Learning Style Model Research Articles

An Adaptive E-Learning System based on Student's Learning Styles

Personalized e-learning implementation is recognized as one of the most interesting research areas in the distance web-based education. Since the learning style of each learner is different one must fit e-learning with the different needs of learners. This paper presents an approach to integrate learning styles into adaptive e-learning hypermedia. The main objective was to develop a new Adaptive Educational Hypermedia System based on Honey and Mumford learning style model (AEHS-H&M) and assess the effect of adapting educational materials individualized to the student's learning style. To achieve the main objectives, a case study was developed. An experiment between two groups of students was conducted to evaluate the impact on learning achievement. Inferential statistics were applied to make inferences from the sample data to more general conditions was designed to evaluate the new approach of matching learning materials with learning styles and their influence on student's learning achievement. The findings support the use of learning styles as guideline for adaptation into the adaptive e-learning hypermedia systems.

Cataloguers May Tend to Have Learning Styles Different from Other Library Job Responsibilities

Objective – To determine whether relationships exist between academic librarians’ learning styles and their professional work responsibilities. 
 
 Design – Self-selecting survey. 
 
 Setting – Email listservs. 
 
 Subjects – 1579 academic librarians. 
 
 Methods – The authors used the Index of Learning Styles questionnaire, based on the Felder-Silverman Learning Styles model consisting of eight dimensions on four scales: Active/Reflective, Sensing/Intuitive, Visual/Verbal, and Sequential/Global. The multiple choice survey was distributed online to 23 email listservs for academic librarians in 2011, and to 14 additional listservs in 2013 targeting technical services librarians. 1579 responses were received in total, which were analyzed using ANOVA with a Tukey-Kramer post-hoc mean separation, and descriptively using observed frequencies. 
 
 Main Results – In examining the relationship between positions and learning styles, the study revealed there to be five statistically significant p-values when the data were analyzed. Catalogers (n=145) were found to be more reflective learners compared to Administrative (n=321) and Instruction librarians (n=228) at the p = 0.009 level. Administrative, Instruction, and “Other” librarians were found to be more intuitive learners than Catalogers, who are more likely to be sensing learners, at the p = 0.0004 level. Digital librarians (n=40) are more likely to be visual learners and Catalogers more likely to be sequential learners when compared to several other librarian categories, at the p = 0.020 and p = 0.001 levels respectively. 
 
 Conclusions – The authors concluded that there were some statistically significant differences between librarians’ learning styles scores according to job responsibilities. Catalogers were found to have different learning styles than other types of librarians for three out of four scales. Based on these findings, the authors indicate that further research into how librarians’ work responsibilities impact learning styles is justified.

Prilagodljivi računalniški sistem za priporočanje učnih objektov v konstruktivističnem učnem okolju – ALECA

Dandanes se pojavlja vse več učnih sistemov, ki podpirajo aktivno učenje in upoštevajo učenčeve učne lastnosti, značilnosti in aktivnosti. V prispevku predstavljamo zasnovo učnega priporočilnega sistema, ki združuje znanja pedagogike in računalniških priporočilnih algoritmov. Proučujemo, kako združevanje modelov učnih stilov vpliva na izbiro različnih tipov večpredstavnih učnih gradiv. Rezultati kažejo, da študentje za učenje najpogosteje uporabljajo dobro strukturirana učna gradiva, ki vsebujejo barvno diskriminacijo, in da je hemisferični model učnih stilov najpomembnejši odločitveni kriterij. V nadaljevanju opisujemo postopek za reševanje t. i. problema hladnega zagona, s katerim je mogoče izboljšati točnost sistema za priporočanje učnih gradiv v okoljih, kjer o učencih nimamo predhodnih podatkov. Namen prispevka je predstaviti idejno zasnovo prilagodljivega učnega sistema z analizo njegovih predvidenih učinkov na učno prakso.

Increasing teacher candidates' ways of interaction and levels of learning through action research in a blended course

Within the scope of this research, efforts were exerted to increase teacher candidates' interaction ways through action research in a blended teaching profession course in higher education level. Teacher candidates participated in various blended learning activities during a semester-long course, and the problems related to learners' participation in blended learning activities and their interactions during learning process were solved via action decisions. Blended learning activities were developed according to Felder-Silverman Learning Style Model and the lessons learned from previous two pilot studies. In order to conduct the activities; face-to-face and synchronous virtual classroom sessions were combined with asynchronous from discussions and blog. At the beginning of the course, teacher candidates chosed activity sets according to learning style model and throughout a semester-long course, they participated in the activities. Throughout the action research process, the ways of interactions between students, students and instructor, and students and the content were examined as well as the levels of learning accomplished by students during the learning process. This study has revealed indicators pointing to an increase both in students’ interactions and levels of learning during the blended learning process.

