FINDING TOPICS IN CREATIVE WRITING ON ENVIRONMENTAL PRESERVATION FOR BETTER TEACHING STRATEGIES: A CASE OF STUDY IN AN ELEMENTARY SCHOOL FROM COLOMBIA

In this research, essays on trees preservation of fourth grade students (elementary school from Colombia) were evaluated with Latent Dirichlet Allocation (LDA). The objective was extracting the fundamental topics, to understand the students’ behavior and awareness towards the environment from the creative writing. The computational results suggest the student’s reflections on environment preservation are focused on five main topics in: Teach-Learn to care for the environment, Explore-discover the environment, Well-being of the environment, Concern for the environment, and Restoration and conservation of the environment. This text analysis by LDA can complement the manual analysis of teachers, avoiding the veracity bias and allowing the enhancement of teaching strategies.En esta investigación, se evaluaron ensayos sobre la preservación de árboles de estudiantes de cuarto grado (escuela primaria de Colombia) con Latent Dirichlet Allocation (LDA). El objetivo fue extraer los temas fundamentales, para comprender el comportamiento y la conciencia de los estudiantes hacia el medio ambiente a partir de la escritura creativa. Los resultados computacionales sugieren que las reflexiones del estudiante sobre la preservación del medio ambiente se centran en cinco temas principales en: Enseñar-Aprender a cuidar el medio ambiente, Explorar-descubrir el medio ambiente, Bienestar del medio ambiente, Preocupación por el medio ambiente y Restauración y conservación del entorno. Este análisis de texto por LDA puede complementar el análisis manual de los docentes, evitando el sesgo de veracidad y permitiendo potenciar las estrategias de enseñanza

Bayesian hierarchical modeling for the forensic evaluation of handwritten documents

The analysis of handwritten evidence has been used widely in courts in the United States since the 1930s (Osborn, 1946). Traditional evaluations are conducted by trained forensic examiners. More recently, there has been a movement toward objective and probability-based evaluation of evidence, and a variety of governing bodies have made explicit calls for research to support the scientific underpinnings of the field (National Research Council, 2009; President\u27s Council of Advisors on Science and Technology (US), 2016; National Institutes of Standards and Technology). This body of work makes contributions to help satisfy those needs for the evaluation of handwritten documents. We develop a framework to evaluate a questioned writing sample against a finite set of genuine writing samples from known sources. Our approach is fully automated, reducing the opportunity for cognitive biases to enter the analysis pipeline through regular examiner intervention. Our methods are able to handle all writing styles together, and result in estimated probabilities of writership based on parametric modeling. We contribute open-source datasets, code, and algorithms. A document is prepared for the evaluation processed by first being scanned and stored as an image file. The image is processed and the text within is decomposed into a sequence of disjoint graphical structures. The graphs serve as the smallest unit of writing we will consider, and features extracted from them are used as data for modeling. Chapter 2 describes the image processing steps and introduces a distance measure for the graphs. The distance measure is used in a K-means clustering algorithm (Forgy, 1965; Lloyd, 1982; Gan and Ng, 2017), which results in a clustering template with 40 exemplar structures. The primary feature we extract from each graph is a cluster assignment. We do so by comparing each graph to the template and making assignments based on the exemplar to which each graph is most similar in structure. The cluster assignment feature is used for a writer identification exercise using a Bayesian hierarchical model on a small set of 27 writers. In Chapter 3 we incorporate new data sources and a larger number of writers in the clustering algorithm to produce an updated template. A mixture component is added to the hierarchical model and we explore the relationship between a writer\u27s estimated mixing parameter and their writing style. In Chapter 4 we expand the hierarchical model to include other graph-based features, in addition to cluster assignments. We incorporate an angular feature with support on the polar coordinate system into the hierarchical modeling framework using a circular probability density function. The new model is applied and tested in three applications