In recent years, engineering education has witnessed a sharp increase in research aimed at the outcomes of academic success and persistence within engineering programs. However, research surrounding the key forces shaping student persistence remains unknown. This study explores enhancements and broader perspectives of learning; the relationship among dimensions of resilience theory and learning styles in engineering students to identify elements of both that contribute towards academic persistence and to determine which components of both contribute towards strengthening students’ academic persistence in engineering. The study was conducted using two quantitative self-reporting instruments to measure resilience and learning style preference, the Personal Resilience Questionnaire (PQR) and the Index of Learning Styles (ILS). Retention was measured as the continuous enrollment of a student into the second semester of the first-year engineering program. Results indicate that the following have a statistically significant effect on student persistence in engineering programs at Texas A&M University: learning style construct sequential; resilience constructs positive (self) and focus; with both tools combined, positive (self), organized, positive (world), flexibility (self) and focus; and a newly combined construct, Walton’s self-efficacy.

The purpose of this study was to determine the learning styles of preparatory school students from Gazi University and examine the relationship between students&rsquo / learning style preferences (LSP) and faculty students will study in, gender, proficiency level of English and achievement scores on listening, reading, grammar, and writing in the English Course. The instrument, Index of Learning Styles (ILS), was administered to 367 randomly selected students. As for the data analysis, descriptive statistics portrayed the frequencies, percentages, means and standard deviations, the t test was conducted to see whether students&rsquo / achievement scores differ according to their LSPs and the Crosstabs procedure was conducted to investigate whether the LSPs of the students at Gazi University differ according to faculty they will study in, gender and level of proficiency. The results indicated that there was no significant difference between students&rsquo / LSPs and faculty, gender, level and achievement scores.

The research purpose was to investigate the differences between learning preferences (i.e., Active-Reflective, Sensing-Intuitive, Visual-Verbal, and Sequential-Global) determined by the Index of Learning Style and gender (i.e., Male and Female) in regards to standardized achievement multiple-choice test performance determined by the Illinois Nurse Aide Competency Examination (INACE), i.e., overall INACE performance and INACE performance based on six duty areas (i.e., communicating information, performing basic nursing skills, performing personal care, performing basic restorative skills, providing mental health-services, and providing for resident’s rights) of nurse aide students. The study explored the relationship between variables using a non-experimental, comparative and descriptive approach. The nurse aide students who completed the Illinois approved Basic Nurse Aide Training (BNAT) and 21-mandated skills assessment and were ready to take the Illinois Nurse Aide Competency Examination (INACE) in the month of October 2018 and December 2018 at various community colleges across the state of Illinois were the participants of the study. A sample of 800 nurse aide students were selected through stratified (north, central, and south) random sampling out of which N = 472 participated in the study representing the actual sample.

Few studies of learning styles among adults with special needs exist worldwide. Even though there are large numbers of adults with special needs, this population in university education has been largely ignored in educational research. Therefore, this study aimed to gather and analyze learning styles of adult special needs students and to provide data for researchers interested in the fields of learning styles, adult education, and special education. This study examined the learning style patterns among special needs adult students at King Saud University as measured by the dimensions of the Index of Learning Styles, which include active/reflective, sensing/intuitive, visual/verbal, and sequential/global dimensions. The study also included variables of gender, age, special need conditions, and years studying in the university. The research questions were (a) What are the learning style patterns among special needs students at King Saud University? (b) Are there differences in the learning style patterns among special needs students at King Saud University by gender? (c) Are there differences in the learning style patterns among special needs students at King Saud University by age? (d) Are there differences in the learning style patterns among special needs students at King Saud University by special need condition (visual, hearing, physical, or other)? and (e) Are there differences in the learning style patterns among special needs students at King Saud University by their years attending the university? The participants of this study were 168 special needs students at King Saud University from different majors and colleges during spring semester 2017. The questionnaire was distributed electronically to the students through the Offices of Special Needs. The data were analyzed using descriptive statistics, t tests, one-way ANOVAs, and chi-square tests of independence. The study discussed the learning styles of the participants and found the majority of participants were balanced learners. There were no statistically significant differences in the variable of gender. On the other hand, the study found there were significant differences on the variables of age and special need conditions on the visual/verbal dimension; and years studying in university on the sensing/intuitive and visual/verbal dimension.

