Cultural Competence: An Adaptive Approach to Closing the Achievement and Opportunity Gaps

The current demographic changes in the U.S. have resulted in a national culture gap, which contributes to the achievement and opportunity disparities that persistently plague students of color. According to the U.S. Department of Education, 86% of all PreK-12 grade teachers are European Americans. Yet, the student population in urban settings continues to be overwhelming economically disadvantaged students of color. Plaguing the nation’s schools are concerns about identifying teachers capable of successfully teaching in diverse classrooms, as stakeholders continue to speculate about the efficacy of White teachers to teach students of color due to the White teachers’ lack of understanding about and sensitivity toward students of color. Consequently, educators struggle to effectively serve their culturally dissimilar students. Despite the technical fixes of school reforms over the past three decades, however, the achievement and opportunity gaps remain. Hence, an adaptive approach to closing the opportunity and achievement gaps necessitates challenging our nation’s beliefs, values, and assumptions through a series of professional learning opportunities, as engaging in a series of intensive professional learning within a 12-month period improves student achievement by as much as 21 percentile points. The purpose of this study was to develop the Cultural Competence Professional Learning Module (CCPLM, 2016), which is grounded in Adult Learning Theory, Critical Race Theory, and Culturally Relevant Pedagogy and is designed to foster Cultural Competence in NYC DoE public school teachers. The researcher used the Cultural Competence Needs Assessment Survey (2016) and the Multicultural Personality Questionnaire (2000) to ascertain NYC DoE schools administrators’ level of cultural competence and their beliefs about their teachers’ cultural competence. Of the school administrators who completed the surveys and were culturally competent, 15 provided feedback on the CCPLM’s content and design in a focus group. Concurrently, four national experts on professional learning and diversity in schools also reviewed and provided feedback on the module. The results from the study support the need for a professional learning module that fosters NYC educators’ cultural competence. Delimitations and limitations of the study are discussed

End-to-end Learning for Mining Text and Network Data

A wealth of literature studies user behaviors in online communities, e.g., how users respond to information that are spreading over social networks. One way to study user responses is to analyze user-generated text, by identifying attitude towards target topics. Another way is to analyze the information diffusion networks over involved users. Conventional methods require manual encoding of world knowledge, which is ineffective in many cases. Therefore, to push research forward, we design end-to-end deep learning algorithms that learn high-level representations directly from data and optimize for particular tasks, relieving humans from hard coding features or rules, while achieving better performance. Specifically, I study attitude identification in the text mining domain, and important prediction tasks in the network domain. The key roles of text and networks in understanding user behaviors in online communities are not the only reason that we study them together. Compared with other types of data (e.g., image and speech), text and networks are both discrete and thus may share similar challenges and solutions. Attitude identification is conventionally decomposed into two separate subtasks: target detection that identifies whether a given target is mentioned in the text, and polarity classification that classifies the exact sentiment polarity. However, this decomposition fails to capture interactions between subtasks. To remedy the issue, we developed an end-to-end deep learning architecture, with the two subtasks interleaved by a memory network. Moreover, as the learned representations may share the same semantics for some targets, but vary for others, our model also incorporates the interactions among entities. For information networks, we aim to learn the representation of network structures in order to solve many valuable prediction tasks in the network community. An example of prediction tasks is network growth prediction, which assists decision makers in optimizing strategies. Instead of handcrafting features that could lead to severe loss of structural information, we propose to learn graph representations through a deep end-to-end prediction model. By finding "signatures" for graphs, we convert graphs into matrices, where convolutional neural networks could be applied. In additional to topology, information networks are often associated with different sources of information. We specifically consider the task of cascade prediction, where global context, text content on both nodes, and diffusion graphs play important roles for prediction. Conventional methods require manual specification of the interactions among different information sources, which is easy to miss key information. We present a novel, end-to-end deep learning architecture named DeepCas, which first represents a cascade graph as a set of cascade paths that are sampled through random walks. Such a representation not only allows incorporation of the global context, but also bounds the loss of structural information. After modeling the information of global context, we equip DeepCas with the ability to jointly model text and network in a unified framework. We present a gating mechanism to dynamically fuse the structural and textual representations of nodes based on their respective properties. To incorporate the text information associated with both diffusion items and nodes, attention mechanisms are employed over node text based on their interactions with item text.PHDInformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/140791/1/lichengz_1.pd