Appraisal of Felder - Silverman Learning Style Model with Discrete Data Sets

Objectives: E-learning is adapted to suit the different heterogeneous learners with different leaning styles. Felder and Silverman have given a catalogue of learning styles (FSLS). This paper is indented to analyze the reliability of their recommended learning styles. Method: Personalization is the latest improvement in E-learning system. Personalized E-learning system (PES) is suggested as the next generation of E-learning system. Various factors are analyzed to address the prediction of user's preferences. Learning styles is main deciding factor of personalizing E-leaning system. Most of the personalized models personalizing on learning styles have used FSLS. Previous research works performs the analysis of FSLS with single set of data. In this paper, the model is tested with two distinct of data using Karl's Pearson Coefficient method. To carry out analysis, adaption model is developed with FSLS using JAVA language. Findings: The observation of Appraisal of FSLS model states that it is strongly accepted by one set of data and there is small divergence in another set of data. Applications/Improvement: The study can be expanded with larger set of data and more than two distinct set of data.

Yet another adaptive learning management system based on Felder and Silverman’s learning styles and Mashup

This study designs and implements an adaptive learning management system based on Felder and Silverman’s Learning Style Model and the Mashup technology. In this system, Felder and Silverman’s Learning Style model is used to assess students’ learning styles, in order to provide adaptive learning to leverage learners’ learning preferences. Additionally, this learning system also allows learners to use a Mashup search engine to search for related supplementary learning materials for better learning outcomes. An experiment is conducted to evaluate the effectiveness of the developed adaptive learning management system. A questionnaire is also circulated to collect data for qualitative and quantitative analysis of the usability of the developed system. The results show that the system significantly improves learning outcomes and the usability is also well-accepted by the participants.

Web Log Pre-processing and Analysis for Generation of Learning Profiles in Adaptive E-learning

Adaptive E-learning Systems (AESs) enhance the efficiency of online courses in education by providing personalized contents and user interfaces that changes according to learner’s requirements and usage patterns. This paper presents the approach to generate learning profile of each learner which helps to identify the learning styles and provide Adaptive User Interface which includes adaptive learning components and learning material. The proposed method analyzes the captured web usage data to identify the learning profile of the learners. The learning profiles are identified by an algorithmic approach that is based on the frequency of accessing the materials and the time spent on the various learning components on the portal. The captured log data is pre-processed and converted into standard XML format to generate learners sequence data corresponding to the different sessions and time spent. The learning style model adopted in this approach is Felder-Silverman Learning Style Model (FSLSM). This paper also presents the analysis of learner’s activities, preprocessed XML files and generated sequences.

A survey of personality and learning styles models applied in virtual environments with emphasis on e-learning environments

Today, one of the most important and challenging issues in artificial intelligence is modeling human behavior in virtual environments. Furthermore, studying e-learning environments is in great demand in computer science which requires understanding human behaviors. Thus, considering human behavior factors, such as personality, mood, and emotion, and modeling them in e-learning environments is a challenging issue in artificial intelligence. The purpose of this paper is to review the psychological models of personality used in computer science. In addition, the most important applications of personality models and their direct related topics in learning, i.e. learning style issues in e-learning environments, are presented. The study shows that researchers tend to use models that are simple to implement in virtual world and are as comprehensive as possible to cover all the features of human behavior. Finally, we concluded that models such as the Five Factor Model, the Myers---Briggs Type Indicator personality model, and Felder---Silverman learning styles model have the two most important features, which are simplicity and comprehensiveness. These two features have made these psychology models the most favorable in the virtual world.

