Silverman Learning Style Research Articles

Fuzzy Classification Approach to Select Learning Objects Based on Learning Styles in Intelligent E-Learning Systems

In e-learning systems, even though the automatic detection of learning styles is considered the key element in the adaptation process, it does not represent the main goal of this process at all. Indeed, to accomplish the task of adaptation, it is also necessary to be able to automatically select the learning objects according to the detected styles. The classification techniques are the most used techniques to automatically select the learning objects by processing data derived from learning object metadata. By using these classification techniques, considerable results are obtained via several approaches and consist of mapping the learning objects into different teaching strategies and then mapping these strategies into the identified learning styles. However, these approaches have some limitations related to robustness. Indeed, a common feature of these approaches is that they do not directly map learning object metadata elements to learning style dimensions. Moreover, they do not consider the fuzzy nature of learning objects. Indeed, any learning object can be suitable for different learning styles at varying degrees of suitability. This highlights the need to find a way to remedy this shortcoming. Our work is part of the automatic selection of learning objects. So, we will propose an approach that uses the fuzzy classification technique to select learning objects based on learning styles. In this approach, the metadata of each learning object that complies with the Institute of Electrical and Electronics Engineers (IEEE) standard are stored in a database as an Extensible Markup Language (XML) file. The Fuzzy C Means algorithm is used, on one hand, to assign fuzzy suitability rates to the stored learning objects and, on the other hand, to cluster them into the Felder and Silverman learning styles model categories. The experiment results show the performance of our approach.

Enhancing E-Learning Adaptability with Automated Learning Style Identification and Sentiment Analysis: A Hybrid Deep Learning Approach for Smart Education

In smart education, adaptive e-learning systems personalize the educational process by tailoring it to individual learning styles. Traditionally, identifying these styles relies on learners completing surveys and questionnaires, which can be tedious and may not reflect their true preferences. Additionally, this approach assumes that learning styles are fixed, leading to a cold-start problem when automatically identifying styles based on e-learning platform behaviors. To address these challenges, we propose a novel approach that annotates unlabeled student feedback using multi-layer topic modeling and implements the Felder–Silverman Learning Style Model (FSLSM) to identify learning styles automatically. Our method involves learners answering four FSLSM-based questions upon logging into the e-learning platform and providing personal information like age, gender, and cognitive characteristics, which are weighted using fuzzy logic. We then analyze learners’ behaviors and activities using web usage mining techniques, classifying their learning sequences into specific styles with an advanced deep learning model. Additionally, we analyze textual feedback using latent Dirichlet allocation (LDA) for sentiment analysis to enhance the learning experience further. The experimental results demonstrate that our approach outperforms existing models in accurately detecting learning styles and improves the overall quality of personalized content delivery.

Investigating the effect of learning styles on HyFlex learning towards equity of learning in higher education

PurposeThis paper aims to investigate the effect of learning styles on HyFlex learning towards equity of learning in higher education.Design/methodology/approachA quantitative approach was used, with data collection through a structured online questionnaire. The study participants were undergraduate students (n = 451) studying at various public and private universities in Indonesia. Measurement analysis is used to test the validity of the instrument used. Analysis of structural equations is used to test the relationships between the constructs under study.FindingsSurvey instruments have satisfactory internal validity and consistency. The learning style of students in higher education positively influences the use of HyFlex’s three learning modalities. All three modalities of HyFlex learning positively affect learning equity, especially the asynchronous online modality. However, the synchronous online effect is insignificant. Active/reflective learning styles only affect face-to-face mode but do not significantly affect the two online modalities, synchronous and asynchronous. Some of the learning style dimensions have an indirect effect on equity through three HyFlex learning modalities. Face-to-face and online asynchronous mediate well the indirect relationship between learning style and equity. The impact of gender and higher education status was not shown to strengthen the relationship between learning styles, HyFlex learning modalities and equity.Research limitations/implicationsThis study will provide valuable understanding for lecturers, educators and developers to adapt and develop HyFlex learning strategies based on the positive dimensions of the Felder–Silverman learning style that can support equitable and inclusive learning. The study forms a foundation for researchers to investigate more constructs that could improve HyFlex learning in future studies.Originality/valueThis research is a pioneer in using learning styles to investigate trends in using three HyFlex learning modalities, particularly emphasising modalities that can provide equitable learning.

