Research on the effects of school desegregation has failed to produce conclusive findings. An over emphasis on the outcomes of school desegregation, usually assessed through the use of standardized test scores, has created a situation in which there exists a paucity of studies of the day to day process of school desegregation: instructional practices, student interaction, and teacher behavior in the classroom. More research on the process of school desegregation is needed if its results or outcomes are to become more interpretable.

Indiana University-Purdue University Indianapolis (IUPUI) / According to the US Centers for Disease Control and Prevention, less than half of all adults in the US meet basic physical activity guidelines. Physical activity can help not just improve physical and mental health but also reduce the risk of heart disease and some cancers. Researchers and companies have tried to investigate the use of modern technologies to motivate people to increase and maintain physical activities. However, in spite of these efforts, there are criticisms. Those include low dietary effectiveness of the tools, lack of sustainable effects in the long-term, and proof of effectiveness only shown in laboratory settings. To overcome these limitations, first, the author developed a framework of overarching motivation theories and HCI factors and contextualized it within the running domain. Second, the author has developed a mobile application called HamkeRun within this framework, using the concepts of information visualization, gamification, and social grouping to increase a user’s motivation to run more frequently. Third, the HamkeRun application was empirically tested through a two-month-long longitudinal experiment and follow-up interviews. The results showed that the single runner type showed significant increases in the levels of their external motivation (motivational effect of the HamkeRun application), internal motivation and satisfaction, while the team runner type showed significant increases only in internal motivation. In addition, motivational effects were also different depending on the runners’ behavior change stage. Runners at the maintenance stage showed significant increases in external motivation, internal motivation, satisfaction, and total number of running activities performed during the study. Although action stage runners showed significant increase in internal motivation, female runners at the action stage showed significant decrease in their external motivation. Gamification greatly influenced increases of external motivation, internal motivation and total number of actual activities. Although both male and female runners showed increased internal motivation, significant increase in external motivation was only found in male runners. The dissertation closes with a series of design guidelines for application developers and designers which may help develop motivational tools in other health-related domains.

In this dissertation, new digital image processing methods for hyperdimensional imagery are developed and experimentally tested on remotely sensed Earth observations and medical imagery. The high dimensionality of the imagery is either inherent due to the type of measurements forming the image, as with imagery obtained with hyperspectral sensors, or the result of preprocessing and feature extraction, as with synthetic aperture radar imagery and digital mammography. In the first study, two omni-directional adaptations of gray level co-occurrence matrix analysis are developed and experimentally evaluated. The adaptations are based on a previously developed rubber band straightening transform that has been used for analysis of segmented masses in digital mammograms. The new methods are beneficial because they can be applied to imagery where the region of interest is either poorly segmented or not segmented. The methods are based on the concept of extracting circular windows s around each pixel in the image which are radially resampled to derive rectangular images. The images derived from the resampling are then suitable for standard GLCM techniques. The methods were applied to both remotely sensed synthetic aperture radar imagery, for the purpose of automated detection of landslides on earthen levees, and to digital mammograms, for the purpose of automated classification of masses as either benign or malignant. Experimental results show the newly developed methods to be valuable for texture feature extraction and classification of un-segmented objects. In the second study, a new technique of using spatial information in spectral band grouping for remotely sensed hyperspectral imagery is developed and experimentally tested. The technique involves clustering the spectral bands based on similarity of spatial features extracted from each band. The newly developed technique is evaluated in automated classification systems that utilize a single classifier and systems that utilize multiple classifiers combined with decision fusion. The systems are experimentally tested on remotely sensed imagery for agricultural applications. The spatial-spectral band grouping approach is compared to uniform band windowing and spectral only band grouping. The results show that the spatial-spectral band grouping method significantly outperforms both of the comparison methods, particularly when using multiple classifiers with decision fusion.

In this thesis we present a technique to group search-engine returned citations for person-name queries, such that the search-engine returned citations in each group belong to the same person. To group the returned citations we use a multi-faceted approach that considers evidence from three facets: (1) attributes, (2) links, and (3) page similarity. For each facet we generate a confidence matrix. Then we construct a final confidence matrix for all facets. Using a threshold, we apply a grouping algorithm on the final confidence matrix. The output is a group of search-engine returned citations, such that the citations in each group relate to the same person.

