Diverse Approaches to the Diversity of Learning Styles in Teacher Education

The goal of this paper is to present the findings of a new research project on teacher candidates' learning style preferences and the implications thereof for their teaching styles. The researchers utilized two different learning‐style assessment instruments based on Dunn and Dunn's learning style model—one paper and pencil and one online learning style assessment instrument to identify course participants' learning styles. Within the same institution of higher education, the learning style concept was introduced and operationalized in two different ways. Using a combination of quantitative and qualitative approaches, teacher candidates' individual and group learning style profiles and their reflections on their own learning and teaching styles are discussed.     

混合学习环境下学习风格挖掘及群体差异研究

利用所罗门学习风格量表显式获取用户学习风格,并运用K-means聚类算法挖掘不同风格学习者的线上学习行为特征,依据精确度计算结果不断调整Felder-Silverman学习风格模型对应的线上学习行为属性分类,并最终构建学习风格挖掘模型。结果表明,利用该模型来预测学习者的学习风格具有一定有效性。对不同类别学习风格者的学习特点以及倾向进行差异分析,有利于教师与学生有的放矢地调整教学与学习策略。     

Problems and Inconsistencies with Kolb's Learning Styles

Kolb's Learning Style Inventory (LSI) (1984) is frequently used within many areas of study and research as a method of assigning students to a given learning style. However, this paper argues that there are substantial problems with the theoretical foundations of his work. Anomalies are noted with the claimed relationship with Jung's styles (1977) and with Kolb's use of 'possibility processing' (Tyler, 1978). It is argued that these anomalies make it impossible for firm conclusions about the nature of Kolb's learning style to be made. Implications for the use of Kolb's learning styles are presented.     

Using Learning Style Instruments to Enhance Student Learning

The emergence of numerous learning style models over the past 25 years has brought increasing attention to the idea that students learn in diverse ways and that one approach to teaching does not work for every student or even most students. We have reviewed five learning style instruments (the Kolb Learning Style Indicator, the Gregorc Style Delineator, the Felder–Silverman Index of Learning Styles, the VARK Questionnaire, and the Dunn and Dunn Productivity Environmental Preference Survey) in this article in order to describe the learning style modes or dimensions measured in the instruments; find the common measures and the differences; report on research on instrument validity, reliability, and possible improvement in student performance; suggest classroom activities that work with the different student learning styles; and recommend selection of models under several conditions. We also review one additional learning style instrument, the Revised Approaches to Studying Inventory, as a complementary approach to using one or more of the first five learning style instruments.     

Course Format and Student Learning Styles: A Comparison of Political Science Courses

ABSTRACT

This study investigates whether differences in learning styles exist between students in online and face-to-face (FTF) sections of political science courses taught by three instructors. Some studies suggest that student preferences regarding online or FTF formats are influenced by their preferred modes of learning. Independent learners, for example, may prefer online courses since they provide individualistic opportunities to study outside of the traditional classroom. This study uses original survey data to assign students one of six learning styles in order to assess whether independent learners are more common in online courses. Our analysis finds no significant differences in independent learners when comparing the two formats. This finding runs counter to studies that argue that independent learners tend to prefer online courses. In fact, the only learning style where we observe a meaningful difference among online and FTF formats is among dependent learners. Contrary to expectation, students enrolled in online versions demonstrated a greater tendency toward dependent modes of learning. Further survey responses suggest that student lifestyle drove course format selection rather than learning style. These findings have important implications for universities that increasingly turn to online courses to address decreasing enrollments and attempt to remedy the high attrition rates associated with those courses.     

