Learning Preferences with Kernel-Based Methods

Learning of preference relations has recently received significant attention in machine learning community. It is closely related to the classification and regression analysis and can be reduced to these tasks. However, preference learning involves prediction of ordering of the data points rather than prediction of a single numerical value as in case of regression or a class label as in case of classification. Therefore, studying preference relations within a separate framework facilitates not only better theoretical understanding of the problem, but also motivates development of the efficient algorithms for the task. Preference learning has many applications in domains such as information retrieval, bioinformatics, natural language processing, etc. For example, algorithms that learn to rank are frequently used in search engines for ordering documents retrieved by the query. Preference learning methods have been also applied to collaborative filtering problems for predicting individual customer choices from the vast amount of user generated feedback. In this thesis we propose several algorithms for learning preference relations. These algorithms stem from well founded and robust class of regularized least-squares methods and have many attractive computational properties. In order to improve the performance of our methods, we introduce several non-linear kernel functions. Thus, contribution of this thesis is twofold: kernel functions for structured data that are used to take advantage of various non-vectorial data representations and the preference learning algorithms that are suitable for different tasks, namely efficient learning of preference relations, learning with large amount of training data, and semi-supervised preference learning. Proposed kernel-based algorithms and kernels are applied to the parse ranking task in natural language processing, document ranking in information retrieval, and remote homology detection in bioinformatics domain. Training of kernel-based ranking algorithms can be infeasible when the size of the training set is large. This problem is addressed by proposing a preference learning algorithm whose computation complexity scales linearly with the number of training data points. We also introduce sparse approximation of the algorithm that can be efficiently trained with large amount of data. For situations when small amount of labeled data but a large amount of unlabeled data is available, we propose a co-regularized preference learning algorithm. To conclude, the methods presented in this thesis address not only the problem of the efficient training of the algorithms but also fast regularization parameter selection, multiple output prediction, and cross-validation. Furthermore, proposed algorithms lead to notably better performance in many preference learning tasks considered.

Continuing education learning preferences and styles of legal clinic lawyers /

The purpose of this qualitative research was to study the learning preferences and styles of management lawyers who work in Ontario's legal aid clinics. Data were gathered from two sources and analyzed using the constant comparison method. A preand postconference survey provided the principal data on clinic lawyers' learning preferences. Follow-up interviews were then conducted with 3 purposefully selected survey participants to explore their personal learning styles. Kolb's experiential learning theory provided the theoretical framework for discussing personal learning styles. The findings showed a general consistency among the lawyers to learn by listening to lectures and experts. This preference may suggest a lingering influence from law school training. The lawyers' more informal learning associated with daily practice, however, appeared to be guided by various learning styles. The learning style discussions provided some support for Kolb's model but also confirmed some shortcomings noted by other authors. Educators who design continuing education programs for lawyers may benefit from some insights gained from this exploratory research. This study adds to a limited but growing body of work on the learning preferences and styles of lawyers and suggests new questions for future research.

Reaching and teaching the child with autism spectrum disorder : using learning preferences and strengths.

Manual que ofrece un enfoque positivo a la comprensión y la educación de los niños con autismo. Ofrece una mayor comprensión de la perspectiva y el comportamiento de un niño con autismo y explora cómo pueden utilizarse sus preferencias de aprendizaje, fortalezas e intereses para facilitar el aprendizaje y aumentar la motivación. La autora ha utilizado una mezcla de teorías de la personalidad, inteligencias múltiples y aprendizaje para crear un método de trabajo, Aprendizaje Preferencias y Fortalezas (LPS), para el desarrollo de habilidades de aprendizaje para la vida independiente en niños con autismo entre dos y doce años. Describe los principios básicos, preferencias de aprendizaje y los puntos fuertes típicos de los niños con autismo y ofrece una estructura de programa detallado, pero flexible, basada en estos conceptos. Fácil de seguir, se describen en cada capítulo actividades y orientaciones junto con ejemplos e ilustraciones.

