scholarly journals Are learning style preferences of health science students predictive of their attitudes towards e-learning?

2009 ◽  
Vol 25 (4) ◽  
Author(s):  
Ted Brown ◽  
Maryam Zoghi ◽  
Brett Williams ◽  
Shapour Jaberzadeh ◽  
Louis Roller ◽  
...  
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Linear Regression Analysis ◽  
Health Science ◽  
Science Students ◽  
Health Science Students ◽  
Learning Style Preferences ◽  
E Learning ◽  
Students Attitudes ◽  
Style Preferences

<span>The objective for this study was to determine whether learning style preferences of health science students could predict their attitudes to e-learning. A survey comprising the </span><em>Index of Learning Styles</em><span> (ILS) and the </span><em>Online Learning Environment Survey</em><span> (OLES) was distributed to 2885 students enrolled in 10 different health science programs at an Australian university. A total of 822 useable surveys were returned generating a response rate of 29.3%. Using </span><em>SPSS</em><span>, a linear regression analysis was completed. On the ILS Active-Reflective dimension, 44% of health science students reported a preference as being active learners, 60% as sensing learners, and 64% as sequential learners. Students' attitudes toward e-learning using the OLES showed that their </span><em>preferred</em><span> scores for all 9 subscales were higher than their </span><em>actual</em><span> scores. The linear regression analysis results indicated that ILS learning styles accounted for a small percentage of the OLES </span><em>actual</em><span> and </span><em>preferred</em><span> subscales' variance. For the OLES </span><em>actual</em><span> subscales, the ILS Active-Reflective and Sensing-Intuitive learning style dimensions were the most frequent predictors of health science students' attitudes towards e-learning. For the OLES </span><em>preferred</em><span> subscales, ILS Active-Reflective and Sequential-Global learning style dimensions accounted for the most frequent source of variance. It appears that the learning styles of health science students (as measured by the ILS) can be used only to a limited extent as a predictor of students' attitudes towards e-learning. Nevertheless, educators should still consider student learning styles in the context of using technology for instructional purposes.</span>

Predictors of attitudes to e‐learning of Australian health care students

2010 ◽  
Vol 2 (1) ◽  
pp. 60-76 ◽  
Author(s):  
Ted Brown ◽  
Brett Williams ◽  
Shapour Jaberzadeh ◽  
Louis Roller ◽  
Claire Palermo ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Linear Regression Analysis ◽  
Health Science ◽  
Computer Assisted Instruction ◽  
Computer Assisted ◽  
Science Students ◽  
Health Science Students ◽  
E Learning ◽  
Students Attitudes ◽  
Assisted Instruction

Computers and computer‐assisted instruction are being used with increasing frequency in the area of health science student education, yet students’ attitudes towards the use of e‐learning technology and computer‐assisted instruction have received limited attention to date. The purpose of this study was to investigate the significant predictors of health science students’ attitudes towards e‐learning and computer‐assisted instruction. All students enrolled in health science programmes (n=2885) at a large multi‐campus Australian university in 2006‐2007, were asked to complete a questionnaire. This included the Online Learning Environment Survey (OLES), the Computer Attitude Survey (CAS), and the Attitude Toward Computer‐Assisted Instruction Semantic Differential Scale (ATCAISDS). A multiple linear regression analysis was used to determine the significant predictors of health science students’ attitudes to e‐learning. The Attitude Toward Computers in General (CASg) and the Attitude Toward Computers in Education (CASe) subscales from the CAS were the dependent (criterion) variables for the regression analysis. A total of 822 usable questionnaires were returned, accounting for a 29.5 per cent response rate. Three significant predictors of CASg and five significant predictors of CASe were found. Respondents’ age and OLES Equity were found to be predictors on both CAS scales. Health science educators need to take the age of students and the extent to which students perceive that they are treated equally by a teacher/tutor/instructor (equity) into consideration when looking at determinants of students’ attitudes towards e‐learning and technology.

scholarly journals Using learning styles data to inform e-learning design: A study comparing undergraduates, postgraduates and e-educators

2011 ◽  
Vol 27 (6) ◽  
Author(s):  
Julie Willems
Keyword(s):  
Learning Styles ◽  
Learning Environments ◽  
Learning Style ◽  
Mixed Methods Research ◽  
Teaching And Learning ◽  
Learning Style Preferences ◽  
Index Of Learning Styles ◽  
Significant Difference ◽  
E Learning ◽  
Style Preferences