FSLSM-Based Analysis of Student Performance Information in a Blended Learning Course Using Moodle LMS

Abstract Over the past decades, blended learning using a flipped classroom approach has become an increasingly important part of global learning development and has gradually revolutionized educational environments. However, in the online part, individual differences of learners are often not considered and treated in an equipollent way, neglecting their needs and personal characteristics, which results in a poor quality of the learning service. Thus the need to improve both the content of e-learning systems and their adaptability requires a careful examination of the direct behavior of students in distance learning platforms. Our study is based on a dataset from the FOAD_FSBM e-learning platform involved in the learning of Moroccan university students. This work is motivated by the fact that professors seek to better understand students’ learning styles, which are founded on the Felder and Silverman Learning Style Model (FSLSM) so that they can offer them adaptive and personalized courses. In this article, we first describe the basic environment of this study and the reasons that led us to choose the Moodle e-learning platform. We also present the format of the courses created to conduct our experiments, and its advantage in e-learning. Then, we highlight the development tools that we used to conduct our studies. Finally, we present the results of our statistical analyses in relation to the performance of the students.

Understanding students’ backtracking behaviors in digital textbooks: a data-driven perspective

ABSTRACT The purpose of this study is to explore students’ backtracking patterns in using a digital textbook, reveal the relationship between backtracking behaviors and academic performance as well as learning styles. This study was carried out for 2 semesters on 102 university students and they are required to use a digital textbook system called DITeL to review courseware. Students’ backtracking behaviors are characterized by seven backtracking features extracted from interaction log data and their learning styles are measured by Felder–Silverman learning style model. The results of this study reveal that there is a subgroup of students called backtracker who backtrack more frequently and performed better than the average students. Furthermore, the causal inference analysis reveals that a higher initial ability can directly cause a higher frequency of backtracking, thus affecting the final test score. In addition, the significance analysis reveals no significant correlation between backtracking behavior and learning style. Building upon these experimental findings, we offer several suggestions for the future advancement of digital teaching materials development.

Unlocking teachers’ potential: MOOCLS, a visualization tool for enhancing MOOC teaching

Massive Open Online Courses (MOOCs) are revolutionizing online education and have become a popular teaching platform. However, traditional MOOCs often overlook learners' individual needs and preferences when designing learning materials and activities, resulting in suboptimal learning experiences. To address this issue, this paper proposes an approach to identify learners' preferences for different learning styles by analyzing their traces in MOOC environments. The Felder–Silverman Learning Style Model is adopted as it is one of the most widely used models in technology-enhanced learning. This research focuses on developing a reliable predictive model that can accurately identify learning styles. Based on insights gained from our model implementation, we propose MOOCLS (MOOC Learning Styles), an intuitive visualization tool. MOOCLS can help teachers and instructional designers to gain significant insight into the diversity of learning styles within their MOOCs. This will allow them to design activities and content that better support the learning styles of their learners, which can lead to higher learning engagement, improved performance, and reduction in time to learn.

Personalized Learning in Virtual Learning Environments Using Students’ Behavior Analysis

In recent years, many research studies have focused on personalized e-learning. One of the most crucial parts of any learning environment is having a learning style that focuses on individual learning. In this paper, we propose an approach to personalizing learning resources based on students’ learning styles in a virtual learning environment to enhance their academic performance. Students’ interactions with the learning management system are utilized to analyze learners’ behaviors. The Felder–Silverman Learning Style Model (FSLSM) is used to map students’ interactions with online learning resources to learning style (LS) features. The learning style and demographic features are then utilized for training machine learning models to predict students’ academic performance in each quarter of courses. The most accurate prediction model for each quarter is then used to find learning style features that maximize students’ pass rates. We statistically prove that students whose actual learning style features were close enough to the ones calculated by the approach achieved better grades. To improve students’ academic performance each quarter, we suggest two strategies based on the learning style features calculated by the process.