Visual Analytic Extraction of Meaning from Photo-Sharing Services for Leisure Pedestrian Routing

Present-day routing services are able to provide travel directions for users of all modes of transport. Most of them are focusing on functional journeys (i.e. journeys linking given origin and destination with minimum cost) and pay less attention to recreational trips, in particular leisure walks in an urban context. These walks have predefined time or distance and as their purpose is the process of walking itself, the attractiveness of chosen paths starts playing an important role in route selection. Conventional map data that are informing routing algorithms cannot be used for extracting street attractiveness as they do not contain a subjective component, or in other words, do not tell whether or not people enjoy their presence at a particular place. Recent research demonstrates that the crowd-sourced data available from the photo- sharing websites have a potential for being a good source of this measure, thus becoming a base for a routing system that suggests attractive leisure walks. This PhD research looks at existing projects, which aim to utilize user-generated photographic data for journey planning, and suggests new techniques that make the estimation of street attractiveness based on this source more reliable. First, we determine the artifacts in photo- graphic datasets that may negatively impact the resulting attractiveness scores. Based on the findings, we suggest filtering methods that improve the compliance of the spatial distributions of photographs with the chosen purpose. Second, we discuss several approaches of assigning attractiveness scores to street segments and make conclusions about their differences. Finally, we experiment with the routing itself and develop a prototype system that suggests leisure walks through attractive streets in an urban area. The experiments we perform cover Central London and involve four photographic sources: Flickr, Geograph, Panoramio and Picasa. A Visual analytic (VA) approach is used throughout the work to glean new insights. Being able to combine computation and the analytical capabilities of the human brain, this research method has proven to work well with complex data structures in a variety of tasks. The thesis contributes to VA as an example of what can be achieved by means of the visual exploration of data

Semantic social network analysis

Mestrado em Engenharia InformáticaOver the last few years, online social networks have become part of society, affecting the way people interact, and share and spread ideas. Their large and still increasing popularity led to the emergence of multiple huge social datasets. Although a vast set of social network analysis methods and algorithms have already been proposed and scrutinized by the research community, most of them aren’t prepared for a direct application over the rich and heterogeneous information contained in the online social network datasets. With the emergence of the Semantic Web, the up until now closed datasets are evolving to become one semantically enriched and distributed social dataset shared by all online social network applications. In such an environment, new methods and tools that fill the gap between the new social and semantic web technologies, and well established and accepted social network analysis methods are required. In that sense, the main goal of this thesis work, as part of the Toursplan project, is to adapt the current Toursplan platform so it fits into the Social and Semantic Web environments, while also providing mechanisms to perform social network analysis over the Toursplan semantically enriched social dataset.Nos últimos anos as redes sociais online têm sido progressivamente adoptadas pela sociedade, influenciando a maneira como as pessoas interagem, partilham e distribuem ideias. A sua crescente popularidade levou ao aparecimento de múltiplas bases de dados com enormes quantidades de dados relativos a interacções sociais. Apesar de já existir uma vasta quantidade de métodos e algoritmos para análise de redes sociais propostos e escrutinados pela comunidade científica, a maior parte não se encontra preparada para a sua aplicação directa sobre a informação, rica e heterogénea, contida nas bases de dados das redes sociais online. Com o aparecimento da Web Semântica, os dados sociais até agora enclausurados e protegidos por cada uma das entidades responsáveis pelos mesmos, estão a convergir para formar uma enorme massa de informação distribuida e semanticamente enriquecida, partilhada por todas as aplicações com funcionalidades sociais. Num ambiente como este, novos métodos e ferramentas são necessários para que exista uma ponte entre as novas tecnologias emergentes devido à Web Social e Semântica, e os já bem aceites e estabelecidos métodos e algoritmos para análise de redes sociais. Sendo assim, o principal objectivo desta tese como parte do projecto Toursplan, é não só a adaptação da actual plataforma Toursplan de forma a que esta se possa encaixar na Web Social e Semântica, mas também o desenvolvimento de mecanismos que permitam analisar a semanticamente enriquecida base de dados Toursplan. Do trabalho desenvolvido resultaram: • A análise do estado da arte das actuais redes sociais online, e de métodos e algoritmos de análise de redes sociais; • A ontologia Toursplan, que descreve o domínio de conhecimento da plataforma Toursplan, incluindo o perfil dos utilizadores, pontos de interesse turísticos e planeamento de viagens; • A ontologia SocioNet, que descreve o domínio de conhecimento relativo a métodos e algoritmos de análise de redes sociais, proporcionando um modelo para a persistência de dados resultantes da execução de múltiplos algoritmos previamente analisados e descritos; • A implementação de uma base de triplos para a plataforma Toursplan, e a migração de toda a informação encontrada na base de dados relacional para a base de triplos; • A implementação de uma camada de acesso a dados, com base na framework Jena para a Web Semântica, que permite o acesso à base de triplos através das ontologias Toursplan e SocioNet; • A implementação de um protótipo que corresponde à nova aplicação Web da rede social Toursplan; • A especificação e implementação de uma fase de normalização de dados provenientes de bases de triplos (ou grafos de triplos) com base em múltiplos padrões de modelação, usando a camada de acesso a dados anterior e a ontologia SocioNet; • A implementação de alguns dos algoritmos de análise de redes sociais previamente analisados com base na camada SocioNet, a sua execução sobre informação normalizada, e a análise dos resultados obtidos

