Analysis of Housing Partnerships using the Balanced Scorecard framework

Housing Partnerships (HPs) are collaborative arrangements that assist communities in the delivery of affordable housing by combining the strengths of the public and private sectors. They emerged in several states, counties, and cities in the eighties as innovative solutions to the challenges in affordable housing resulting from changing dynamics of delivery and production. ^ My study examines HPs with particular emphasis upon the identification of those factors associated with the successful performance of their mission of affordable housing. I will use the Balanced Scorecard (BSC) framework in this study. The identification of performance factors facilitates a better understanding of how HPs can be successful in achieving their mission. The identification of performance factors is significant in the context of the current economic environment because HPs can be viewed as innovative institutional mechanisms in the provision of affordable housing. ^ The present study uses a mixed methods research approach, drawing on data from the IRS Form 990 tax returns, a survey of the chief executives of HPs, and other secondary sources. The data analysis is framed according to the four perspectives of BSC: the financial, customer, internal business, and learning and growth. Financially, revenue diversification affects the financial health of HPs and overall performance. Although HPs depend on private and government funding, they also depend on service fees to carry out their mission. From a customer perspective, the HPs mainly serve low and moderate income households, although some serve specific groups such as seniors, homeless, veterans, and victims of domestic violence. From an internal business perspective, HPs’ programs are oriented toward affordable housing needs, undertaking not only traditional activities such as construction, loan provision, etc., but also advocacy and educational programs. From an employee and learning growth perspective, the HPs are small in staff size, but undertake a range of activities with the help of volunteers. Every part of the HP is developed to maximize resources, knowledge, and skills in order to assist communities in the delivery of affordable housing and related needs. Overall, housing partnerships have played a key role in affordable housing despite the housing market downturn since 2006. Their expenses on affordable housing activities increased despite the decrease in their revenues.^

Third Space Theory: Reconceptualizing Content Literacy Learning

This paper develops, through a literature review, a conceptual framework for a study in process of the literacy views and practices of youth offenders. The framework offers a reconceptualized view of literacy to increase opportunities for content literacy learning with marginalized youth.

Self -regulated learning: The role of a journal in the learning process for students and teachers

A fundamental goal of education is to equip students with self-regulatory capabilities that enable them to educate themselves. Self directedness not only contributes to success in formal instruction but also promotes lifelong learning (Bandura, 1997). The area of research on self-regulated learning is well grounded within the framework of psychological literature attributed to motivation, metacognition, strategy use and learning. This study explored past research and established the purpose of teaching students to self-regulate their learning and highlighted the fact that teachers are expected to assume a major role in the learning process. A student reflective writing journal activity was sustained for a period of two semesters in two fourth-grade mathematics classrooms. The reflective writing journal was analyzed in search of identifying strategies reported by students. Research questions were analyzed using descriptive statistics, frequency counts, cross-tabs and chi-square analyses. ^ Results based on student-use of the journals and teacher interviews indicated that the use of a reflective writing journal does promote self-regulated learning strategies to the extent which the student is engaged in the journaling process. Those students identified as highly self-regulated learners on the basis of their strategy use, were shown to consistently claim to learn math “as well or better than planned” on a weekly basis. Furthermore, good self-regulators were able to recognize specific strategies that helped them do well and change their strategies across time based on the planned learning objectives. The perspectives of the participating teachers were examined in order to establish the context in which the students were working. The effect of “planned change” and/or the resistance to change as established in previous research, from the teachers point of view, was also explored. The analysis of the journal data did establish a significant difference between students who utilized homework as a strategy. ^ Based on the journals and interviews, this study finds that the systematic use of metacognitive, motivational and/or learning strategies can have a positive effect on student's responsiveness to their learning environment. Furthermore, it reflects that teaching students “how to learn” can be a vital part of the effectiveness of any curriculum. ^

Effects of real -time captioning and sign language interpreting on the learning of college students who are deaf or hard of hearing

