Distribution Of Learning Styles Research Articles

Students' Mathematical Connection Skills Reviewed From Learning Styles

The purpose of this study is to determine the distribution of student learning styles, to determine the average mathematical communication skills that have Auditory, Visual, and Kinestatic learning styles. and to find out if there are significant differences in the ability of mathematical connections between students who have auditory, visual, and kinesthetic learning styles. This research was conducted at SMAN 17 Pandeglang in class XII MIPA. The instrument used was in the form of a learning style questionnaire and a test of mathematical connection abilities. This type of research is a comparative study with a quantitative approach. Based on the results of data processing, that the distribution of student learning styles is included in the Auditory learning style of 34% of students, Visual 46% of students, and Kinesthetic 19% of students. By using a scale of 0-50, the average mathematical connection ability of students is 29.58, whereas when viewed from each distribution of learning styles, the average mathematical ability of auditory students is 24.89, visual students 28.33, and kinestatic students 34,40. Based on the results of the analysis with the ANOVA test and t test (Dunnet) that there is a significant difference in the ability of students' mathematical connections between auditory, visual and kinesthetic learning styles with a significant level of ? = 0.05, and the results of the value of Fcount > Ftable = 3.62 > 3,42, where the kinesthetic learning style has higher mathematical connection ability than auditory and visual learning styles.

Kemampuan Komunikasi Matematika Siswa Ditinjau dari Perbedaan Gaya Belajar Menurut David Kolb

The purpose of this study was to determine the distribution of student learning styles according to David Kolb's classification, to learn the average mathematical communication skills that have diverger learning styles, assimilators, convergers and accommodators, and to know whether or not there are significant differences in mathematical communication skills between students has a learning style diverger, assimilator, converger and accommodator. This research was conducted in MAN 2 Cirebon. The instruments used were the Learning style inventory (LSI) and mathematical communication skills tests. This type of research is a comparative study with a quantitative approach. Based on the results of data processing, the distribution of student learning styles included in diverger learning styles was 17% of students, assimilators were 27% of students, converger was 20% of students, and accommodators were 36% students. Using a scale of 0-50, the average mathematical communication ability of students is 28.63. Whereas when viewed from each distribution of learning styles, the ordinary mathematical communication skills of students diverger 12.80, students assimilator 32.88, student converger 44.17, and accommodator students 24.27. Based on the results of the analysis with the ANOVA test that there is a significant difference in students' mathematical communication skills between the learning style diverger, assimilator, converger and accommodator with a significant level of α = 5%.

Sudanese EFL Students' Preferred Learning Styles and Evaluation Techniques

The effectiveness of learning in any language is usually determined by the type of learning styles adopted by the language learner himself. This study traces the heterogeneous learning style used by EFL undergraduate students in the department of English language, Faculty of Education, University of Khartoum in Sudan. The study took place during the academic year 2016 / 2017. Undergraduate students have miscellaneous strategies they recommend to use promptly inside the classroom. To function productively in classrooms, teachers should think about their students' favored learning styles because at this university level students have different backgrounds, strengths, weaknesses, concerns, desires, and even they have different levels of motivation and styles to studying. Thus, teachers should be more acquainted with the divergent methods to their students' better learning. A questionnaire was given out to (205) male and female students which covered twenty-five indispensable items in learning styles. Students responses were analyzed statistically to measure the efficiency of these learning styles used by the students in the department of English language and work out whether the distribution of learning styles was distinct over the four years of the program. Results revealed that there existed notable mismatch and correspondences in learned style choices. This indicated that students have affirmative perspective towards learning styles. Thus, teachers should regard their students' preferred learning styles and try to use different teaching styles inside the classroom to avoid monotonous classes that are not harmonized with their students' learning styles. Suggestions are stated for required future research in finding out how language learning styles can reinforce students in becoming more competent foreign language learners. Keywords : Learning styles, Evaluation techniques, Sudanese EFL students DOI : 10.7176/JEP/10-3-15