Temperament and self-based correlates of cooperative, competitive and individualistic learning preferences

People vary in the extent to which they prefer cooperative, competitive or individualistic achievement tasks. In the present research, we conducted two studies designed to investigate correlates and possible roots of these social interdependence orientations, namely approach and avoidance temperament, general self-efficacy, implicit theories of intelligence, and contingencies of self-worth based in others’ approval, competition, and academic competence. The results indicated that approach temperament, general self-efficacy, and incremental theory were positively, and entity theory was negatively related to cooperative preferences (|r| range from .11 to .41); approach temperament, general self-efficacy, competition contingencies, and academic competence contingencies were positively related to competitive preferences (|r| range from .16 to .46); and avoidance temperament, entity theory, competitive contingencies, and academic competence contingencies were positively related, and incremental theory was negatively related to individualistic preferences (|r| range from .09 to .15). The findings are discussed with regard to the meaning of each of the three social interdependence orientations, cultural differences among the observed relations, and implications for practicioners.

Adult Learning in the Transformational Environments of the Digital Revolution: Connecting Theory and Practice

The purpose of this study was to investigate the learning preferences and the post-secondary educational experiences of a group of Net-Gen adult learners, aged between 18 and 35, currently working in the knowledge economy workplace, and their assessment of how adequately they were prepared to meet the requirements of the knowledge economy workplace. This study utilized an explanatory mixed-method research design. Participants completed a questionnaire providing information on their self-reported learning style preferences, their use of digital tools for formal and informal learning, their use of digital technologies in postsecondary educational experiences, and their use of digital technologies in their workplace. Four volunteers from the questionnaire respondents were selected to participate in interviews based on the diversity of their experiences in higher education, including digital environments, and the diversity of their knowledge economy workplaces. Data collected from the questionnaire were analyzed for descriptive and demographic statistics, and categorized so that common patterns could be identified from information gathered from the online questionnaire and interviews. Findings based on this study indicated that these Net-Gen adult learners were fluent with all types of digital technologies in collaborative environments, expecting their educational experiences to provide a similar experience. Participants clearly expressed an understanding that digital/collaborative aptitudes are essential to successful employment in the knowledge economy workplace. The findings of this study indicated that the majority of participants felt that their post-secondary educational experiences did not adequately prepare them to meet the expectations of this type of working environment.

Classroom Reform with CMPLE: A Cogenerative Mediation Process for Learning Environments

We have designed a classroom goal setting process whereby students and instructors rank, discuss, and combine their learning preferences and then rate their classroom with respect to those preferences. All participants have the opportunity to be collectively engaged in building a preferred learning environment.

A Biocultural Examination of Student Learning Behaviors in Large Undergraduate Lectures

Thesis (Ph.D.)--University of Washington, 2016-08

Tutor Inteligente para o Ensino em Medicina

O objectivo deste trabalho consistiu no desenvolvimento de um protótipo que possibilita a adaptação do conteúdo disponibilizado de acordo com as características pessoais e psicológicas do aluno, aplicado no ensino da Medicina, nomeadamente na componente de Desenho de Estudos da disciplina de Introdução à Medicina. Para o protótipo desenvolvido foi definida uma arquitectura constituída por três componentes: um Modelo de Aluno que engloba as características pessoais e psicológicas do aluno, um Modelo de Domínio constituído por um grafo de conceitos e um Modelo Pedagógico formado pelas regras de adaptação e mecanismos de interação utilizados para obter uma solução adaptativa. Os diferentes componentes desenvolvidos para este protótipo permitem que este apresente as seguintes funcionalidades: Acesso ao conceito adequado, tendo em consideração o nível de conhecimento do aluno; Visualização de conte udos adequados ao estilo de aprendizagem do aluno; Adaptação do percurso do aluno de acordo com os resultados obtidos; Atualização das preferências de aprendizagem, com base no comportamento demonstrado pelo aluno na interação com o sistema. A primeira versão da ferramenta j a foi implementada. No entanto ainda será realizada a avaliação do protótipo em ambiente de aprendizagem, com a maior brevidade possível.

Design of Personalization of Massive Open Online Courses

Massive Open Online Courses have been in the center of attention in the recent years. However, the main problem of all online learning environments is their lack of personalization according to the learners’ knowledge, learning styles and other learning preferences. This research explores the parameters and features used for personalization in the literature and based on them, evaluates to see how well the current MOOC platforms have been personalized. Then, proposes a design framework for personalization of MOOC platforms that fulfills most of the personalization parameters in the literature including the learning style as well as personalization features. The result of an assessment made for the proposed design framework shows that the framework well supports personalization of MOOCs.

