Felder-Silverman Learning Style Model Research Articles

Prediction of Learner’s Profile based on Learning Styles in Adaptive E-learning System

The major requirement of present e-learning system is to provide a personalized interface with adaptiveness. This is possible to provide by analyzing the learning behaviors of the learners in the e-learning portal through Web Usage Mining (WUM). In this paper, a method is proposed where the learning behavior of the learner is captured using web logs and the learning styles are categorized according to Felder-Silverman Learning Style Model (FSLSM). Each category of FSLSM learner is provided with the respective content and interface that is required for the learner to learn. Fuzzy C Means (FCM) algorithm is used to cluster the captured data into FSLSM categories. Gravitational Search based Back Propagation Neural Network (GSBPNN) algorithm is used to predict the learning styles of the new learner. This algorithm is a modification of basic Back Propagation Neural Network (BPNN) algorithm that calculates the weights using Gravitation Search Algorithm (GSA). The algorithm is validated on the captured data and compared using various metrics with the basic BPNN algorithm. The result shows that the performance of GSBPNN algorithm is better than BPNN. Based on the identified learning style, the adaptive contents and interface can be provided to the learner.

The Learning Preferences among Nursing Students in the King Saud University in Saudi Arabia: A Cross-Sectional Survey

Objective The present study aimed to identify the most common learning preferences among the nursing students in Saudi Arabia and to investigate the associations of certain demographic variables with the learning preferences. Methods All the undergraduate nursing students in the nursing college were requested to participate in this descriptive cross-sectional study. An Arabic version of the Felder-Silverman learning style model (FSLSM) questionnaire was used to examine the learning preferences among undergraduate nursing students. Results A total of 56 (43%) completed questionnaires were included in the final analysis. Results of the present study indicate that the most common learning preferences among the nursing students were visual (67.9%), followed by active (50%) and sequential (37.5%) learning preferences. The verbal style was the least common learning preference (3.6%) among the nursing students. There was no association between gender and learning preferences (p > .05). Conclusion The present study concluded that the visual, active, and sequential styles are the commonest learning preferences among the nursing students. The nursing educators should emphasize the use of this information in their teaching methods to improve learning skills among the nursing students.

Index of mathematics learning activities suitability to learners

The paper aims to present an original probabilistic method to creating personalised learning activities and units. The method is based on evaluating learning components suitability to students needs according to their learning styles. In the paper, Felder–Silverman Learning Styles Model and Inquiry Based Learning method suitable to learn Math are used. Expert evaluation method based on Trapezoidal Fuzzy Numbers theory is applied in the research to obtain numerical values of suitability of IBL activities to students learning styles. Personalised learning units should consist of learning components (i.e. learning activities) that are optimal (i.e. the most suitable) to particular students according to their learning styles. Optimal means having the highest suitability index. In the paper, this original probabilistic method is applied to establish probabilistic suitability of IBL activities to particular students.

A hybrid approach for supporting adaptivity in e-learning environments

Purpose The purpose of this paper is to identify a framework to support adaptivity in e-learning environments. The framework reflects a novel hybrid approach incorporating the concept of the event-condition-action (ECA) model and intelligent agents. Moreover, a system prototype is developed reflecting the hybrid approach to supporting adaptivity in any given learning management system based on learners’ learning styles. Design/methodology/approach This paper offers a brief review of current frameworks and systems to support adaptivity in e-learning environments. A framework to support adaptivity is designed and discussed, reflecting the hybrid approach in detail. A system prototype is developed incorporating different adaptive features based on the Felder-Silverman learning styles model. Finally, the prototype is implemented in Moodle. Findings The system prototype supports real-time adaptivity in any given learning management system based on learners’ learning styles. It can deal with any type of content provided by course designers and instructors in the learning management system. Moreover, it can support adaptivity at both course and learner levels. Originality/value To the best of the authors’ knowledge, no previous work has been done incorporating the concept of the ECA model and intelligent agents as hybrid architecture to support adaptivity in e-learning environments. The system prototype has wider applicability and can be adapted to support different types of adaptivity.

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.

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.

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.

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.

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 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.

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.

Using Q-Learning for Recommend Learning Object on e-Learning System

This paper discusses the software development used for theselection of instructional materials based on an appropriatelearning style. Q-Learning is used to estimate state-actionlearners using Q-Learning. It is conducted by calculating thevalue of the action done to reward students in selectingteaching materials, so the pattern matches are found. Thisstudy uses the Felder Silverman Learning Style Model(FSLSM). Tests showed that the system is able to perform theselection (action) of instructional materials for teachingmaterials that match learning styles. Q-Learning Lessons canbe applied to optimize the selection of instructional materialswith minimal effort

Using Q-Learning for Recommend Learning Object on e-Learning System

This paper discusses the software development used for theselection of instructional materials based on an appropriatelearning style. Q-Learning is used to estimate state-actionlearners using Q-Learning. It is conducted by calculating thevalue of the action done to reward students in selectingteaching materials, so the pattern matches are found. Thisstudy uses the Felder Silverman Learning Style Model(FSLSM). Tests showed that the system is able to perform theselection (action) of instructional materials for teachingmaterials that match learning styles. Q-Learning Lessons canbe applied to optimize the selection of instructional materialswith minimal effort

Learning Style Dimensions and Professional Characteristics of Academic Librarians

Do librarians with different characteristics, such as type of work responsibilities or age, have different learning styles? The authors analyzed results from over 1,500 responses to a version of the Index of Learning Styles (ILS) questionnaire based on the Felder-Silverman Learning Styles model. This model consists of eight dimensions paired on four scales: Active/Reflective; Sensing/Intuitive; Visual/Verbal; and Sequential/Global. In addition to their scores on the ILS questionnaire, respondents were also asked about demographic and professional characteristics. Statistically significant differences in learning style scores were found to exist between librarians with different types of position responsibilities. In particular, for three out of four scales, catalogers have statistically different learning styles than other librarian groups. Recognition of different learning styles and thoughtful integration of appropriate teaching styles may improve workplace interpersonal communication, enhance professional development and staff training, and strengthen teaching.