Learning Style and Attitude toward Computer among Iranian Medical Students

Background and purpose: Presently, the method of medical teaching has shifted from lecture-based to computer-based. The learning style may play a key role in the attitude toward learning computer. The goal of this study was to study the relationship between the learning style and attitude toward computer among Iranian medical students. Methods: This cross-sectional study included 400 medical students. Barsch learning style inventory and a questionnaire on the attitude toward computer was sent to each student. The enthusiasm, anxiety, and overall attitude toward computer were compared among the different learning styles. Results: The response rate to the questionnaire was 91.8%. The distribution of learning styles in the students was 181 (49.3%) visual, 106 (28.9%) auditory, 27 (7.4%) tactual, and 53 (14.4%) overall. Visual learners were less anxious for computer use and showed more positive attitude toward computer. Sex, age, and academic grade were not associated with students’ attitude toward computer. Conclusions: The learning style is an important factor in the students’ attitude toward computer among medical students, which should be considered in planning computer-based learning programs. Keywords: LEARNING STYLE, ATTITUDE, COMPUTER, MEDICAL STUDENT, ANXIETY, ENTHUSIASM

Exploring the relation between learning style and cognitive impairment in patients with acquired brain injury

The way a patient prefers to approach or choose a learning situation represents the patient's learning style. The objective of this chart review study was to explore the relation between learning style and cognitive impairment in patients with acquired brain injury (ABI). We used data from files of 92 adult patients with ABI referred to inpatient rehabilitation, who completed the Adapted Learning Style Inventory (A-LSI) and at least one of the following neuropsychological tests: Trail Making Test, Rey Auditory Verbal Learning Test, WAIS-III Digit Span, Rey-Osterrieth Complex Figure Test–Copy, Stroop Color-Word Test, or the Brixton Spatial Anticipation Test. The A-LSI yielded the following distribution of learning styles: 4 doers, 48 observers, 2 deciders and 38 thinkers. No significant correlation coefficients were found between the neuropsychological tests and the A-LSI. Furthermore, Chi-square tests revealed no significant associations between learning style (observer, thinker) and cognitive impairment. The results of this exploratory study suggest that learning style and cognitive impairment are independent in patients with ABI.

The Distribution of Learning Styles and Types for Hard and Soft Business Majors

This study reports on the distribution of Kolb's four learning styles and four learning types for a sample of 437 volunteer management undergraduates divided by sex, hard-soft majors, and by major including accounting, finance, marketing and management information system. The overall results showed a preference for the assimilator learning style (35.4%) and 'thinking' learning type (62.8%). There was no sex difference in the distribution of styles or types, but there was a difference in the hard-soft majors distinction such that there was a higher proportion of women in the soft majors and a higher proportion of men in the hard majors. Among accounting majors there was a preference for the assimilator style (38.5%). Frequencies for the other majors were not large enough to allow separate analyses. Implications are noted for educators, trainers and learners.

Interactive learning in a higher education Level 1 mechanics module

Concern has been expressed that traditional higher education programmes are producing students with a surface approach to learning, rather than the desired deep approach. The aim of this study was to encourage Level 1 students (those taking the subject for the first time at higher education level) to develop such a deeper learning approach. Changes were made to both the teaching method and the form of examination paper for a Mechanics module taken by a cohort of 52 physics students. The cooperative learning approach was used to pose conceptual questions for interactive discussion. Preand post-module the students' conceptual understanding of mechanics was tested using the Force Concept Inventory (FCI) and they also completed a Contrasted Groups Learning Styles questionnaire so that we could assign a 'deep learning index'. Additionally the students completed the Learning Styles Inventory to gain a picture of the distribution of learning styles in the class. Qualitative information concerning the reaction of both the lecturer and students to the changes was also collected using a focus group enquiry, module evaluative questionnaires and reflective comments from the lecturer. Analysis of the quantitative data showed that there was no discernible overall change in either the deep learning index or the FCI score of the students. Analysis of the qualitative data, however, showed that experience of the module by both students and lecturer had been greatly improved by the changes.