学习风格对在线学习交互程度影响的实验研究

交互是在线学习的灵魂,在线学习交互程度包含交互频度和交互深度两个方面。学习风格的差异是否会影响学习者的在线学习交互程度及其学习结果,已经成为在线学习交互研究领域的热点问题。本研究首先通过理论分析提出假设:学习风格会影响在线学习交互程度及学习效果,然后通过实验研究验证假设。研究根据Kolb的学习风格分类模型对被试对象进行分类,并使用QQ群作为交互工具,对一段时间内的教学实验数据进行分析。参与交互的频度以学习者在QQ群中的发言数量(条数)来考察,并使用内容分析法将学习者的发言内容划分为简单分析类、分享资源类、探讨问题类以及情感交流类等四种类型;以学习者发布探讨问题类发言的数量来衡量其参与交互的深度,并运用方差分析法对实验结果进行验证。本研究得出以下结论:学习风格直接影响在线学习交互的频度和深度;学习风格对学习绩效并没有造成直接的影响;交互的频度对学习绩效的影响也不显著,交互深度对学习绩效的影响非常明显。基于此结论,参与在线学习的教师应该关注学习者的学习风格差异,改变交互方式和交互策略,从而改进在线学习者的交互绩效。     

Learning style,sense of community and learning effectiveness in hybrid learning environment

The purpose of this study is to investigate how hybrid learning instruction affects undergraduate students' learning outcome, satisfaction and sense of community. The other aim of the present study is to examine the relationship between students' learning style and learning conditions in mixed online and face-to-face courses. A quasi-experimental design was used and 140 sophomores were recruited in this study. Students' learning outcomes, satisfaction, sense of community and learning styles were measured. Results showed that students in a hybrid course had significantly higher learning scores and satisfaction than did students of the face-to-face courses. The result also indicated that students of hybrid learning classrooms felt a stronger sense of community than did students in a traditional classroom setting. Analysis of learning style indicated that learning style had significant effect on learning outcome in the study group. Accommodator learners had higher e-learning effectiveness than other style learners. Possible reasons of results were discussed.     

Getting old and heading south: the academic success of Southerner learners in design cohorts

This paper investigates the relationship between learning style, as determined by Kolb's Learning Style Inventory, age and one measure of academic performance in design assignments for two cohorts of first‐ and third‐year architecture students. The paper focuses on the results of a cross‐curriculum learning style survey conducted as part of a project aimed at resolving the learning difficulties of students collaborating in multi‐disciplinary and multi‐cultural team assignments. The research was conducted to determine how learning style differences in heterogeneous teams might be addressed through pedagogy. In light of evidence in student cohorts of learning style changes towards the learning styles of design teachers as students progress through their studies, this paper demonstrates how these changes reflect a statistically significant relationship between learning styles and academic performance in design assignments.     

‘Learning Style’: frameworks and instruments

In both education and training an important aspect of the design, development and delivery of learning is the role of individual differences between learners in terms of their ‘learning styles’. One may identify four broad categories of what have been termed ‘learning style’: (i) ‘cognitive personality elements’ (e.g. Witkin et al. 1977; Riding, 1991); (ii) ‘information‐processing style’ (e.g. Kolb, 1984; Honey & Mumford, 1992); (iii) ‘approaches to studying’ (e.g. Entwistle & Tait, 1994); (iv) ‘instructional preferences’ (e.g. Riechmann & Grasha, 1974). A study of 245 university undergraduates in business studies aimed to: (i) describe the range of individual differences present within the sample; (ii) investigate the relationship between learners’ cognitive styles, learning styles, approaches to studying and learning preferences; (iii) consider the implications of ‘learning style’ for teaching and learning in higher education. The present study suggested some overlap between the dimensions measured by the Learning Styles Questionnaire (Honey & Mumford, 1986; 1992) and the Revised Approaches to Studying Inventory (Entwistle & Tait, 1994). No statistically significant correlations were found between cognitive style, as measured by the Cognitive Styles Analysis (Riding, 1991) and any of the other ‘style’ constructs used. Further research is required to investigate these relationships, as is a large‐scale factor analytical study of the Honey and Mumford and Kolb instruments. The notions of whole brain functioning, integra‐tive approaches to studying and degree of learning activity are discussed.     

The Determinants of Students' Perceived Learning Outcomes and Satisfaction in University Online Education: An Empirical Investigation*

In this study, structural equation modeling is applied to examine the determinants of students' satisfaction and their perceived learning outcomes in the context of university online courses. Independent variables included in the study are course structure, instructor feedback, self‐motivation, learning style, interaction, and instructor facilitation as potential determinants of online learning. A total of 397 valid unduplicated responses from students who have completed at least one online course at a university in the Midwest were used to examine the structural model. The results indicated that all of the antecedent variables significantly affect students' satisfaction. Of the six antecedent variables hypothesized to affect the perceived learning outcomes, only instructor feedback and learning style are significant. The structural model results also reveal that user satisfaction is a significant predictor of learning outcomes. The findings suggest online education can be a superior mode of instruction if it is targeted to learners with specific learning styles (visual and read/write learning styles) and with timely, meaningful instructor feedback of various types.     