Importance of Dynamic Identification of Learning Style on the Efficacy of Adaptive e-Learning Systems

This article presents an ongoing research project on the development of adaptive e-Learning systems in relation to the theory of learning style. Previously, a literature review has been conducted on learning style modeling methods, the Felder-Silverman learning style model (FSLSM), and the main problems encountered with the current traditional adaptive e-learning system (TAES). In this work a proposed intelligent adaptive e-learning system (PIAES) is designed to dynamically detect students’ learning styles, and to inform appropriate learning material design. An experiment was conducted in order to validate the system’s ability to detect students' learning styles and to enhance their academic achievements relative to those of a traditional adaptive e-learning system (TAES). Twenty-three students from the University of Jordan completed the Index of Learning Style (ILS) questionnaire and used the PIAES. The precision in learning style detection was satisfactory. The study also used a quasi-experimental design to validate the effectiveness of the PIAES in enhancing academic achievements relative to those of the TAES. A group of 110 students were recruited from the University of Jordan and randomly assigned to a control (56 students) or experimental group (54 students). Students in the experimental group (PIAES) showed better academic achievement relative to that of students in the control group (TAES). These results show that the proposed adaption strategies presented in the PIAES could improve student achievement because it adapts to each student’s learning style and dynamically tailor learning material based on learner responses.

A Meta-Analysis on the Effect of Instructional Designs based on the Learning Styles Models on Academic Achievement, Attitude and Retention

The purpose of this study is to calculate the effect size, by running a meta-analysis, of the experimental studies carried out in Turkey between 2004 and 2014 that investigate the effect of learning styles on academic achievement, attitude, retention, and to define whether the academic achievement shows a significant difference in terms of learning styles model, experimental design and course type. For this purpose, a metaanalytical review method was employed to combine the outcome of the independent experimental studies. The studies included in this review were collected from CoHE National Thesis Archive (2015), ULAKBIM (2015), Google Academic (2015), ERIC (2015) and EBSCO (2015) databases. As a result of the searching process, 402 studies were assessed according to the inclusion criteria and 30 experimental studies were included in this study. Cohen’s d coefficient was calculated for the effect size in this study. Because there was a high amount of heterogeneity (Q > x2, p < .05) among the effect sizes of the studies, the common effect size was calculated according to the random effect model. As a result of meta-analysis, it was determined that the instructional designs based on the learning styles model had a large effect on the academic achievement (d = 1.029), attitude (d = 1.113) and retention (d = 1.290). Moreover, the academic achievement did not show any significant difference according to learning style model, course type and experimental design.

Automatic detection of learning styles based on dynamic Bayesian network in adaptive e-learning system

A large number of studies attest that learning is facilitated if the teaching strategies are in accordance with the students learning styles (LS), making the learning process more effective and considerably improving student's performances. But, traditional approaches for detection of LS are inefficient. This work determines the current preferences through dynamic Bayesian network that represent the matches between LS and teaching strategies in order to determine how much a given strategy is interesting to a student. The LS theory that supports this approach is the LS model proposed by Felder-Silverman's learning styles model (FSLSM). Our approach gradually and constantly adjusts the student model, taking into account students' performances, student's effort, student's intensity, student's resistance and student's attention. Promising results were obtained from experiments, and some of them are discussed in this paper.

Towards adaptive open learning environments: Evaluating the precision of identifying learning styles by tracking learners’ behaviours

Open learning represents a new form of online learning where courses are provided freely online for large numbers of learners. MOOCs are examples of this form of learning. The authors see an opportunity for personalising open learning environments by adapting to learners' learning styles and providing adaptive support to meet individual learner needs and preferences. Identifying learning styles of learners in open learning environments is crucial to providing adaptive support. Learning styles refer to the manner in which learners receive and perceive information. In the literature, a number of learning style models have been proposed. The Felder and Silverman Learning Styles Model (FSLSM) has been selected as the most appropriate model for open learning. In previous studies two approaches have been used to automatically identify learning styles based on the FSLSM. These approaches are known as the data-driven method and the literature-based method. In the literature, the literature-based method has been shown to be more accurate in identifying learning styles. This method relies on tracking learners' interactions with the provided learning objects based on a set of pre-determined patterns that help in inferring learning styles. The patterns are monitored based on pre-identified threshold values. This paper aims to apply the literature-based method to open learning environments and introduce the optimal patterns and threshold values for identifying learning styles based on the FSLSM. To achieve this aim, a study was conducted whereby a prototype that simulates the open learning environment was developed and piloted on an undergraduate IT course so that learner behaviour could be tracked and data could be collected. Next, different sets of threshold values from the literature were considered along with some updated threshold values considering the context of open learning environments, and the precision of identifying learning styles was calculated. Eighty-three students participated in the study and used the developed prototype. Precision results from different threshold values presented in the literature along with customised threshold values for this study are reported and analysed in this paper. It is shown that threshold values derived from literature and customised to suit open learning environments provide a high level of accuracy in identifying learning styles. The paper presents the first study of its kind in evaluating threshold values and precision in identifying learning styles based on the FSLSM in open learning environments. The results are promising and indicate that the proposed methodology is efficient in detecting learning styles in open learning environments and useful for developing an adaptive framework.