<span>What are the differences in learning styles between students and educators who teach and/or design their e-learning environments? Are there variations in the learning styles of students at different levels of study? How may we use this learning styles data to inform the design in e-learning environments? This paper details mixed-methods research with three cohorts teaching and learning in e-learning environments in higher education: novice undergraduate e-learners, graduate e-learners, and educators teaching in, or designing for, e-learning environments (Willems, 2010). Quantitative findings from the </span><em>Index of Learning Styles (ILS)</em><span> (Felder &amp; Silverman, 1988; Felder &amp; Soloman, 1991, 1994) reflect an alignment of the results between both the graduate e-learner and e-educator cohorts across all four domains of the</span><em>ILS</em><span>, suggesting homogeneity of results between these two cohorts. By contrast, there was a statistically significant difference between the results of the graduate and educator cohorts with those of the undergraduate e-learners on two domains: sensing-intuitive (p=0.015) and the global-sequential (p=0.007), suggesting divergent learning style preferences. Qualitative data was also gathered to gain insights on participants' responses to their learning style results</span>

scholarly journals Personalized E-Learning: Relation Between Felder– Silverman Model and Academic Performance

2019 ◽  
Vol 24 (1) ◽  
pp. 25-31 ◽  
Author(s):  
Diana Zagulova ◽  
Viktorija Boltunova ◽  
Sabina Katalnikova ◽  
Natalya Prokofyeva ◽  
Kateryna Synytsya
Keyword(s):  
Academic Performance ◽  
Learning Styles ◽  
Learning Style ◽  
University Education ◽  
Global Learning ◽  
Learning Style Preferences ◽  
Index Of Learning Styles ◽  
E Learning ◽  
Style Preferences

Abstract The growing demands for the training of students and the need for continuous improvement of the quality of university education make it necessary to find and apply more effective educational technologies and practices based on the correlation of teaching with the student’s profile and his/her individual Learning Style. This article discusses the topic of relevance of personalized e-learning. It describes Learning Styles and looks at the Felder– Silverman model in more detail. The article contains the results of student surveys on the basis of which the interrelation between the Index of Learning Styles and academic performance is analysed. The relation between performance and learning styles according to the Felder-Silverman Learning Style Model is shown: in some specialties, students with sequential learning style have higher academic performance than students with global learning style, as well as students with mild learning style preferences on the Activist/Reflector dimension.

EFL LEARNERS’ LEARNING STYLE PREFERENCES IN RELATION TO THEIR ENGLISH PROFICIENCY

2019 ◽  
Vol 4 (2) ◽  
pp. 189
Author(s):  
Siti Saleha Manan ◽  
Lelly Suhartini ◽  
Muhammad Khusnun Muhsin
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
English Proficiency ◽  
Linear Regression Analysis ◽  
English Department ◽  
Learning Style Preferences ◽  
Perceptual Learning Style ◽  
Level Of Significance ◽  
Style Preferences ◽  
Learning Style Preference

The aim of this study was to find out students’ learning style preference and its correlation with their English Proficiency. The research questions were as follows (1) what are the most preferred learning styles of EFL Learner at English Department? (2) Is there any significant correlation between students’ learning style preferences and their English Proficiency? The study used Correlation design. The number of sample in this study was 35 students from English department of Halu Oleo University in the academic year who had followed TOEFL test. The data were collected using questionnaire which adopted   Perceptual   Learning-Style   Preference   Questionnaire   (PLSPQ), developed by John Reid (1987) consisting of 30 statements and student’s TOEFL  score.  The  data  of  this  study  were  analyzed  through  descriptive statistic and Linear Regression Analysis. The results showed that (1) major learning style were tactile learning style and kinesthetic learning style (2) The coefficient correlation was 0.070 which was greater than 0.05 (level of significance).  It  means  that  there  was  no  correlation  between  students’ learning style and their English proficiency.Keywords: Student’s Learning Style, English Proficiency.

Learning Style Preferences of Undergraduate Nursing Students: A Systematic Review

2019 ◽  
Vol 21 (1) ◽  
Author(s):  
Tonderai Washington Shumba ◽  
Scholastika Ndatinda Iipinge
Keyword(s):  
Systematic Review ◽  
Academic Performance ◽  
Academic Success ◽  
Nursing Students ◽  
Learning Styles ◽  
Learning Style ◽  
Undergraduate Nursing Students ◽  
Learning Style Preferences ◽  
Undergraduate Nursing ◽  
Style Preferences

This study sought to synthesise evidence from published literature on the various learning style preferences of undergraduate nursing students and to determine the extent they can play in promoting academic success in nursing education of Namibia. A comprehensive literature search was conducted on electronic databases as a part of the systematic review. Although, kinaesthetic, visual and auditory learning styles were found to be the most dominant learning style preferences, most studies (nine) indicated that undergraduate nursing students have varied learning styles. Studies investigating associations of certain demographic variables with the learning preferences indicated no significant association. On the other hand, three studies investigating association between learning styles and academic performance found a significant association. Three studies concluded that indeed learning styles change over time and with academic levels. The more nurse educators in Namibia are aware of their learning styles and those of their students, the greater the potential for increased academic performance.

scholarly journals INSIGHTS INTO LEARNING STYLES PREFERENCE OF ENGINEERING UNDERGRADUATES: IMPLICATIONS FOR TEACHING AND LEARNING