A Preliminary Investigation into the Adaptive Learning Styles of Business Students

An exploration of the preferred learning styles for over 1,100 business students has yielded an intriguing result. While many previous studies have examined the learning styles in different majors at different universities, these studies have been focused on describing the typical student for that major. This study demonstrates that the preferred learning styles of students may depend more on the course than the major, indicating that students may have adaptive learning styles. Rather than having an innate, consistent preferred learning style, business students adapt their preferred learning style to the subject of the course. Further research is necessary to confirm these exploratory findings.     

Learning Environments and Learning Styles: Non-traditional Student Enrollment and Success in an Internet-based Versus a Lecture-based Computer Science Course

Colleges and universities are increasingly using information technologies to enhance the learning environment. Many educational institutions offer Internet-based on-line courses in an effort to meet the educational needs of students. The primary goal of this research was to determine if there is a relationship between students' preferred learning environment (i.e. face-to-face or on-line) and their learning style. The secondary goal was to determine if there were any differences in the academic success of students in the face-to-face versus on-line sections. Participants were adult (ages 22+ years), non-traditional computer science students given the option to take a face-to-face lecture-based or an on-line Internet-based computer science course. Results revealed that computer science students in the face-to-face learning environment were more likely to have the Assimilator learning-style, whereas computer science students in the on-line Internet-based learning environment were more likely to have the Converger learning-style. Student academic success did not reliably differ as a function of learning environment selection. Implications of these results are discussed in terms of learning style characteristics of computer science students, learning styles and gender differences and implications of student academic success in on-line vs face-to-face environments. This revised version was published online in July 2006 with corrections to the Cover Date.     

Evaluating Change and Stability in Learning Style Scores: a methodological concern

Learning styles are purported to be relatively stable characteristics with some change or development expected. Some studies using Kolb's Learning Style Inventory (LSI) have reported significant positive test‐retest correlations of LSI scores or nonsignificant repeated‐measures ANOVAs and concluded that learning styles are stable. This study examined stability and change on Kolb's revised Learning Style Inventory (LSI‐1985) using 152 participants at two points in time separated by about 10 weeks. A variety of statistics were used to evaluate stability and change in LSI‐1985 scores for the four subscales and two dimensions and the four learning styles. The use of test‐retest correlations, differences between means and other methods emphasising group effects were criticised. It was recommended that researchers also analyse and report the stability and change of style categories directly, not just score changes. These comments are also applicable to other learning style measures such as the Learning Style Questionnaire.     

英语学习者的感知学习风格和词汇策略研究

本文根据Reid的感知学习风格和Schmitt的词汇学习策略分类,通过问卷调查方式,调查了128位高职英语学习者的感知学习风格倾向和词汇学习策略运用特点及两者的相关性。研究结果表明:该群体最倾向于独立和视觉风格而最不倾向于合作型学习风格;最常用记忆、认知策略而少用元认知和社会策略;感知学习风格影响学习策略的选择,风格与策略多处显著相关。     

Gender differences in learning styles: A narrative review and quantitative meta-analysis

This article reviews research on gender and learning styles of students, 18 and older, conducted after 1980. Curry's onion model (1983) is used to classify definitions of learning styles and to reconstruct the theoretical frameworks used. The extent to which learning style is considered stable or variable in different learning contexts determines its position in the model. Most studies used theoretical frameworks that belonged in the middle or outer layers of the model. This location indicates the strong influence of learning context on women's and men's learning styles. While there were differences between learning styles, research designs rarely included learning contexts.In addition to the narrative review, we performed a quantitative meta-analysis on two instruments (Kolb's Learning Style Inventory and Entwistle's Approaches to Studying Inventory) to determine the direction and magnitude of gender differences in various samples. A search for these two instruments resulted in 26 studies for which the necessary statistics were available. On Kolb's instrument, the results showed that men were more likely than women to prefer the abstract conceptualisation mode of learning. On Entwistle's ASI a difference was found on the affective components of approaches to studying.     

