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.

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

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 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>

scholarly journals Design of Feedback in Interactive Multimedia Language Learning Environments

2012 ◽  
Vol 54 (4) ◽  
Author(s):  
Vehbi Türel
Keyword(s):  
Language Learning ◽  
Learning Environments ◽  
Learning Style ◽  
Language Learners ◽  
Teaching And Learning ◽  
Interactive Multimedia ◽  
Learning Theories ◽  
Learning Style Preferences ◽  
Multimedia Environments ◽  
Style Preferences

In interactive multimedia environments, different digital elements (i. e. video, audio, visuals, text, animations, graphics and glossary) can be combined and delivered on the same digital computer screen (TDM 1997: 151, CCED 1987, Brett 1998: 81, Stenton 1998: 11, Mangiafico 1996: 46). This also enables effectively provision and presentation of feedback in pedagogically more efficient ways, which meets not only the requirement of different teaching and learning theories, but also the needs of language learners who vary in their learning-style preferences (Robinson 1991: 156, Peter 1994: 157f.). This study aims to bring out the pedagogical and design principles that might help us to more effectively design and customise feedback in interactive multimedia language learning environments. While so doing, some examples of thought out and customized computerised feedback from an interactive multimedia language learning environment, which were designed and created by the author of this study and were also used for language learning purposes, will be shown.

Comparing Learning Styles and Technology Acceptance of Two Culturally Different Groups of Students

2014 ◽  
pp. 244-264
Author(s):  
Nauman Saeed ◽  
Suku Sinnappan
Keyword(s):  
Learning Styles ◽  
Technology Acceptance ◽  
Learning Style ◽  
Learning Style Preferences ◽  
Index Of Learning Styles ◽  
Culturally Different ◽  
Australian Cohort ◽  
Acceptance Model ◽  
Style Preferences ◽  
Almost All

This chapter reports on a study that compares the learning styles and technology acceptance of two culturally different groups of students studying at different universities, one in Australia and the other in the US. However, almost all of the students from the Australian cohort were international students from China; thus, this study is essentially a comparison between American and Chinese students. Felder-Solomon's Index of Learning Styles (ILS) was used to collect learning styles data while Davis' Technology Acceptance Model (TAM) was used to examine the acceptance of Twitter, the technology being examined. The study results revealed no statistical significant differences in the learning style preferences of the two groups suggesting that culture did not play a significant role in defining their learning habits. However, culture was a significant factor in the acceptance of technology, in this case Twitter.

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

2019 ◽  
pp. 349-368
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 Students’ Learning Style Preferences of English at Universitas Pamulang

2016 ◽  
Vol 2 (1) ◽  
Author(s):  
Hilma Safitri
Keyword(s):  
Learning Styles ◽  
Student Learning ◽  
Learning Style ◽  
Teaching And Learning ◽  
Good Effect ◽  
Learning Style Preferences ◽  
Student Learning Style ◽  
The Individual ◽  
Style Preferences ◽  
English Students

To have insight of the students learning style is vital since it can facilitate teacher to teach and decide what methods as well as activities are appropriate for the students, for instance,  in learning English. For the students in the future, it is hoped that they can recognize their own learning style preferences in order to be able to learn successfuly, because the students themselves may have no ideas of learning styles they prefer. They just imitate what their friends do, and the teacher does not understand what the students are like and what they prefer in learning. As the consequence, it will not result in any good effect for the improvement for their successful teaching and learning. Hence, both teacher and students, particularly the teachers need to know the students’ characteristics and their learning style preferances. This study adapted the Style Analysis Survey (SAS) model proposed by Oxford (1995) concerning the the student learning style preferences. The samples of this quantitative study involved 60 students who consisted of 30 of non-English and 30 English students at UNPAM. The data taken from quetionnaire were analyzed by using SPSS into descriptive statistic. It was found that the student learning style preferences of both non-English and English students mostly fell on the Social and affective. Meanwhile, for the individual learning style preferences of each group of students are  Physiological and Cognitive executive (II) in which non-English students will remember things better if they discuss them, and English students prefer realism instead of new, unstested ideas.

scholarly journals Experiential Learning Styles in the Age of a Virtual Surrogate

2015 ◽  
Vol 9 (3) ◽  
pp. 93 ◽  
Author(s):  
Rui Wang ◽  
Sidney Newton ◽  
Russell Lowe
Keyword(s):  
Virtual Reality ◽  
Learning Styles ◽  
Learning Style ◽  
Teaching And Learning ◽  
Learning Style Preferences ◽  
Experiential Mode ◽  
Technology Changes ◽  
Learning Behaviours ◽  
Style Preferences ◽  
The Impact

There is a long-held sense in general that the increasing use of computers and digital technology changes how a user experiences and learns about the world, not always for the better. This paper reports on a longitudinal study of 245 architecture and construction students over a two year period which examines the impact that virtual reality technologies have on the learning style preferences of students. A series of controlled experiments tests for the impact that increasing exposure to a proprietary virtual reality system has on the mode of learning and learning style preferences of individuals and particular cohorts. The results confirm that when virtual reality applications are used in teaching and learning, the learning behaviours will favour a more concrete experiential mode of learning and a preference for the Accommodator learning style. However, the results also demonstrate, consistently and for the first time, individual students do not privilege any particular mode of learning or learning style preference to any significant extent but rather engage in all modes and represent all learning styles. Novel visualisation techniques are introduced to examine and discuss this contrast.

scholarly journals Learning style preferences of nursing students at two universities in Iran and Malaysia

2014 ◽  
Vol 11 ◽  
pp. 30 ◽  
Author(s):  
Abdolghani Abdollahimohammad ◽  
Rogayah Ja’afar
Keyword(s):  
Nursing Students ◽  
Learning Styles ◽  
Learning Style ◽  
Learning Style Preferences ◽  
Middle Range ◽  
Significant Difference ◽  
Analytic Learning ◽  
Optimum Response ◽  
Regular Class Time ◽  
Style Preferences

Purpose: Learning style preferences vary within the nursing field and there is no consensus on a predominant learning style preference in nursing students. The current study compared the learning style preferences of nursing students at two universities in Iran and Malaysia. Methods: A purposive sampling method was used to collect data from the two study populations. Data were collected using the Learning Style Scale (LSS), which is a valid and reliable inventory. The LSS consists of 22 items with five subscales including perceptive, solitary, analytic, imaginative, and competitive. The questionnaires were distributed at the end of the academic year during regular class time for optimum response. The Mann-Whitney U-test was used to compare the learning style preferences between the two study populations. Results: A significant difference was found in perceptive, solitary, and analytic learning styles between two groups of nursing students. However, there was no significant difference in imaginative and competitive learning styles between the two groups. Most of the students were in the middle range of the learning styles. Conclusion: There were similarities and differences in learning style preferences between Zabol Medical Sciences University (ZBMU) and University Sains Malaysia (USM) nursing students. The USM nursing students were more sociable and analytic learners, whereas the ZBMU nursing students were more solitary and perceptive learners.

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