College personnel are required to provide accommodations for students who are deaf and hard of hearing (D/HoH), but few empirical studies have been conducted on D/HoH students as they learn under the various accommodation conditions (sign language interpreting, SLI, real-time captioning, RTC, and both). Guided by the experiences of students who are D/HoH at Miami-Dade College (MDC) who requested RTC in addition to SLI as accommodations, the researcher adopted Merten’s transformative-emancipatory theoretical framework that values perceptions and voice of students who are D/HoH. A mixed methods design addressed two research questions: Did student learning differ for each accommodation? What did students experience while learning through accommodations? Participants included 30 students who were D/HoH (60% women). They represented MDC’s majority minority population: 10% White (non-Hispanic), 20% Black (non-Hispanic, including Haitian/Caribbean), 67% Hispanic, and 3% other. Hearing loss, ranged from severe-profound (70%) to mild-moderate (30%). All were able to communicate with American Sign Language: Learning was measured while students who were D/HoH viewed three lectures under three accommodation conditions (SLI, RTC, SLI+RTC). The learning measure was defined as the difference in pre- and post-test scores on tests of the content presented in the lectures. Using repeated measure ANOVA and ANCOVA, confounding variables of fluency in American Sign Language and literacy skills were treated as covariates. Perceptions were obtained through interviews and verbal protocol analysis that were signed, videotaped, transcribed, coded, and examined for common themes and metacognitive strategies. No statistically significant differences were found among the three accommodations on the learning measure. Students who were D/HoH expressed thoughts about five different aspects of their learning while they viewed lectures: (a) comprehending the information, (b) feeling a part of the classroom environment, (c) past experiences with an accommodation, (d) individual preferences for an accommodation, (e) suggestions for improving an accommodation. They exhibited three metacognitive strategies: (a) constructing knowledge, (b) monitoring comprehension, and (c) evaluating information. No patterns were found in the types of metacognitive strategies used for any particular accommodation. The researcher offers recommendations for flexible applications of the standard accommodations used with students who are D/HoH.

Students' recollections of participating in a first year residential learning community experience

The purpose of this qualitative study was to gain an understanding of what participation in a first year residential learning community meant to students 2-3 years after their involvement in the program. Various theories including environmental, student involvement, psychosocial and intellectual, were used as a framework for this case study. Each of the ten participants was a junior or senior level student at the time of the study, but had previously participated in a first year residential learning community at Florida International University. The researcher held two semi-structured interviews with each participant, and collected data sheets from each. The narrative data produced from the interviews were transcribed, coded and analyzed to gain insights into the experiences and perspectives of the participants. Member checking was used after the interview process. A peer reviewer offered feedback during the data analysis. The resulting data was coded into categories, with a final selection of four themes and 15 sub-themes, which captured the essence of the participants' experiences. The four major themes included: (a) community, (b) involvement, (c) identity, and (d) academics. The community theme is used to describe how students perceived the environment to be. The involvement theme is used to describe the students' participation in campus life and their interaction with other members of the university community. The identity theme is used to describe the students' process of development, and the personal growth they underwent as a result of their experiences. The academics theme refers to the intellectual development of students and their interaction around academic issues. The results of this study showed that the participants valued greatly their involvement in the First Year Residents Succeeding Together program (FYRST) and can articulate how it helped them succeed as students. In describing their experience, they most recall the sense of community that existed, the personal growth they experienced, the academic development process they went through, and their involvement, both with other people and with activities in their community. Recommendations are provided for practice and research, including several related to enhancing the academic culture, integrating faculty, utilizing peer influence and providing further opportunities to create a seamless learning environment.

Um Framework para criação de jogos voltados para o ensino de lógica de programação

Digital games have been used as aiding tool for transmission of knowledge, allowing faster dissemination of content. Using this strategy of disseminating logical reasoning development for basic school children can be the motivating gear that helps in the learning process for any area. In this context, many games can be created and provided for the use of teacher and student. However, the complexity of construction of these games becomes a obstacle which can, often, prevent their construction. Thus, this paper presents a framework for creating games, which teach programming logic, presenting from their conception to their integration with the visual programming environment (Blockly) and scenarios created in HTML5.

Learning leadership: a resource for aspiring nurse leaders

Background/purpose – Nurse leaders play a key role in the growth of the nursing profession; hence the development of future leaders is essential. Despite its importance, opportunities for leadership development can be limited. The purpose of the practicum project was to develop a comprehensive, yet concise resource to assist aspiring nurse leaders in their journey towards effective leadership. Methods – The methods used to achieve the practicum objectives were: (a) explore the literature and complete a comprehensive review (b) conduct expert consultations with current nurse leaders, and (c) develop a learning resource for aspiring nurse leaders. Results – The literature review and expert consultations highlighted the importance of effectively developing aspiring nurse leaders. The information obtained allowed for the development of a comprehensive learning resource. A detailed background, leadership theory, leadership framework, leader competencies and learning activities are presented throughout the resource. Conclusion – In order to for the nursing profession to continue to advance, effective nurse leaders are of paramount importance. However, it is essential that aspiring leaders are given appropriate opportunities for development. The learning resource was developed to provide aspiring leaders with a comprehensive tool to enhance their leadership development.