Características de personalidade e preferências de aprendizagens: um estudo exploratório

O presente trabalho descreve um estudo exploratório de características de personalidade e preferências de aprendizagem em estudantes universitários de quatro cursos da área da Saúde: Medicina, Odontologia, Enfermagem e Nutrição. Para este estudo utilizou-se como fonte de dados dois instrumentos de medida, as Escalas de personalidade de Comrey e o Inventário de Pre2erências de Aprendizagem. O tratamento dos dados foi feito através das estatísticas distribuição de frequência, correlação e análise da regressão. Os resultados do tratamento estatístico não nos possibilitou. alcançar nenhuma conclusão objetiva das relações entre as variáveis de personalidade e preferências de aprendizagem, no entanto, forneceu-nos dados para posteriores estudos.

Abstrahierendes Lernen durch aktive Modellbildung: Evaluation eines Prozesses und einer Lernumgebung

Die Fähigkeit zum Lernen durch Abstraktion aus Erfahrungen unterscheidet Experten von Novizen. Wir stellen einen Prozess für individuelles abstrahierendes Lernen und eine diesen Prozess unterstützende Lernumgebung vor. Die Ergebnisse einer Pilotstudie zeigen, dass Lernende unter Nutzung der Lernumgebung aus Fallbeispielen ein abstraktes Modell erstellen und über ihren Prozess reflektieren konnten. Dies fiel ihnen leichter, wenn die Fallbeispiele wenige gemeinsame Oberflächenmerkmale aufwiesen. Im Gegensatz zum intendierten Lernprozess wandten manche Lernende einen anderen Prozess an.

The effect of multimedia instruction on achievement of college history students

The purpose of this study was to investigate the effect of multimedia instruction on achievement of college students in AMR 2010 from exploration and discovery to 1865. A non-equivalent control group design was used. The dependent variable was achievement. The independent variables were learning styles, method of instruction, and visual clarifiers (notes). The study was conducted using two history sections from Palm Beach Community College, in Boca Raton, Florida, between August and December, 1998. Data were obtained by means of placement scores, posttests, the Productivity Environmental Preference Survey (PEPS), and a researcher-developed student survey. Statistical analysis of the data was done using SPSS statistical software. Demographic variables were compared using Chi square. T tests were run on the posttests to determine the equality of variances. The posttest scores of the groups were compared using the analysis of covariance (ANCOVA) at the .05 level of significance. The first hypothesis there is a significant difference in students' learning of U.S. History when students receive multimedia instruction was supported, F (1, 52) = 16.88, p < .0005, and F = (1, 53) = 8.52, p < .005 for Tests 2 and 3, respectively. The second hypothesis there is a significant difference on the effectiveness of multimedia instruction based on students' various learning preferences was not supported. The last hypotheses there is a significant difference on students' learning of U.S. History when students whose first language is other than English and students who need remediation receive visual clarifiers were not supported. Analysis of covariance (ANCOVA) indicated no difference between the groups on Test 1, Test 2, or Test 3: F (1, 45) = .01, p < .940, F (1, 52) = .77, p < .385, and F (1, 53) =.17, p < .678, respectively, for language. Analysis of covariance (ANCOVA) indicated no significant difference on Test 1, Test 2, or Test 3, between the groups on the variable remediation: F (1, 45) = .31, p < .580, F (1, 52) = 1.44, p < .236, and F (1, 53) = .21, p < .645, respectively. ^

