Self-regulation in physical activity: understanding decisions that older adults make

Physical activity and exercise have been shown to strongly contribute to an extended quality of life. Half of all physical declines in aging can be prevented by engaging in adequate levels of daily physical activity. Only one in four older adults over the age of 65 participates in regular physical activity. There are various factors that have been identified in this population that influence physical activity behaviors including gender, ethnicity, education, and socioeconomic status. Though important for providing baseline data on older adults, these descriptions do not fully explain why or why not this population engages in physical activity; or identifies the influences on physical activity adoption and/or maintenance. This study integrated two theoretical perspectives, self-determination theory and stages of change, to examine the motivations toward physical activity and the readiness for behavior change. Also, self-report physical activity scales (PASE) and objective fitness measures were compared. The major purpose of this study was to investigate physical activity behaviors in older adults, with specific focus on decision-making about exercise. Levels of self-regulation from self-determination theory predicted stages of change in older adults. Specifically, external regulation and identified regulation differentiated between inactive individuals, individuals who were initiating activity, and individuals who were maintaining activity. Subscales of the PASE were a better indicator of overall fitness levels. Finally, in order to capture the full experience and meaning of physical activity and exercise, a phenomenological approach across varying levels of activity and readiness was employed. Overall findings showed the importance of correctly measuring physical activity, guiding older adults through the varying levels of motivation by understanding their readiness for change, and ultimately being able to understanding the true meaning of physical activity and exercise as experienced by older adults. Suggestions for practitioners are also addressed

Model Continuation High Schools: social-cognitive promotive factors that contribute to re-engaging at-risk students emotionally, behaviorally, and cognitively towards graduation

Although school dropout rate remains a significant social and economic concern to our nation and has generated considerable research, little attention by scholars has examined the phenomena of re-engagement in effective school context and its developmental influences on at-risk students expectancy for success and task-value towards graduation. Given the multifaceted interactions of school context and the complex developmental needs of at-risk students, there were dual purposes for this three-phase, two-method qualitative study that addressed the literature concerns. The first purpose was to explore and identify policies, programs, and practices perceived as being most effective in re-engaging at-risk students behaviorally, emotionally, and cognitively, at ten Model Continuation High Schools in California. Phases one and two collected data on the Model Continuation High Schools (MCHS) to address this purpose. In phase one, an inductive document review of the ten MCHS applications including four statement letters was conducted and results identified eleven policies, ten programs, and eleven practices that were effective in re-engaging at-risk students behaviorally, emotionally, and cognitively. In phase two, the phenomenological ten-step analysis of semi-structured administrator interviews revealed eight re-engaging implementation strategies perceived to be effective with at-risk students. The second purpose was to build upon Eccles\u27 Expectancy-Value Theoretical Framework by gaining insight on effective school context that supported at-risk students\u27 developmentally appropriate expectancy for success and task-value beliefs towards graduation. Phase three conducted a deductive content analysis of eight theoretical based components on the combine data collected in phases one and two to address this second purpose. Results revealed that principles of Eccles’ Expectancy-Value Model were evident in all identified policies, programs, and practices of the ten MCHS. Model Continuation High Schools are exemplary sites with effective school context that have much to share with other continuation high schools looking for successful re-engaging approaches for at-risk students. The research provided results suggesting that MCHS had significant policies, programs, practices and implementation strategies that transform disengaged at-risk students into graduates by developing students\u27 expectancy for success belief and task-value belief towards graduation. Implications for policy, practice, and future research are discussed

Human-machine communication for educational systems design

This book contains the papers presented at the NATO Advanced Study Institute (ASI) on the Basics of man-machine communication for the design of educational systems, held August 16-26, 1993, in Eindhoven, The Netherland

