THE EFFECT OF A PROGRAM OF PORTABLE ELECTRONIC PIANO KEYBOARD EXPERIENCE ON THE ACQUISITION OF SIGHT-SINGING SKILL IN THE NOVICE HIGH SCHOOL CHORUS

The problem was to determine whether a method of aural and visual vocal training that included a program of portable electronic piano keyboard experience would be more effective in teaching sight-singing skills to novice high school chorus students than a method that included only aural and visual vocal training. A sub-problem was to determine whether novice chorus students enjoyed playing electronic keyboards in chorus as a reinforcement experience in sight-singing training. Students were randomly assigned to two treatment groups, tested with the Musical Aptitude Profile, Tonal Imagery, part A, and then trained separately. The experimental group sang repetitions of melodic patterns and utilized techniques associated with the Kodály Method while simultaneously playing keyboard. The comparison group received a similar treatment without using keyboards. The students were pre- and post-tested in sight-singing using the Vocal Sight-Reading Inventory. Results of the Analysis of Covariance using MAP scores as the covariate revealed no significant difference (p<.05) between post-test scores of the two groups. Improvement was noted in 96% of students from pre-test to post-test regardless of grouping. The repeated measures ANOVA revealed a significant relationship (p<.006) between aptitude group and post-test score. High aptitude students in both groups were found to benefit more from the training than low aptitude students. High aptitude keyboard group students achieved an average gain score that was 8.67 points higher than the comparison group. Of the total experimental group, 92% enjoyed playing keyboards in chorus. It is recommended that future research be undertaken to study the use of keyboards with advanced high school choruses and with uncertain singers in the high school chorus. Research is also needed to develop graded, valid, and reliable sight-singing tests for use in high school chorus. Techniques of the Kodály Method should be further investigated for use in high school sight-singing training.

A Visual Analytics Approach to Comparing Cohorts of Event Sequences

Sequences of timestamped events are currently being generated across nearly every domain of data analytics, from e-commerce web logging to electronic health records used by doctors and medical researchers. Every day, this data type is reviewed by humans who apply statistical tests, hoping to learn everything they can about how these processes work, why they break, and how they can be improved upon. To further uncover how these processes work the way they do, researchers often compare two groups, or cohorts, of event sequences to find the differences and similarities between outcomes and processes. With temporal event sequence data, this task is complex because of the variety of ways single events and sequences of events can differ between the two cohorts of records: the structure of the event sequences (e.g., event order, co-occurring events, or frequencies of events), the attributes about the events and records (e.g., gender of a patient), or metrics about the timestamps themselves (e.g., duration of an event). Running statistical tests to cover all these cases and determining which results are significant becomes cumbersome. Current visual analytics tools for comparing groups of event sequences emphasize a purely statistical or purely visual approach for comparison. Visual analytics tools leverage humans' ability to easily see patterns and anomalies that they were not expecting, but is limited by uncertainty in findings. Statistical tools emphasize finding significant differences in the data, but often requires researchers have a concrete question and doesn't facilitate more general exploration of the data. Combining visual analytics tools with statistical methods leverages the benefits of both approaches for quicker and easier insight discovery. Integrating statistics into a visualization tool presents many challenges on the frontend (e.g., displaying the results of many different metrics concisely) and in the backend (e.g., scalability challenges with running various metrics on multi-dimensional data at once). I begin by exploring the problem of comparing cohorts of event sequences and understanding the questions that analysts commonly ask in this task. From there, I demonstrate that combining automated statistics with an interactive user interface amplifies the benefits of both types of tools, thereby enabling analysts to conduct quicker and easier data exploration, hypothesis generation, and insight discovery. The direct contributions of this dissertation are: (1) a taxonomy of metrics for comparing cohorts of temporal event sequences, (2) a statistical framework for exploratory data analysis with a method I refer to as high-volume hypothesis testing (HVHT), (3) a family of visualizations and guidelines for interaction techniques that are useful for understanding and parsing the results, and (4) a user study, five long-term case studies, and five short-term case studies which demonstrate the utility and impact of these methods in various domains: four in the medical domain, one in web log analysis, two in education, and one each in social networks, sports analytics, and security. My dissertation contributes an understanding of how cohorts of temporal event sequences are commonly compared and the difficulties associated with applying and parsing the results of these metrics. It also contributes a set of visualizations, algorithms, and design guidelines for balancing automated statistics with user-driven analysis to guide users to significant, distinguishing features between cohorts. This work opens avenues for future research in comparing two or more groups of temporal event sequences, opening traditional machine learning and data mining techniques to user interaction, and extending the principles found in this dissertation to data types beyond temporal event sequences.

Turkish Security Policymaking on Nuclear Issues: Conceptualizing Advanced Cooperative Security Strategies

Turkey is a non-nuclear member of a nuclear alliance in a region where nuclear proliferation is of particular concern. As the only North Atlantic Treaty Organization (NATO) member that has a border with the Middle East, Turkish officials argue that Turkey cannot solely rely on NATO guarantees in addressing the regional security challenges. However, Turkey has not been able to formulate a security policy that reconciles its quest for independence, its NATO membership, the bilateral relationship with the United States, and regional engagement in the Middle East. This dissertation assesses the strategic implications of Turkey’s perceptions of the U.S./NATO nuclear and conventional deterrence on nuclear issues. It explores three case studies by the process tracing of Turkish policymakers’ nuclear-related decisions on U.S. tactical nuclear weapons deployed in Europe, national air and missile defense, and Iran’s nuclear program. The study finds that the principles of Turkish security policymaking do not incorporate a fundamentally different reasoning on nuclear issues than conventional deterrence. Nuclear weapons and their delivery systems do not have a defining role in Turkish security and defense strategy. The decisions are mainly guided by non-nuclear considerations such as Alliance politics, modernization of the domestic defense industry, and regional influence. The dissertation argues that Turkey could formulate more effective and less risky security policies on nuclear issues by emphasizing the cooperative security approaches within the NATO Alliance over confrontational measures. The findings of this dissertation reveal that a major transformation of Turkish security policymaking is required to end the crisis of confidence with NATO, redefinition of the strategic partnership with the US, and a more cautious approach toward the Middle East. The dissertation argues that Turkey should promote proactive measures to reduce, contain, and counter risks before they develop into real threats, as well as contribute to developing consensual confidence-building measures to reduce uncertainty.