COMET-AR User's Manual: COmputational MEchanics Testbed with Adaptive Refinement

The COMET-AR User's Manual provides a reference manual for the Computational Structural Mechanics Testbed with Adaptive Refinement (COMET-AR), a software system developed jointly by Lockheed Palo Alto Research Laboratory and NASA Langley Research Center under contract NAS1-18444. The COMET-AR system is an extended version of an earlier finite element based structural analysis system called COMET, also developed by Lockheed and NASA. The primary extensions are the adaptive mesh refinement capabilities and a new "object-like" database interface that makes COMET-AR easier to extend further. This User's Manual provides a detailed description of the user interface to COMET-AR from the viewpoint of a structural analyst

Dramatic Interventions: A multi-site case study analysis of student outcomes in the School Drama program

For the last two decades a growing body of research has articulated the transformative potential of learning in, about and through The Arts (for example: Bamford, 2006, 2009; Catterall, Dumais, & Hampden-Thompson, 2012; Deasy, 2002; DICE, 2010; Ewing, 2019, 2010a; Fiske, 1999; Fleming, Gibson, & Anderson, 2016; Winner & Cooper, 2000). In particular, it is clear that there can be a powerful relationship between drama-based pedagogy and the enhancement of student literacies (for example: Baldwin, 2012; Baldwin & Fleming, 2003; Ewing, 2019, 2010a, 2010b; Ewing & Simons, 2016, 2004; Gibson & Ewing, 2011; McNaughton, 1997; Miller & Saxton, 2004, 2009, 2016; Podlozny, 2000). At the same time there has been a need to equip educators with the knowledge, confidence and expertise in the use of drama as critical, quality pedagogy (Ewing, 2002, 2006). This dissertation reports on research that has examined the process and outcomes of one teacher professional learning program, the School Drama program. School Drama is a teacher professional learning program developed through a partnership between Sydney Theatre Company and The University of Sydney’s School of Education and Social Work. The program’s dual aims are to provide primary classroom teachers with the knowledge, understanding, skills and confidence to use drama-based pedagogy with quality children’s literature and to improve student literacy in a designated focus area such as confidence in oracy, creative/imaginative writing, descriptive language or inferential comprehension. Based on a co-mentoring professional learning model (Ewing, 2002, 2006), a teaching artist works alongside a primary classroom teacher to co-plan, co-teach and co-mentor each other during seven weekly in-class workshops over a term using quality children’s literature and process drama-based strategies. The School Drama program has been operating for ten years (from 2009 to 2019) reaching over 30,000 teachers and their students across Australia. While a growing body of research has explored aspects of the program, relatively little focus to date has centred on the student outcomes. This research aimed to investigate the impact of the program on students. An analysis of all data collected in 2017 from a range of participating schools, teachers and students provides a top-level overview of the program’s outcomes. A fine-grained analysis of three case study classrooms in diverse school contexts follows. A range of data was collected from students, the class teacher and the teaching artist/researcher including: student pre- and post-program literacy benchmarking tasks; student pre- and post-program surveys; student focus groups; teacher interviews; and teaching artist/researcher observations and journals. ii While the findings suggest positive shifts in student English and literacy outcomes in the selected focus area (inferential comprehension), particularly in less able male students, perhaps even more importantly there is strong evidence that quality drama-based pedagogy enhances student confidence, collaboration, imagination, engagement and connection to character. A model is proposed to explain how drama-based pedagogy enables more holistic outcomes for students