Individual Learning Style and the Learning Style Inventory

According to experience learning theory (ELT) learning is a process. ELT conceives of learning as a four-stage cycle including four learning modes: concrete experience, reflective observation, abstract conceptualization and active experimentation. The learning style inventory (LSI) invented from ELT provides a framework for examining one's approach to learning situations. The aim of the present study was to collect data with the LSI and present: (a) the test-retest reliability coefficients for the different learning modes; (b) the correlation between different learning modes and age; (c) gender differences in the learning modes; and (d) homogeneous groups of students with different learning styles. The results showed highly significant reliability coefficients, non-significant correlations between learning modes and age, gender differences in some learning modes, and a cluster analysis found homogenous groups with different learning styles.     

Learning environment,learning styles and conceptual understanding

In recent years there have been many studies on learners developing conceptions of natural phenomena. However, so far there have been few attempts to investigate how the characteristics of the learners and their environment influence such conceptions. This study began with an attempt to use an instrument developed by McCarthy (1981) to describe learners in Malaysian primary schools. This proved inappropriate as Asian primary classrooms do not provide the same kind of environment as US classrooms. It was decided to develop a learning style checklist to suit the local context and which could be used to describe differences between learners which teachers could appreciate and use. The checklist included four dimensions — perceptual, process, self-confidence and motivation. The validated instrument was used to determine the learning style preferences of primary four pupils in Penang, Malaysia. Later, an analysis was made regarding the influence of learning environment and learning styles on conceptual understanding in the topics of food, respiration and excretion. This study was replicated in the Philippines with the purpose of investigating the relationship between learning styles and achievement in science, where the topics of food, respiration and excretion have been taken up. A number of significant relationships were observed in these two studies. Specializations: science education, primary science, educational research and measurement, educational management.     

Mining students' learning patterns and performance in Web-based instruction: a cognitive style approach

Personalization has been widely used in Web-based instruction (WBI). To deliver effective personalization, there is a need to understand different preferences of each student. Cognitive style has been identified as one of the most pertinent factors that affect students' learning preferences. Therefore, it is essential to investigate how learners with different cognitive styles interact with WBI programs. This paper presents an empirical study, which examines the effects of cognitive styles on students' learning patterns and the effects of learning patterns on their learning performances. Riding's cognitive style analysis was used to identify the students' cognitive styles. Data mining, especially a clustering technique, was used to analyze the results. It was found that field independent students frequently used an alphabetical index whereas field dependent students often chose a hierarchical map. Such learning patterns also have great effects on their performance, especially for field dependent students.     

Identification of learning styles online by observing learners' browsing behaviour through a neural network

Identification of individual learning style is important when developing adaptive educational hypermedia systems. Current systems ask learners to complete questionnaires to identify their learning styles, which might not be appropriate in some contexts. The goal of this research is to identify the learner's learning style by simply observing his/her browsing behaviour without asking the learner to answer any questions or filling out any form. It is implemented through a multi‐layer feed forward neural network (MLFF). Browsing behaviour, in this research, includes three factors, the use of embedded support devices (ESDs), the selection of link types, and the navigation between visited/unvisited nodes. The experiment results showed the proposed model performed well in identifying learning styles. Link type is the dominant factor and Time shift may not be a major factor in the identification of learning styles. Because of the fast execution property of neural networks and identification of learning styles online, it is possible to incorporate learning styles into online adaptive educational web‐based systems.     

Individual Differences in Learning: Cognitive control,cognitive style,and learning style

This paper assesses the value of three learning style tests when used to examine the design of educational materials for teaching computer science at a distance. The paper presents three studies where three different learning styles were used to discriminate preference and performance in different contexts. The studies indicate that the Learning Style Questionnaire and the Group Embedded Figures Test are of little value. However the Cognitive Style Analysis proved useful in discriminating performance on imagery‐rich materials in a simulated learning context. The paper argues that it may be necessary to match the theoretical basis of learning style with the context in which it is used in order to gain useful information. On the whole the studies showed that the value of learning style tests may be limited.