Predictors of performance on the pharmacy curriculum outcomes assessment (PCOA)

ObjectiveOur aim was to evaluate factors that predicted student performance on the pharmacy curriculum outcomes assessment (PCOA) examination. MethodsPCOA was administered to two P2 pharmacy classes in 2013 and 2014. Predictor data was collected during a learning style workshop using the Unified Learning Style Model (ULSM) and through institutional databases. Possible predictors of performance were included in a multiple linear regression. Variables included in the regression were chosen as predictors based upon previous studies in the health sciences literature. ResultsA total of 142 second professional year (P2) students completed the PCOA. Average PCOA scores were 363.5 ± 42.2. Significant predictors in the multiple linear regression included P1 grade point average (GPA) institution, Pharmacy College Admissions Test (PCAT) reading, accommodators (compared to assimilators), and students that did not prefer reading. Increases in P1 GPA, and PCAT reading improved PCOA scores. Students that did not prefer reading as a learning preference, accommodators, and institution on average led to a reduction in PCOA scores. ConclusionsOur pilot study found that P1 GPA, PCAT reading, institution, accommodators, and students that do not prefer reading may predict PCOA performance, which mirrors findings related to summative examinations in health sciences literature.

Learning Styles Among Undergraduate Nursing Students’ in School of Nursing and Midwifery, Tehran University of Medical Sciences, Tehran, Iran

Background: Learning style has been the focus of numerous studies, but it remains complex and affected by many factors. Nursing students should learn large quantities of theoretical content in a short period of time. Aims: The purpose of this study was to determine learning styles among undergraduate nursing students in School of nursing and midwifery, Tehran University of Medical Sciences, Tehran, Iran. Method: Using Institutional based cross-sectional descriptive study and self-administrated structured questionnaire. The questionnaire was divided into two sections including (a) demographic profile, and (b) Kolb’s Learning Style Inventory. Data was collected from 232 systematically selected undergraduate nursing students from School of nursing and Midwifery, Tehran University of Medical Sciences, Tehran, Iran. After coding and cleaning, data was entered and analyzed by SPSS Window version 21. Descriptive statistics was used to see the frequency and percentage of each variable. P-value <0.005 was significance measurement for the result in this study. Results: A total of 232 undergraduate Nursing students on the study; the majority 143(61.6%) were female and 89 (38.4%) of respondents were male. Most of the participants (60.8%) being in the age group of 20-25 years. The most frequency learning style of students was AC (37.5%). In addition, AE (30.17%), RO (19.83%) and CE (12.5%) were in the next order in LS of students. However, there was a relationship between self management and AC of LS (F=8.485; P=0.004).In addition, female students were a high scorer than male students respectively (55.6% and 14.4%). There was no relationship between LS and self study, permanent residence, living, place and the average hours of independent study. Conclusion: Facing the various challenges of the effective learning issue, many researchers attempted to conceptually systematize the learning preferences by constructing explanatory models of learning styles.

Towards better Scrum learning using learning styles

Considerable attention has been paid to teaching Scrum in software engineering education as an academic response to the software industry’s demands. In order to reinforce and strengthen the understanding of Scrum concepts, professors should personalize the learning process, catering for students’ individual learning characteristics. To address this issue, learning styles become effective to understand students’ different ways of learning. In this context, the meshing hypothesis claims that when both teaching and learning styles are aligned, the students’ learning experience is enhanced. However, the literature fails to evidence support for the meshing hypothesis in the context of software engineering education. We aim to corroborate the meshing hypothesis by using teaching strategies matching the Felder–Silverman Learning Style Model in a Scrum course. Based on previous findings, we focused on the processing dimension of the model. To validate our approach, two experiments were conducted in an undergraduate software engineering course in the academic years 2013 and 2014. We provided students with a Scrum class by applying teaching strategies suiting students’ learning style. Test results corroborate that students’ outcomes improved when receiving the strategy that match their learning styles. Our data highlight opportunities for improving software engineering education by considering the students’ learning preferences.