Riconoscimento real-time di gesture tramite tecniche di machine learning

Il riconoscimento delle gesture è un tema di ricerca che sta acquisendo sempre più popolarità, specialmente negli ultimi anni, grazie ai progressi tecnologici dei dispositivi embedded e dei sensori. Lo scopo di questa tesi è quello di utilizzare alcune tecniche di machine learning per realizzare un sistema in grado di riconoscere e classificare in tempo reale i gesti delle mani, a partire dai segnali mioelettrici (EMG) prodotti dai muscoli. Inoltre, per consentire il riconoscimento di movimenti spaziali complessi, verranno elaborati anche segnali di tipo inerziale, provenienti da una Inertial Measurement Unit (IMU) provvista di accelerometro, giroscopio e magnetometro. La prima parte della tesi, oltre ad offrire una panoramica sui dispositivi wearable e sui sensori, si occuperà di analizzare alcune tecniche per la classificazione di sequenze temporali, evidenziandone vantaggi e svantaggi. In particolare, verranno considerati approcci basati su Dynamic Time Warping (DTW), Hidden Markov Models (HMM), e reti neurali ricorrenti (RNN) di tipo Long Short-Term Memory (LSTM), che rappresentano una delle ultime evoluzioni nel campo del deep learning. La seconda parte, invece, riguarderà il progetto vero e proprio. Verrà impiegato il dispositivo wearable Myo di Thalmic Labs come caso di studio, e saranno applicate nel dettaglio le tecniche basate su DTW e HMM per progettare e realizzare un framework in grado di eseguire il riconoscimento real-time di gesture. Il capitolo finale mostrerà i risultati ottenuti (fornendo anche un confronto tra le tecniche analizzate), sia per la classificazione di gesture isolate che per il riconoscimento in tempo reale.

A deep learning-based approach for 3D people tracking

Questa tesi si occupa dell’estensione di un framework software finalizzato all'individuazione e al tracciamento di persone in una scena ripresa da telecamera stereoscopica. In primo luogo è rimossa la necessità di una calibrazione manuale offline del sistema sfruttando algoritmi che consentono di individuare, a partire da un fotogramma acquisito dalla camera, il piano su cui i soggetti tracciati si muovono. Inoltre, è introdotto un modulo software basato su deep learning con lo scopo di migliorare la precisione del tracciamento. Questo componente, che è in grado di individuare le teste presenti in un fotogramma, consente ridurre i dati analizzati al solo intorno della posizione effettiva di una persona, escludendo oggetti che l’algoritmo di tracciamento sarebbe portato a individuare come persone.

Development of a work-based learning MSc course which incorporates the development and demonstration of professional engineering competence standards

UK engineering standards are regulated by the Engineering Council (EC) using a set of generic threshold competence standards which all professionally registered Chartered Engineers in the UK must demonstrate, underpinned by a separate academic qualification at Masters Level. As part of an EC-led national project for the development of work-based learning (WBL) courses leading to Chartered Engineer registration, Aston University has started an MSc Professional Engineering programme, a development of a model originally designed by Kingston University, and build around a set of generic modules which map onto the competence standards. The learning pedagogy of these modules conforms to a widely recognised experiential learning model, with refinements incorporated from a number of other learning models. In particular, the use of workplace mentoring to support the development of critical reflection and to overcome barriers to learning is being incorporated into the learning space. This discussion paper explains the work that was done in collaboration with the EC and a number of Professional Engineering Institutions, to design a course structure and curricular framework that optimises the engineering learning process for engineers already working across a wide range of industries, and to address issues of engineering sustainability. It also explains the thinking behind the work that has been started to provide an international version of the course, built around a set of globalised engineering competences. © 2010 W J Glew, E F Elsworth.