Using Learning Styles to Enhance Learning Management Systems

Learning management systems (LMSs) are becoming increasingly popular at many educational institutions such as universities. However, they provide same content for all learners in a given course. Educational theory suggests that learners have different styles of learning. Ideally, the content must be arranged to match each learner’s learning style. In this study, we propose a framework for adaptive LMSs that can tailor course content to the individual learner’s learning style. We estimate the learning style of the student using two methods: a questionnaire and a rule-based method that utilizes the learner’s activity within the LMS. The Felder–Silverman learning style model served as the basis for our system implementation. We explain the operational aspects of our framework and present the findings in applying the framework to a course conducted in Moodle LMS.International Journal on Advances in ICT for Emerging Regions 2014 07 (02)DOI: http://dx.doi.org/10.4038/icter.v7i2.7153

An Empirical Investigation of the Relationships between Learning Styles based on an Arabic version of the Felder-Silverman Model

Learning styles vary according to the individual and this diversity is fundamental in terms of teaching as curricula must respond adaptively to the various learning styles of pupils. This study conducts an analysis of an Arabic form of the Index of Learning Styles (ILS), a 44-item questionnaire designed to determine learning styles using the Felder-Silverman learning style model. This study focuses on the interpretation of data derived from the Arabic form of the Index of Learning Styles (ILS) to establish correlations between the learning styles of 1024 female students drawn from two specific departments at the King Abdul- Aziz University in Saudi Arabia. The findings, generated by Multiple Correspondence Analysis and cross-validated by correlation analysis, demonstrate a definite link between certain learning styles from opposing dimensions that are considered to be contradictory within the same dimension of learning. The validity and reliability of the Arabic scale was established and compared to the examples reported in the literature. Findings show comparable reliability and factor analysis supports the interdependencies between dimensions and perhaps the constructs they intend to assess. The results of this paper have implications for the design of e- learning tools, materials and sessions in order to adapt to the relationships between learning styles and have a positive impact on the learners themselves and their learning experience

Fuzzy-logic based learning style prediction in e-learning using web interface information

The e-learners’ excellence can be improved by recommending suitable e-contents available in e-learning servers that are based on investigating their learning styles. The learning styles had to be predicted carefully, because the psychological balance is variable in nature and the e-learners are diversified based on the learning patterns, environment, time and their mood. Moreover, the knowledge about the learners used for learning style prediction is uncertain in nature. This paper identifies Felder–Silverman learning style model as a suitable model for learning style prediction, especially in web environments and proposes to use Fuzzy rules to handle the uncertainty in the learning style predictions. The evaluations have used the Gaussian membership function based fuzzy logic for 120 students and tested for learning of C programming language and it has been observed that the proposed model improved the accuracy in prediction significantly.

Learning Style Classification Based on Student's Behavior in Moodle Learning Management System

In learning field, each student has his own learning style that affects his way of get, process, understand and percept information. Determining the learning style of students enhances the performance of learning process. Two methods are commonly used to acquire student's learning style: static by questionnaire and dynamic by tracing student's navigation on e-learning environment. In this paper, a new approach to classify students dynamically depending on their learning style was proposed. This approach was experimented on 35 students for Data Structure online course created using Moodle. By extracting students' behavior, data from Moodle log, the learning style for each student was identified according to Felder and Silverman model. Also, learning style based on the behavior have been compared with a quiz results conducted at the end of the course. Receiver operating characteristic (ROC) curve have been used to evaluate the quality of classification results comparing with quiz results. Good results with average accuracy of 76% are achieved. Students' data have been divided into four training and testing sets with different splitting ratio. Different testing accuracy values are obtained for the different ratios using each dimension of Felder-Silverman learning style model (FSLSM).

Felder-Silverman Learning Style Model and the Relationship With Academic Performance

The gap between organizational demands on employee competence and actual employee competence gives new challenges to the education industry to continually increase the readiness of its graduates. Teaching models are developed to produce graduates that are in accordance to the demands of the global competition. However in practice, educational staff often use the same model to all of their students (Blau, 2012), setting aside the importance of congruent concept in an effective pedagogical process (Akin-Little & Little, 2009). The current study is aimed to test the learning style of university engineering students based on the Felder-Silverman model and its role on academic performance. Results showed that engineering students in this study have the following learning style preferences: Active–Sensing–Visual–Global, which can be used as a base to design teaching methods, which will hopefully be able to provide better support to students‟ academic performance. Furthermore, a significant relationship between learning style for Active–Reflective dimension and academic performance was found, as indicated by their GPA.