An ontology-based adaptive personalized e-learning system, assisted by software agents on cloud storage

E-learning and online education have made great strides in the recent past. It has moved from a knowledge transfer model to a highly intellect, swift and interactive proposition capable of advanced decision-making abilities. Two challenges have been observed during the exploration of recent developments in e-learning. Firstly, to incorporate e-learning systems effectively in the evolving semantic web environment and secondly, to realize adaptive personalization according to the learner's changing behavior. An ontology-driven system has proposed to implement the Felder-Silverman learning style model in addition to the learning contents, to validate its integration with the semantic web environment. Software agents are employed to monitor the learner's actual learning style and modify them accordingly. The learner's learning style and their modifications are made within the proposed e-learning system. Cloud storage is used as the primary back-end in order to maintain the ontology, databases and other required server resources. To verify the system, comparisons are made between the information presented and adaptive learning styles of the learner along with actions of agents according to learners' behavior. Finally, various conclusions are drawn by exploring the learner's behavior in an adaptive environment for the proposed e-learning system.

A Psychometric Analysis of Reliability and Validity of the Index of Learning Styles (ILS)

Prior literature showed that Felder and Silverman learning styles model (FSLSM) was widely adopted to cater to individual styles of learners whether in traditional or Technology Enhanced Learning (TEL). In order to infer this model, the Index of Learning Styles (ILS) instrument was proposed. This research aims to analyse the soundness of this instrument in an Arabic sample. Data were integrated from different courses and years. A total of 259 engineering students participated voluntarily in the study. The reliability was analysed by applying internal construct reliability, inter-scale correlation, and total item correlation. The construct validity was also considered by running factor analysis. The overall results indicated that the reliability and validity of perception and input dimensions were moderately supported, whereas processing and understanding dimensions showed low internal-construct consistency and their items were weakly loaded in the associated constructs. Generally, the instrument needs further effort to improve its soundness. However, considering the consistency of the produced results of engineering students irrespective of cross-cultural differences, it can be adopted to diagnose learning styles.

Teaching Style and Learning Style Model: An Overview of Grasha

Teaching Style and Learning Style Model: An Overview of Grasha

Learning Style Preferences Among Male and Female ESL Students in Universiti-Sains Malaysia

Individuals preferentially process information in different ways. This includes the varied learning style preference of the individuals in any study program, including English as a Second Language (ESL). However, one of major concerns is, do the ESL students have different preferred way to learn? Past studies have given mixed results including pertaining to Malaysian students. To address this issue, this study sought to identify whether there are differences in learning style preferences between male and female students who undertook ESL courses in the Universiti Sains Malaysia (USM). To achieve the study objective, Felder-Silverman Learning Style Model (FSLSM) was selected to gather data on the respondents’ learning style preference due to its validity, widespread use and suitability to the scope of the study. The responses gathered from FSLSM were tallied and assessed for gender difference in LSP. Results indicated that, there is a strong representation of visual learners from both male and female respondents. On the other hand, the respondents, irrespective of the gender difference, are well-balanced in the dimensions of sensing/intuitive, active/reflective, and sequential/global. In addressing the gender difference, it was found in this study that there is no significant difference between male and female ESL students in their preferred learning The Journal of Educators Online-JEO July 2015 ISSN 1547-500X Vol 13 Number 2 104 104 styles on each of the FSLSM dimension. Thus, this study revealed that, gender does not help differentiate students’ learning preferences. The findings lend support to several past studies on LSP.

Learning and Teaching Style Models in Pedagogical Design of Electronic Educational Environment of the University

Education system of Russia is undergoing significant changes due to the need to overcome the contradictions between the traditional pace of learning and teaching and the ever-increasing flow of new knowledge. The development of e-learning poses new demands on the organization and improvement of the quality of learning and teaching process. The article discusses issues about the pedagogical design of electronic educational environment of the university. The description of the main learning and teaching style models (Gregorc’s Mind Style Model, Kolb’s Experiential Learning Theory, the VARK Model, the Felder-Silverman Learning and Teaching Styles Model), ways of designing individual learning pathways based on the models are represented. The criteria for the design of a pedagogical scenario for each learning and teaching style are defined and described. A scheme of the pedagogical scenario of electronic educational environment based on the Anthony Gregorc’s model is introduced; the results of experimental testing carried out with the students of the Applied Mathematics and Informatics Department to determine the effect of pedagogical scenario on improving the quality of students’ training in university are presented. DOI: 10.5901/mjss.2015.v6n3s7p281