2022 ◽  
Vol 7 (1) ◽  
pp. 390-409
Author(s):  
Nadia Nur Afiqah Ismail ◽  
Tina Abdullah ◽  
Abdul Halim Abdul Raof
Keyword(s):  
Learning Styles ◽  
Language Learning ◽  
Engineering Education ◽  
Learning Style ◽  
Teaching And Learning ◽  
English Language ◽  
Learning Style Preferences ◽  
Style Preferences ◽  
Learning Style Preference ◽  
Engineering Undergraduates

Background and Purpose: Education at higher institutions prepares graduates for the real world. To develop and maintain quality, the focus must not only be on what institutions can offer but also on the learning needs and styles of learners. Despite many studies on engineering learners’ learning styles, limited research has been conducted to compare the learning styles of Engineering and Engineering Education learners. This study was conducted to ascertain the learning style preferences of first-year undergraduates from both groups in a science and technology-driven university in Malaysia.   Methodology: This descriptive study consisted of 40 Engineering and 40 Engineering Education learners who attended an English language course at the university. Perceptual Learning Style Preference Questionnaire was adopted as the survey instrument. The data were analysed using self-scoring sheet and Statistical Package for the Social Sciences.   Findings: While both groups chose Kinaesthetic as a major learning style preference, the Engineering Education learners also chose Group, Tactile, and Auditory learning styles as their other major preferences. Both groups chose Visual and Individual as their minor preferences.   Contributions: The findings extend research demonstrating the significant role of specific disciplines in Engineering to determine the learning style preferences of learners. The findings also provide useful insights that suggest implications for practice and policy.   Keywords: Engineering, engineering education, English language, learning styles, teaching and learning.   Cite as: Ismail, N. N. A., Abdullah, T., & Abdul Raof, A. H. (2022). Insights into learning styles preference of engineering undergraduates: Implications for teaching and learning.  Journal of Nusantara Studies, 7(1) 390-409. http://dx.doi.org/10.24200/jonus.vol7iss1pp390-409

Changes in Learning-Style Preferences: A Reply to Ruble and Stout

1992 ◽  
Vol 70 (3_suppl) ◽  
pp. 1072-1074
Author(s):  
Marshall A. Geiger ◽  
Jeffrey K. Pinto
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Learning Style Preferences ◽  
Learning Styles Inventory ◽  
Style Preferences ◽  
Over Time

This note is a reply to Ruble and Stout's 1992 critique of our 1991 study of changes in learning style over time. While some of their comments have merit, the remaining conclusions are that the dimension scores on the Learning Styles Inventory exhibit considerable stability over time and should be analyzed when assessing changes in learning style.

Survey of Students' Perception and the Influence of Learning Style Preferences on Learner's Intention to Use 3D Visualization Instruction

2016 ◽  
pp. 297-316
Author(s):  
Yu-Hsin Hung ◽  
Ray I. Chang ◽  
Chun Fu Lin
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Undergraduate Students ◽  
Teaching And Learning ◽  
3D Visualization ◽  
Intention To Use ◽  
Learning Style Preferences ◽  
Computer Science Pedagogy ◽  
Style Preferences ◽  
3D Representations

3D visualization specifically has been widely applied in a broad range of fields, including computer science, pedagogy, and so forth. 3D visualization instruction has become the essential tool that uses computer programs to generate 3D representations of manmade objects. For users, 3D visualization instruction can be manipulated, altered and efficiently communicated to others, and it is efficient for teaching and learning. The aim of this study is investigating students' perception toward 3D visualization instruction, and the influence of learning-style preferences on learners' intentions to use 3D visualization instruction. We are trying to develop the experiment which undergraduate students participated in this study, the purpose of which was to investigate the utilize 3D visualization instruction access to the single learning style and multiple learning styles. Data mining technology was employed in this study to identify multiple learning styles. The result showed that high visual and high sensing learning style has potential of using 3D visualization instruction.

scholarly journals An Exploration of Learning Styles Preferences of Higher Education Students in Pakistan

2017 ◽  
Vol 6 (4) ◽  
pp. 49
Author(s):  
Fakhra Yasmin ◽  
Ahsan Akbar ◽  
Zhang Yan
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Learning Approaches ◽  
Academic Goals ◽  
Education Practice ◽  
The Public ◽  
Learning Style Preferences ◽  
The Social ◽  
Master Level ◽  
Style Preferences

Every individual adopts a unique way to obtain knowledge and this way of acquiring knowledge is known as learning style. These learning approaches of the students can significantly influence their learning outcomes. The aim of this study was to examine the learning styles of Master level students enrolled in Education programs of the public sector universities in southern Punjab region of Pakistan. The study findings reveal that students majoring in Education practice multiple learning styles to accomplish their academic goals endeavors. Moreover, assimilating was the most practiced learning style by the sampled students. The results of ANOVA posits that there are significant differences in learning styles adopted by the students of different universities. The findings of this study provide useful information about the learning style preferences of the social science students and postulate that the choice of university significantly influences the learning style adaptation of students.

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