Engineering the future! Innovation in learning & teaching:an empirical approach

Learning and teaching approaches to engineering are generally perceived to be difficult and academically challenging. Such challenges are reflected in high levels of student attrition and failure. In addressing this issue, a unique approach to engineering education has been developed by one of the paper authors. This approach, which is suitable for undergraduate and postgraduate levels, brings together pedagogic and engineering epistemologies in an empirically grounded framework. It is underpinned by three distinctive concepts: Relationships, Variety & Alignment. Based upon research, the R + V + A approach to engineering education provides a learning and teaching strategy which in enhancing the student experience increases retention and positively impacts student success. In discussing the emergent findings of a study into the pedagogical value of the approach the paper makes a significant contribution to academic theory and practice in this area.

Learning from Geometry

Subspaces and manifolds are two powerful models for high dimensional signals. Subspaces model linear correlation and are a good fit to signals generated by physical systems, such as frontal images of human faces and multiple sources impinging at an antenna array. Manifolds model sources that are not linearly correlated, but where signals are determined by a small number of parameters. Examples are images of human faces under different poses or expressions, and handwritten digits with varying styles. However, there will always be some degree of model mismatch between the subspace or manifold model and the true statistics of the source. This dissertation exploits subspace and manifold models as prior information in various signal processing and machine learning tasks.

A near-low-rank Gaussian mixture model measures proximity to a union of linear or affine subspaces. This simple model can effectively capture the signal distribution when each class is near a subspace. This dissertation studies how the pairwise geometry between these subspaces affects classification performance. When model mismatch is vanishingly small, the probability of misclassification is determined by the product of the sines of the principal angles between subspaces. When the model mismatch is more significant, the probability of misclassification is determined by the sum of the squares of the sines of the principal angles. Reliability of classification is derived in terms of the distribution of signal energy across principal vectors. Larger principal angles lead to smaller classification error, motivating a linear transform that optimizes principal angles. This linear transformation, termed TRAIT, also preserves some specific features in each class, being complementary to a recently developed Low Rank Transform (LRT). Moreover, when the model mismatch is more significant, TRAIT shows superior performance compared to LRT.

The manifold model enforces a constraint on the freedom of data variation. Learning features that are robust to data variation is very important, especially when the size of the training set is small. A learning machine with large numbers of parameters, e.g., deep neural network, can well describe a very complicated data distribution. However, it is also more likely to be sensitive to small perturbations of the data, and to suffer from suffer from degraded performance when generalizing to unseen (test) data.

From the perspective of complexity of function classes, such a learning machine has a huge capacity (complexity), which tends to overfit. The manifold model provides us with a way of regularizing the learning machine, so as to reduce the generalization error, therefore mitigate overfiting. Two different overfiting-preventing approaches are proposed, one from the perspective of data variation, the other from capacity/complexity control. In the first approach, the learning machine is encouraged to make decisions that vary smoothly for data points in local neighborhoods on the manifold. In the second approach, a graph adjacency matrix is derived for the manifold, and the learned features are encouraged to be aligned with the principal components of this adjacency matrix. Experimental results on benchmark datasets are demonstrated, showing an obvious advantage of the proposed approaches when the training set is small.

Stochastic optimization makes it possible to track a slowly varying subspace underlying streaming data. By approximating local neighborhoods using affine subspaces, a slowly varying manifold can be efficiently tracked as well, even with corrupted and noisy data. The more the local neighborhoods, the better the approximation, but the higher the computational complexity. A multiscale approximation scheme is proposed, where the local approximating subspaces are organized in a tree structure. Splitting and merging of the tree nodes then allows efficient control of the number of neighbourhoods. Deviation (of each datum) from the learned model is estimated, yielding a series of statistics for anomaly detection. This framework extends the classical {\em changepoint detection} technique, which only works for one dimensional signals. Simulations and experiments highlight the robustness and efficacy of the proposed approach in detecting an abrupt change in an otherwise slowly varying low-dimensional manifold.

Learning and Exploiting Camera Geometry for Computer Vision

This work explores the use of statistical methods in describing and estimating camera poses, as well as the information feedback loop between camera pose and object detection. Surging development in robotics and computer vision has pushed the need for algorithms that infer, understand, and utilize information about the position and orientation of the sensor platforms when observing and/or interacting with their environment.

