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As educational professionals strive to help students become efficient and effective learners, they must assist in the development of student learning strategies and a greater understanding of the learning process. The purpose of this study was to analyze and compare the learning pattern preferences of middle and high school students in general education and special education settings. The results of this study were intended to help guide teachers and other education professionals to make informed decisions about differentiating instruction in a way to reach more, if not all, students in their classroom. The results could furthermore assist educators in fostering greater self-knowledge and self-advocacy in students, which then can assist them to become active participants of their own learning experiences. Archival data was examined using scores of middle and high school students on the Learning Connections Inventory (LCI), the survey associated with the Let Me Learn Process®. 251 students LCI scores were studied on the basis of grade level and special education classification.<br>Research questions utilized one-way MANOVA's in order to determine preference for particular individual patterns on the LCI. The first set of research questions compared students in special education and students in general education. The second set of questions compared students in special education broken down by classification, specifically, Other Health Impairment and Specific Learning Disability. Analyses revealed preference for certain LCI patterns between the groups examined. This study was intended to be a starting point for the analysis of the learning patterns of special education students. Once pattern preferences and the interactions between preferences are identified, and the utility of the Let Me Learn Process® is examined, a greater understanding of learning will occur in combination with the development of self-advocacy skills in the classroom. Overall, the Let Me Learn Process® has been shown to have promise in utilizing cognition, conation and affectation approaches in order to assist in developing effective learning strategies. As each of these elements is taken into consideration, this process can allow learners to become active participants in their own learning process. / School of Education; / School Psychology / PhD; / Dissertation;

Businesses and Human Resources professionals face the ongoing challenge of continuously upskilling and developing employees. Changes to processes or procedures, changes in technology, changes in job functions, and updates or changes to compliance laws or regulations are all reasons that employees must attend and complete employer-developed training. This study utilized the updated Multimodal Paired Associates Learning Test, version four (MMPALT-IV) instrument to determine perceptual learning styles and to determine if there exists a measureable difference in Latinos perceptual learning styles. The purpose of this study was to measure the perceptual learning styles of Latino adults from Central and South America utilizing the MMPALT-IV. The study compared Latinos born in either Central or South American to Caucasians born in the United States. The variables that were examined included gender, age, place of birth, and education level. A convenience sample of 40 adults living in the Tampa Bay area was used. Of that population, 20 individuals (10 females, 10 males) born in either Central or South America were participants of the study; each completing the seven subtests for the MMPALT-IV. Additionally, 20 Caucasian individuals (10 females, 10 males) born in the United States participated in the study as a comparison group and completed the same seven subtests for the MMPALT-IV. The results of this study indicate that there were no differences in preferred perceptual modalities based on race/ethnicity and gender, with the exception of the Kinesthetic where Latinos performed significantly higher than Caucasians. The implications for this study are broad ranging and can be applied to the corporate learning environment or any place adults spend time learning new knowledge, skills, or abilities.

This study presents a model for the organization of educational content customized for environments of individual studies. For many students the availability of content in general form can not be efficient. It proposed a multilevel structure of concepts to provide the development of different combinations to show the same content. The work shows that it is possible to customize the content in order to encourage other students with the use of proximal learning standards. These patterns are obtained from the analysis of the action of students with positive results in the individual organization of the content. The formal representation establishes the definition of the student profile, multi-level content, the distribution plan of correction of concepts and teaching career. The structure of the trajectory of student teaching is formally established by the method of finite differences. The system uses artificial intelligence techniques to organize and personalize content reactively. Customization is provided by an artificial neural network that enables the classification of the student profile and assign that profile to a standard proximal learning. To mediate and adjust the contents of a reactive system was inserted into a set of rules from experts in teaching. The experiment showed the applicability and appropriateness of the proposed model. The results indicated the suitability of the approach by automating the organization\'s custom content so adaptive and reactive. The intelligent system to establish the structure of the custom content to be presented was considered efficient, giving the student a better use of the content, with higher and lower final average study time and content presented. / Este trabalho apresenta uma modelagem para a organização personalizada de conteúdos didáticos para ambientes de estudos individuais. Para muitos estudantes a disponibilização do conteúdo em formato generalizado pode não ser eficiente. É proposta uma estrutura multinível de conceitos para proporcionar o desenvolvimento de diferentes combinações para a apresentação do mesmo conteúdo. O trabalho mostra que é possível personalizar o conteúdo de forma a favorecer outros estudantes com o uso de padrões proximais de aprendizagem. Estes padrões são obtidos da análise da ação de estudantes, com resultados positivos na organização individual do conteúdo. A representação formal estabelece a definição do perfil do estudante, o conteúdo multinível, o plano de distribuição dos conceitos e a correção da trajetória didática. A estruturação da trajetória didática do estudante é formalmente estabelecida pelo método das diferenças finitas. O sistema utiliza técnicas de inteligência artificial para organizar e personalizar reativamente o conteúdo. A personalização é proporcionada por uma rede neural artificial que possibilita a classificação do perfil do estudante e associa esse perfil a um padrão proximal de aprendizagem. Para mediar e ajustar o conteúdo de forma reativa foi inserido no sistema um conjunto de regras de especialistas em docência. O experimento realizado mostrou a aplicabilidade e a adequação da modelagem proposta. Os resultados indicaram a adequação da abordagem, automatizando a organização personalizada do conteúdo de forma adaptativa e reativa. O sistema inteligente ao estabelecer a estruturação personalizada do conteúdo a ser apresentado foi considerado eficiente, proporcionando ao estudante um melhor aproveitamento do conteúdo, com maior média final e menor tempo de estudo e conteúdo apresentado. / Doutor em Ciências

The thesis presents a model, suitable for the design of any type of qualification in integrative eGovernment education. The integrative approach combines education of adult learners and students and promotes international cooperation.

This work deals with the prediction of numerical series whose application is suitable for prediction of stock prices. They explain the procedures for analysis and works with price charts. Also explains the methods of machine learning. Knowledge is used to build a program that finds patterns in numerical series for estimation.