Interactive models for latent information discovery in satellite images

The recent increase in Earth Observation (EO) missions has resulted in unprecedented volumes of multi-modal data to be processed, understood, used and stored in archives. The advanced capabilities of satellite sensors become useful only when translated into accurate, focused information, ready to be used by decision makers from various fields. Two key problems emerge when trying to bridge the gap between research, science and multi-user platforms: (1) The current systems for data access permit only queries by geographic location, time of acquisition, type of sensor, but this information is often less important than the latent, conceptual content of the scenes; (2) simultaneously, many new applications relying on EO data require the knowledge of complex image processing and computer vision methods for understanding and extracting information from the data. This dissertation designs two important concept modules of a theoretical image information mining (IIM) system for EO: semantic knowledge discovery in large databases and data visualization techniques. These modules allow users to discover and extract relevant conceptual information directly from satellite images and generate an optimum visualization for this information. The first contribution of this dissertation brings a theoretical solution that bridges the gap and discovers the semantic rules between the output of state-of-the-art classification algorithms and the semantic, human-defined, manually-applied terminology of cartographic data. The set of rules explain in latent, linguistic concepts the contents of satellite images and link the low-level machine language to the high-level human understanding. The second contribution of this dissertation is an adaptive visualization methodology used to assist the image analyst in understanding the satellite image through optimum representations and to offer cognitive support in discovering relevant information in the scenes. It is an interactive technique applied to discover the optimum combination of three spectral features of a multi-band satellite image that enhance visualization of learned targets and phenomena of interest. The visual mining module is essential for an IIM system because all EO-based applications involve several steps of visual inspection and the final decision about the information derived from satellite data is always made by a human operator. To ensure maximum correlation between the requirements of the analyst and the possibilities of the computer, the visualization tool models the human visual system and secures that a change in the image space is equivalent to a change in the perception space of the operator. This thesis presents novel concepts and methods that help users access and discover latent information in archives and visualize satellite scenes in an interactive, human-centered and information-driven workflow.Der aktuelle Anstieg an Erdbeobachtungsmissionen hat zu einem Anstieg von multi-modalen Daten geführt die verarbeitet, verstanden, benutzt und in Archiven gespeichert werden müssen. Die erweiterten Fähigkeiten von Satellitensensoren sind nur dann von Entscheidungstraegern nutzbar, wenn sie in genaue, fokussierte Information liefern. Es bestehen zwei Schlüsselprobleme beim Versuch die Lücke zwischen Forschung, Wissenschaft und Multi-User-Systeme zu füllen: (1) Die aktuellen Systeme für Datenzugriffe erlauben nur Anfragen basierend auf geografischer Position, Aufzeichnungszeit, Sensortyp. Aber diese Informationen sind oft weniger wichtig als der latente, konzeptuelle Inhalt der Szenerien. (2) Viele neue Anwendungen von Erdbeobachtungsdaten benötigen Wissen über komplexe Bildverarbeitung und Computer Vision Methoden um Information verstehen und extrahieren zu können. Diese Dissertation zeigt zwei wichtige Konzeptmodule eines theoretischen Image Information Mining (IIM) Systems für Erdbeobachtung auf: Semantische Informationsentdeckung in grossen Datenbanken und Datenvisualisierungstechniken. Diese Module erlauben Benutzern das Entdecken und Extrahieren relevanter konzeptioneller Informationen direkt aus Satellitendaten und die Erzeugung von optimalen Visualisierungen dieser Informationen. Der erste Beitrag dieser Dissertation bringt eine theretische Lösung welche diese Lücke überbrückt und entdeckt semantische Regeln zwischen dem Output von state-of-the-art Klassifikationsalgorithmen und semantischer, menschlich definierter, manuell angewendete Terminologie von kartographischen Daten. Ein Satz von Regeln erkläret in latenten, linguistischen Konzepten den Inhalte von Satellitenbildern und verbinden die low-level Maschinensprache mit high-level menschlichen Verstehen. Der zweite Beitrag dieser Dissertation ist eine adaptive Visualisierungsmethode die einem Bildanalysten im Verstehen der Satellitenbilder durch optimale Repräsentation hilft und die kognitive Unterstützung beim Entdecken von relevenanter Informationen in Szenerien bietet. Die Methode ist ein interaktive Technik die angewendet wird um eine optimale Kombination von von drei Spektralfeatures eines Multiband-Satellitenbildes welche die Visualisierung von gelernten Zielen and Phänomenen ermöglichen. Das visuelle Mining-Modul ist essentiell für IIM Systeme da alle erdbeobachtungsbasierte Anwendungen mehrere Schritte von visueller Inspektion benötigen und davon abgeleitete Informationen immer vom Operator selbst gemacht werden müssen. Um eine maximale Korrelation von Anforderungen des Analysten und den Möglichkeiten von Computern sicher zu stellen, modelliert das Visualisierungsmodul das menschliche Wahrnehmungssystem und stellt weiters sicher, dass eine Änderung im Bildraum äquivalent zu einer Änderung der Wahrnehmung durch den Operator ist. Diese These präsentieret neuartige Konzepte und Methoden, die Anwendern helfen latente Informationen in Archiven zu finden und visualisiert Satellitenszenen in einem interaktiven, menschlich zentrierten und informationsgetriebenen Arbeitsprozess

Pinching sweaters on your phone – iShoogle : multi-gesture touchscreen fabric simulator using natural on-fabric gestures to communicate textile qualities

The inability to touch fabrics online frustrates consumers, who are used to evaluating physical textiles by engaging in complex, natural gestural interactions. When customers interact with physical fabrics, they combine cross-modal information about the fabric's look, sound and handle to build an impression of its physical qualities. But whenever an interaction with a fabric is limited (i.e. when watching clothes online) there is a perceptual gap between the fabric qualities perceived digitally and the actual fabric qualities that a person would perceive when interacting with the physical fabric. The goal of this thesis was to create a fabric simulator that minimized this perceptual gap, enabling accurate perception of the qualities of fabrics presented digitally. We designed iShoogle, a multi-gesture touch-screen sound-enabled fabric simulator that aimed to create an accurate representation of fabric qualities without the need for touching the physical fabric swatch. iShoogle uses on-screen gestures (inspired by natural on-fabric movements e.g. Crunching) to control pre-recorded videos and audio of fabrics being deformed (e.g. being Crunched). iShoogle creates an illusion of direct video manipulation and also direct manipulation of the displayed fabric. This thesis describes the results of nine studies leading towards the development and evaluation of iShoogle. In the first three studies, we combined expert and non-expert textile-descriptive words and grouped them into eight dimensions labelled with terms Crisp, Hard, Soft, Textured, Flexible, Furry, Rough and Smooth. These terms were used to rate fabric qualities throughout the thesis. We observed natural on-fabric gestures during a fabric handling study (Study 4) and used the results to design iShoogle's on-screen gestures. In Study 5 we examined iShoogle's performance and speed in a fabric handling task and in Study 6 we investigated users' preferences for sound playback interactivity. iShoogle's accuracy was then evaluated in the last three studies by comparing participants’ ratings of textile qualities when using iShoogle with ratings produced when handling physical swatches. We also described the recording and processing techniques for the video and audio content that iShoogle used. Finally, we described the iShoogle iPhone app that was released to the general public. Our evaluation studies showed that iShoogle significantly improved the accuracy of fabric perception in at least some cases. Further research could investigate which fabric qualities and which fabrics are particularly suited to be represented with iShoogle