The first contribution of this thesis is the development of a set of statistical tools for representing and estimating the uncertainty in object poses. A distribution for representing the joint uncertainty over multiple object positions and orientations is described, called the mirrored normal-Bingham distribution. This distribution generalizes both the normal distribution in Euclidean space, and the Bingham distribution on the unit hypersphere. It is shown to inherit many of the convenient properties of these special cases: it is the maximum-entropy distribution with fixed second moment, and there is a generalized Laplace approximation whose result is the mirrored normal-Bingham distribution. This distribution and approximation method are demonstrated by deriving the analytical approximation to the wrapped-normal distribution. Further, it is shown how these tools can be used to represent the uncertainty in the result of a bundle adjustment problem.

Another application of these methods is illustrated as part of a novel camera pose estimation algorithm based on object detections. The autocalibration task is formulated as a bundle adjustment problem using prior distributions over the 3D points to enforce the objects' structure and their relationship with the scene geometry. This framework is very flexible and enables the use of off-the-shelf computational tools to solve specialized autocalibration problems. Its performance is evaluated using a pedestrian detector to provide head and foot location observations, and it proves much faster and potentially more accurate than existing methods.

Finally, the information feedback loop between object detection and camera pose estimation is closed by utilizing camera pose information to improve object detection in scenarios with significant perspective warping. Methods are presented that allow the inverse perspective mapping traditionally applied to images to be applied instead to features computed from those images. For the special case of HOG-like features, which are used by many modern object detection systems, these methods are shown to provide substantial performance benefits over unadapted detectors while achieving real-time frame rates, orders of magnitude faster than comparable image warping methods.

The statistical tools and algorithms presented here are especially promising for mobile cameras, providing the ability to autocalibrate and adapt to the camera pose in real time. In addition, these methods have wide-ranging potential applications in diverse areas of computer vision, robotics, and imaging.

Learning to 'understand backwards' in time: children's temporal cognition and the primary history curriculum

This study examines children’s temporal ways of knowing and it highlights the centrality of temporal cognition in the development of children’s historical understanding. It explores how young children conceptualise time and it examines the provision for temporal cognition at the levels of the intended, enacted and received history curriculum in the Irish primary school context. Positioning temporality as a prerequisite second-order concept, the study recognises the essential role of both first-order and additional second-order concepts in historical understanding. While the former can be defined as the basic, substantive content to be taught, the latter refers to a number of additional key concepts that are deemed fundamental to children's capacity to make meaningful sense of history. The study argues for due recognition to be given to temporality, in the belief that both sets of knowledge, the content and skills, are required to develop historical thinking (Lévesque, 2011). The study addresses a number of key research questions, using a mixed methods research design, comprising an analysis of history textbooks, a survey among final year student teachers about their teaching of history, and school-based interviews with primary school children: What opportunities are available for children to develop temporal ways of knowing? How do student teachers experience being apprenticed into the available culture for teaching history and understanding temporality at primary level? What insights do the cognitive-developmental and sociocultural perspectives on learning provide for understanding the dynamics of children’s temporal ways of knowing? The study argues that the skill of developing a deeper understanding of time is a key prerequisite in connecting with, and constructing, understandings and frameworks of the past. The study advances a view of temporality as complex, multi-faceted and developmental. The findings have a potential contribution to make in influencing policy and pedagogy in establishing an elaborated and well-defined curriculum framework for developing temporal cognition at both national and international levels.

A Framework to Support the Sharing and Reuse of Computable Phenotype Definitions Across Health Care Delivery and Clinical Research Applications.

INTRODUCTION: The ability to reproducibly identify clinically equivalent patient populations is critical to the vision of learning health care systems that implement and evaluate evidence-based treatments. The use of common or semantically equivalent phenotype definitions across research and health care use cases will support this aim. Currently, there is no single consolidated repository for computable phenotype definitions, making it difficult to find all definitions that already exist, and also hindering the sharing of definitions between user groups. METHOD: Drawing from our experience in an academic medical center that supports a number of multisite research projects and quality improvement studies, we articulate a framework that will support the sharing of phenotype definitions across research and health care use cases, and highlight gaps and areas that need attention and collaborative solutions. FRAMEWORK: An infrastructure for re-using computable phenotype definitions and sharing experience across health care delivery and clinical research applications includes: access to a collection of existing phenotype definitions, information to evaluate their appropriateness for particular applications, a knowledge base of implementation guidance, supporting tools that are user-friendly and intuitive, and a willingness to use them. NEXT STEPS: We encourage prospective researchers and health administrators to re-use existing EHR-based condition definitions where appropriate and share their results with others to support a national culture of learning health care. There are a number of federally funded resources to support these activities, and research sponsors should encourage their use.