Providing high school students a course of study for becoming competent and thorough lifelong independent readers of complex texts was the goal for this thesis. This is accomplished by integrating literary theory that looks beyond just the typical level of analysis often emphasized in many Florida classrooms. If put into use and successful, this curriculum guide will aid Florida teachers in endowing their students with a new level of ability to analyze literature. Research of prior work done in the field of integrating critical theory into high school classrooms was analyzed and synthesized in order to create a larger course of critical theory study to be completed during the senior year of high school in the state of Florida. The curriculum guide acts as a starting point, providing teachers with all the tools necessary to bring literary theory into the high school classroom while maintaining their individual teaching style. The curriculum guide is broken into four distinct units which follow the most common course of Florida twelfth grade study, the English canon, with each chapter addressing two literary theories. The literary theories utilized are: New Criticism, New Historicism, Feminism, Marxism, Reader Response, Psychoanalysis, Structuralism, and Deconstruction.

An important problem that appears often when analyzing data involves identifying irregular or abnormal data points called outliers. This problem broadly arises under two scenarios: when outliers are to be removed from the data before analysis, and when useful information or knowledge can be extracted by the outliers themselves. Outlier Detection in the context of the second scenario is a research field that has attracted significant attention in a broad range of useful applications. For example, in credit card transaction data, outliers might indicate potential fraud; in network traffic data, outliers might represent potential intrusion attempts. The basis of deciding if a data point is an outlier is often some measure or notion of dissimilarity between the data point under consideration and the rest. Traditional outlier detection methods assume numerical or ordinal data, and compute pair-wise distances between data points. However, the notion of distance or similarity for categorical data is more difficult to define. Moreover, the size of currently available data sets dictates the need for fast and scalable outlier detection methods, thus precluding distance computations. Additionally, these methods must be applicable to data which might be distributed among different locations. In this work, we propose novel strategies to efficiently deal with large distributed data containing mixed-type attributes. Specifically, we first propose a fast and scalable algorithm for categorical data (AVF), and its parallel version based on MapReduce (MR-AVF). We extend AVF and introduce a fast outlier detection algorithm for large distributed data with mixed-type attributes (ODMAD). Finally, we modify ODMAD in order to deal with very high-dimensional categorical data. Experiments with large real-world and synthetic data show that the proposed methods exhibit large performance gains and high scalability compared to the state-of-the-art, while achieving similar accuracy detection rates.