The purpose of the present study is to determine the effectiveness of sociometric groupings in bringing about improved social status of rejected girls in eighth-grade homemaking classes. Specifically, the study seeks to answer to the questions: Do significant changes occur in personal and social adjustment when pupils are placed in groups according to their choice? Is there evidence of improved social status of rejected pupils when sociometric groupings are used throughout the year?

The visual processing of a stimulus is facilitated by attention when it is at an attended location compared to an unattended location. However, whether attentional selection operates on the basis of visual features (e.g., color) independently of spatial locations is less clear. Six experiments were designed to examine how color information as well as location information affected attentional selection. In Experiment 1, the color of the targets and the spatial distance between them were both manipulated. Stimuli were found to be grouped based on color similarity. Additionally, the evidence suggested direct selection on the basis of color groups, rather than selection that was mediated by location. By varying the probabilities of target location and color, Experiments 2, 3 and 4 demonstrated that the use of color in perceptual grouping and in biasing the priority of selection is not automatic, but is modulated by task demands. Experiments 5 and 6 further investigated the relationship between using color and using location as the selection basis under exogenous and endogenous orienting. The results suggest that the precise nature of the interaction between color and location varies according to the mode of attentional control. Collectively, these experiments contribute to an understanding of how different types of information are used in selection and suggest a greater degree of flexibility of attentional control than previously expected. The flexibility is likely to be determined by a number of factors, including task demands and the nature of attentional control.

Research on visual working memory (VWM) suggests a capacity limit of three to four objects (Luck & Vogel, 1997), but recent studies on the fidelity of VWM capacity for objects indicates that informational bandwidth, which can vary with factors like complexity and amenability to perceptual grouping, can interact with this capacity (Brady, Konkle & Alvarez, 2011). For example, individual features can be grouped into objects for an added benefit in VWM capacity (Xu, 2002). Along these lines, the Gestalt principles of proximity and connectedness have been shown to benefit VWM, although they do not influence capacity equally (Xu 2006; Woodman, Vecera & Luck, 2003). Closure, which has not been investigated for its influence in VWM capacity, is similar to connectedness and proximity as it promotes the perception of a coherent object without physical connections. In the current experiment, we evaluated whether closure produces similar or greater VWM capacity advantages compared to proximity by having participants engage in a change detection task. Four L-shaped features were grouped in tilted clusters to either form an object (closure condition) or not (no-object condition), with a set size of two (8 L features), four (16 L features), or six clusters (24 L features). Following a brief mask (1000 ms), the orientation of one cluster was changed (tilted 25 or -25 degrees) on half the trials. Our results indicate that there was no difference in accuracy or reaction time for the perceptual grouping conditions of closure/no-object, although we did find a main effect for set size and change conditions. Overall, it seems that grouping by closure provides no further advantages to VWM capacity than proximity; however, more experiments need to be conducted to solidify the findings of the current experiment.

Wearable tech has become increasingly popular with elite level sports organizations. The limiting factor to the value of the wearables is the use cases for the data they provide. This study introduces a technique to be used in tandem with this data to better inform training decisions. K-means clustering was used to group athletes from two seasons worth of data from an NCAA Division 1 American Football team. This data provided average game demands of each student-athlete, which was then used to create training groups. The resultant groupings showed results that were similar to traditional groupings used for training in American football, thus validating the results, while also offering insights on individuals that may need to consider training in a non-traditional group. In conclusion, this technique can be brought to athletic training and be useful in any organization that is dealing with training multitudes of athletes.

To overcome the dimensionality curse of hyperspectral data, an investigation has been done on the use of grouping spectral bands, followed by feature level fusion and classifier decision fusion, to develop an automated target recognition (ATR) system for data reduction and enhanced classification. The entire span of spectral bands in the hyperspectral data is subdivided into groups based on performance metrics. Feature extraction is done using supervised methods as well as unsupervised methods. The effects of classification of the lower dimension data by parametric, as well as non-parametric, classifiers are studied. Further, multiclassifiers and decision level fusion using Qualified Majority Voting is applied to the features extracted from each group. The effectiveness of the ATR system is tested using the hyperspectral signatures of a target class, Cogongrass (Imperata Cylindrica), and a non-target class, Johnsongrass (Sorghum halepense). A comparison of target detection accuracies by before and after decision fusion illustrates the effect of the influence of each group on the final decision and the benefits of using decision fusion with multiclassifiers. Hence, the ATR system designed can be used to detect a target class while significantly reducing the dimensionality of the data.

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