End user programming of awareness systems : addressing cognitive and social challenges for interaction with aware environments

The thesis is put forward that social intelligence in awareness systems emerges from end-Users themselves through the mechanisms that support them in the development and maintenance of such systems. For this intelligence to emerge three challenges have to be addressed, namely the challenge of appropriate awareness abstractions, the challenge of supportive interactive tools, and the challenge of infrastructure. The thesis argues that in order to advance towards social intelligent awareness systems, we should be able to interpret and predict the success or failure of such systems in relationship to their communicational objectives and their implications for the social interactions they support. The FN-AAR (Focus-Nimbus Aspects Attributes Resources) model is introduced as a formal model which by capturing the general characteristics of the awareness-systems domain allows predictions about socially salient patterns pertaining to human communication and brings clarity to the discussion around relevant concepts such as social translucency, symmetry, and deception. The thesis recognizes that harnessing the benefits of context awareness can be problematic for end-users and other affected individuals, who may not always be able to anticipate, understand or appreciate system function, and who may so feel their own sense of autonomy and privacy threatened. It introduces a set of tools and mechanisms that support end-user control, system intelligibility and accountability. This is achieved by minimizing the cognitive effort needed to handle the increased complexity of such systems and by enhancing the ability of people to configure and maintain intelligent environments. We show how these tools and mechanisms empower end-users to answer questions such as "how does the system behave", "why is something happening", "how would the system behave in response to a change in context", and "how can the system’s behaviour be altered" to achieve intelligibility, accountability, and end-user control. Finally, the thesis argues that awareness applications overall can not be examined as static configurations of services and functions, and that they should be seen as the results of both implicit and explicit interaction with the user. Amelie is introduced as a supportive framework for the development of context-aware applications that encourages the design of the interactive mechanisms through which end-users can control, direct and advance such systems dynamically throughout their deployment. Following the recombinant computing approach, Amelie addresses the implications of infrastructure design decisions on user experience, while by adopting the premises of the FN-AAR model Amelie supports the direct implementation of systems that allow end-users to meet social needs and to practice extant social skills

Asking the Right Questions: Increasing Fairness and Accuracy of Personality Assessments with Computerised Adaptive Testing

Personality assessments are frequently used in real-life applications to predict important outcomes. For such assessments, the forced choice (FC) response format has been shown to reduce response biases and distortions, and computerised adaptive testing (CAT) has been shown to improve measurement efficiency. This research developed FC CAT methodologies under the framework of the Thurstonian item response theory (TIRT) model. It is structured into a logical sequence of three areas of investigation, where the findings from each area inform key decisions in the next one. First, the feasibility of FC CAT is tested empirically. Analysis of large historical samples provides support for item parameter invariance when an item appears in different FC blocks, with person score estimation remaining very stable despite minor violations. Remedies for minimising the risk of assumption violations are also developed. Second, the design of the FC CAT algorithm is optimised. Current CAT methodologies are reviewed and adapted for TIRT-based FC assessments, and intensive simulation studies condense the design options to a small number of practical recommendations. Third, the practicality and usefulness of FC CAT is examined. An adaptive FC assessment measuring the HEXACO model of personality is developed and trialled empirically. In conclusion, this research mapped out a blueprint for developing FC CAT that use the TIRT model, highlighting the benefits, limitations, and key directions for further research