The effect of multimedia instruction on achievement of college history students

The purpose of this study was to investigate the effect of multimedia instruction on achievement of college students in AMH 2010 from exploration and discovery to1865. A non-equivalent control group design was used. The dependent variable was achievement. The independent variables were learning styles method of instruction, and visual clarifiers (notes). The study was conducted using two history sections from Palm Beach Community College, in Boca Raton, Florida, between August and December, 1998. Data were obtained by means of placement scores, posttests, the Productivity Environmental Preference Survey (PEPS), and a researcher-developed student survey. Statistical analysis of the data was done using SPSS statistical software. Demographic variables were compared using Chi square. T tests were run on the posttests to determine the equality of variances. The posttest scores of the groups were compared using the analysis of covariance (ANCOVA) at the .05 level of significance. The first hypothesis there is a significant difference in students' learning of U.S. History when students receive multimedia instruction was supported, F = (1, 52)= 688, p < .0005, and F = (1, 53) = 8.52, p < .005for Tests 2 and 3, respectively. The second hypothesis there is a significant difference on the effectiveness of multimedia instruction based on students' various learning preferences was not supported. The last hypotheses there is a significant difference on students' learning of U.S. History when students whose first language is other than English and students who need remediation receive visual clarifiers were not supported. Analysis of covariance (ANCOVA) indicated no difference between the groups on Test 1, Test 2, or Test 3: F (1, 4 5)= .01, p < .940, F (l, 52) = .77, p < .385, and F (1,53) =.17, p > .678, respectively, for language. Analysis of covariance (ANCOVA) indicated no significant difference on Test 1, Test 2, or Test 3, between the groups on the variable remediation: F (1, 45) = .31, p < .580, F (1, 52) = 1.44, p < .236, and F (1, 53) = .21, p < .645, respectively.

Compétences en littératie visuelle chez les étudiantEs en biologie au niveau collégial: bilan des apprentissages selon des activités digitales ou des activités de dessins à la main

This study examines the role of visual literacy in learning biology. Biology teachers promote the use of digital images as a learning tool for two reasons: because biology is the most visual of the sciences, and the use of imagery is becoming increasingly important with the advent of bioinformatics; and because studies indicate that this current generation of teenagers have a cognitive structure that is formed through exposure to digital media. On the other hand, there is concern that students are not being exposed enough to the traditional methods of processing biological information - thought to encourage left-brain sequential thinking patterns. Theories of Embodied Cognition point to the importance of hand-drawing for proper assimilation of knowledge, and theories of Multiple Intelligences suggest that some students may learn more easily using traditional pedagogical tools. To test the claim that digital learning tools enhance the acquisition of visual literacy in this generation of biology students, a learning intervention was carried out with 33 students enrolled in an introductory college biology course. The study compared learning outcomes following two types of learning tools. One learning tool was a traditional drawing activity, and the other was an interactive digital activity carried out on a computer. The sample was divided into two random groups, and a crossover design was implemented with two separate interventions. In the first intervention students learned how to draw and label a cell. Group 1 learned the material by computer and Group 2 learned the material by hand-drawing. In the second intervention, students learned how to draw the phases of mitosis, and the two groups were inverted. After each learning activity, students were given a quiz on the material they had learned. Students were also asked to self-evaluate their performance on each quiz, in an attempt to measure their level of metacognition. At the end of the study, they were asked to fill out a questionnaire that was used to measure the level of task engagement the students felt towards the two types of learning activities. In this study, following the first testing phase, the students who learned the material by drawing had a significantly higher average grade on the associated quiz compared to that of those who learned the material by computer. The difference was lost with the second “cross-over” trial. There was no correlation for either group between the grade the students thought they had earned through self-evaluation, and the grade that they received. In terms of different measures of task engagement, there were no significant differences between the two groups. One finding from the study showed a positive correlation between grade and self-reported time spent playing video games, and a negative correlation between grade and self-reported interest in drawing. This study provides little evidence to support claims that the use of digital tools enhances learning, but does provide evidence to support claims that drawing by hand is beneficial for learning biological images. However, the small sample size, limited number and type of learning tasks, and the indirect means of measuring levels of metacognition and task engagement restrict generalisation of these conclusions. Nevertheless, this study indicates that teachers should not use digital learning tools to the exclusion of traditional drawing activities: further studies on the effectiveness of these tools are warranted. Students in this study commented that the computer tool seemed more accurate and detailed - even though the two learning tools carried identical information. Thus there was a mismatch between the perception of the usefulness of computers as a learning tool and the reality, which again points to the need for an objective assessment of their usefulness. Students should be given the opportunity to try out a variety of traditional and digital learning tools in order to address their different learning preferences.