective Approach for Web Service Design Defects Detection

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/152453/1/JSS_WSBi_Level__Copy_fv.pd