Submitted by Johnny Rodrigues (johnnyrodrigues@ufcg.edu.br) on 2018-05-01T13:20:15Z No. of bitstreams: 1 JANDERSON JASON BARBOSA AGUIAR - DISSERTAÇÃO PPGCC 2015..pdf: 4415910 bytes, checksum: f0d6a47b1a591745921f0dde35a45bb1 (MD5) / Made available in DSpace on 2018-05-01T13:20:15Z (GMT). No. of bitstreams: 1 JANDERSON JASON BARBOSA AGUIAR - DISSERTAÇÃO PPGCC 2015..pdf: 4415910 bytes, checksum: f0d6a47b1a591745921f0dde35a45bb1 (MD5) Previous issue date: 2015-08-25 / Capes / Os Objetos de Aprendizagem (OA) utilizados em cursos presenciais ou à distância são armazenados em ambientes computacionais usados no processo de ensino-aprendizagem com tendência de crescimento da sua quantidade com o passar do tempo. Apesar dos Sistemas de Recomendação (SR) serem atualmente utilizados com sucesso para recomendar itens em vários domínios, o contexto educacional possui particularidades (por exemplo, questões pedagógicas) que tornam ainda mais desafiadora a criação desses sistemas. A Personalidade — que pode ser definida como um padrão de comportamento consistente originado internamente no indivíduo — influencia o processo de tomada de decisão. Além disso, há a preocupação com os Estilos de Aprendizagem (EA), a partir dos quais os aprendizes percebem, processam e retêm as informações. Diante do exposto, a pesquisa ora descrita visa a propor um modelo de Sistema de Recomendação Educacional (SRE) utilizando os conceitos de EA e Personalidade na construção do perfil dos discentes, para realizar uma seleção personalizada de OA a serem recomendados. Embora ainda seja desafiador criar SRE envolvendo a extração e inserção dos conceitos psicológicos comentados, nesta dissertação é apresentado e avaliado um modelo que recomenda OA, seguindo o padrão IEEE LOM, a partir da extração dos EA via inventário ILS (Index of Learning Styles) e da extração dos Traços de Personalidade (TP) via Five Labs, ferramenta online de análise semântica de postagens do Facebook. Considerando métricas utilizadas em SR, um experimento realizado com alunos de Ciência da Computação indicou que o modelo proposto proporcionou resultados melhores ou similares, em comparação a outras abordagens de recomendação pesquisadas. Portanto, a abordagem proposta se mostra promissora para a recomendação personalizada de conteúdo no âmbito educacional. / Learning Objects (LO) used in on-site courses or distance learning are stored in computing environments used in the teaching-learning process, and tend to grow their numbers over time. Although Recommendation Systems (RS) are currently being used successfully to recommend items in various fields, the educational context has special features (for example, pedagogical issues) which make the creation of such systems even more challenging. The Personality — which can be defined as a consistent pattern of behavior originated internally in an individual — influences the decision-making process. In addition, there is concern with the Learning Styles (LS), through which learners perceive, process, and retain information. Based on the above considerations, this research aims to propose a model of RS for Learning (RSL) using the concepts of LS and Personality in building the profile of students, in order to make a custom selection of LO to be recommended. Although it is still challenging to create RSL involving the extraction and insertion of psychological concepts as previously mentioned, in this dissertation a model that recommends LO is presented and evaluated, following the IEEE LOM standard, based on the extraction of LS via Index of Learning Styles and of Personality Traits (PT) via Five Labs, an online tool for semantic analysis of Facebook posts. Considering metrics known in RS, an experiment with computer science students indicated that the proposed model provided similar or better results when compared to other recommendation approaches. Therefore, the proposed approach seems promising for personalized content recommendation in the education field.

Learning style preferences play a significant role during the learning and teaching process. Therefore, a multitude of researchers have developed different models to accommodate students’ various learning styles. Those models share the same goal of trying to classify a particular students’ learning style and to provide an overview of better teaching strategies for educators. This paper presents a research study based on a survey that investigates the learning style preferences of computer network vocational senior secondary school students in Yogyakarta Province, Indonesia. This survey uses the Index of Learning Styles (ILS) questionnaire developed by Felder and Solomon. In total, 162 data sets from five different schools in five different areas were collected in order to represent the Yogyakarta Province. The findings from the study show that students participating in computer network vocational training preferred active (82.66%), sensing (67.66%), visual (83.83%), and sequential (52.44%) learning styles. Students most strongly prefer visual and least favor verbal (16.17%). Identifying learning styles can benefit teachers as they customize teaching methods and can maximize the learning and teaching process.

Learning style preferences play a significant role during the learning and teaching process. Therefore, a multitude of researchers have developed different models to accommodate students’ various learning styles. Those models share the same goal of trying to classify a particular students’ learning style and to provide an overview of better teaching strategies for educators. This paper presents a research study based on a survey that investigates the learning style preferences of computer network vocational senior secondary school students in Yogyakarta Province, Indonesia. This survey uses the Index of Learning Styles (ILS) questionnaire developed by Felder and Solomon. In total, 162 data sets from five different schools in five different areas were collected in order to represent the Yogyakarta Province. The findings from the study show that students participating in computer network vocational training preferred active (82.66%), sensing (67.66%), visual (83.83%), and sequential (52.44%) learning styles. Students most strongly prefer visual and least favor verbal (16.17%). Identifying learning styles can benefit teachers as they customize teaching methods and can maximize the learning and teaching process.