School-Based Referrals to the Juvenile Courts: Prevalence and Characteristics in Several States

Effective school discipline practices are essential to keeping schools safe and creating an optimal learning environment. However, the overreliance of exclusionary discipline often removes students from the school setting and deprives them of the opportunity to learn. Previous research has suggested that students are being introduced to the juvenile justice system through the use of school-based juvenile court referrals. In 2011, approximately 1.2 million delinquency cases were referred to the juvenile courts in the United States. Preliminary evidence suggests that an increasing number of these referrals have originated in the schools. This study investigated school-based referrals to the juvenile courts as an element of the School-to-Prison Pipeline (StPP). The likelihood of school-based juvenile court referrals and rate of dismissal of these referrals was examined in several states using data from the National Juvenile Court Data Archives. In addition, the study examined race and special education status as predictors of school-based juvenile court referrals. Descriptive statistics, logistic regression and odds ratio, were used to analyze the data, make conclusions based on the findings and recommend appropriate school discipline practices.

An exploration of parent ivolvement in Response to Intervention (RTI) in Title I schools

Research demonstrates that parental involvement positively impacts student achievement and enhances targeted instruction. Notably, however, little research currently exists on how schools involve parents in Response to Intervention (RTI), a framework for implementing targeted, tiered, research-based instruction. The purpose of this study was to interview selected parents, teachers, RTI specialists, and principals in three Title I elementary schools in one school district, plus one district-level administrator, in order to examine how elementary schools currently involve parents in RTI prereferral interventions, and to understand the factors that might facilitate or challenge such parent involvement. I employed a comparative case study qualitative design with each elementary school as the main unit of analysis. I conducted individual, in-depth interviews that lasted approximately 45-60 minutes with a total of 33 participants across the three school sites, including 11 parents, 12 teachers, and six RTI specialists, three principals, and one district-level administrator. I also analyzed documents related to RTI processes that are available through websites and participants. I used Strauss and Corbin’s (1998) three-step scheme for thematic/grounded theory analysis, and Atlas.ti as the electronic tool for management and analysis. Analyses of the data revealed that personnel across the sites largely agreed on how they explain RTI to parents and notify parents of student progress. Parents mostly disagreed with these accounts, stating instead that they learn about RTI and their child’s progress by approaching teachers or their own children with questions, or by examining report cards and student work that comes home. Personnel and parents cited various challenges for involving parents in RTI. However, they all also agreed that teachers are accessible and willing to reach out to parents, and that teachers already face considerable workloads. It appears that no district- or school-wide plan guides parent involvement practices in RTI at any of the three schools. Finally, I present a discussion of findings; implications for teachers, RTI implementation leaders, and Title school leaders; study limitations; and possibilities for future research.

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.