UNDERSTANDING LEARNING STYLES AMONG MEDICAL STUDENTS AT GOVERNMENT MEDICAL COLLEGE, VISAKHAPATNAM: A CROSSSECTIONAL STUDY

Background: Understanding a student's degree of motivation and learning performance requires detection of learning styles. To accommodate these distinct student learning styles, teachers should also create a variety of learning resources in a variety of mediums. Hence this study was conducted with an objective to understand the learning styles among Medical students. Methodology: The study was conducted in October 2022 among 2nd and 3rd year M.B.B.S students at a medical college in Visakhapatnam. 286 out of 400 students gave their consent and responded. A Felder and Silverman Learning Style Model (FSLSM) Index of Learning Styles (ILS) questionnaire was used to identify student's learning preferences. The mean (SD) age of the partici Results: pants was 20.3± (0.91) years. The majority of the medical students was moderate-strong visual learners (76.3%) and fairly well balanced in the sequential/global dimension (71.7%), active/reective dimension (61.5%) and sensory/intuitive dimension (61.2%) of the Index of Learning Styles (ILS). Conclusion: The ndings of this study can be utilized to advise teachers to all dimensions in a balanced approach while teaching. This further helps in the improvement of the academic performance of the students.

Editorial

Dear Readers, It gives me great pleasure to announce the ninth regular issue of 2022. I would like to thank all the authors for their sound research and the editorial board for their extremely valuable reviews and suggestions for improvement. These contributions, together with the generous support of the consortium members and the recognition from the research community, enable us to run our journal and maintain its quality. Still, I would like to expand our editorial board: If you are a tenured associate professor or above with a good publication record, please apply to join our editorial board. We are also interested in receiving high-quality proposals for special issues on new topics and emerging trends. In this regular issue, I am very pleased to introduce four accepted papers from 18 authors from six different countries. In a research collaboration between Ecuador, Spain and Argentina, Alexandra Corral, Luis E. Sanchez and Leandro Antonelli focus on improving the software development process to overcome the lack of communication regarding an understanding of the discourse domain and to integrate and process excessive information from different sources using intelligent systems and semantic reasoning. Fatemeh Farnaghi-Zadeh, Mohsen Rahmani and Maryam Amiri from Iran propose an improved approach to feature selection based on targEt PointS To computE neIghborhood relatioNs (EPSTEIN). Zohra Mehenaoui, Yacine Lafifi and Layachi Zemmouri from Algeria present an automatic approach to identifying learning styles based on patterns of learning behavior with respect to the Felder and Silverman Learning Style Model (FSLSM) and report about their study of 73 students enrolled in online courses. Last but not least, in a collaboration between Spain and Italy, Aurora Polo-Rodríguez, Pietro Dionisio, Francesco Agnoloni, Ana Perandrés Gómez, Cristiano Paggetti, Lucía González López, Alfonso Cruz Lendínez, Macarena Espinilla- Estévez and Javier Medina-Quero present their research on ubiquitous and wearable solutions to address active ageing and report about a pilot project implemented for the Andalusian community.

Learning Behavior Analysis to Identify Learner’s Learning Style based on Machine Learning Techniques

Learning styles cover various attributes related to the attitude and the learning behavior of individuals. Research and educational theories confirm that considering learning styles in distance learning environments can improve academic performance and learner satisfaction. The traditional approach to identify learning styles is based on asking students to fill out a questionnaire. This approach is considerably less accurate due to the learners’ lack of awareness of their own preferences. Furthermore, learners’ learning styles are defined only once. In this study, we propose an automatic approach to identify learners’ learning styles based on patterns of learning behavior with respect to Felder and Silverman Learning Style Model (FSLSM), in an online learning environment. Patterns of behavior were analysed based on a data-driven approach. This approach exploits different Machine Learning (ML) techniques to detect the learning styles of learners. To validate our proposals, experiments were carried out in a higher education institution with 73 students enrolled in online courses on the ADLS (Automatic Detection of Learning Styles) system that we implemented. A 9 runs cross-validation was used to evaluate the selected ML techniques. Detection accuracy, recall, precision, and F measure were observed. The findings show the possibility of detecting learning styles automatically based on learning behavior with high performances. Different levels of accuracy were found for the different dimensions of FSLSM. However, Support Vector Machines (SVM) have exhibited great ability in predicting learning styles for all dimensions of FSLSM with an accuracy average of 88%.

ILS Validity Analysis for Secondary Grade through Factor Analysis and Internal Consistency Reliability

In differentiated learning, the teacher needs to be aware of the learning styles of students in the classroom to accommodate specific learning preferences, e.g., the Felder–Silverman learning style model. The corresponding instrument, i.e., the Felder–Silverman Index of Learning Style (ILS), was designed to assess learning styles, specifically for engineering students. The ILS has been tested at the middle school level to identify the learning styles; however, validity/reliability had not been established in earlier studies with large samples. The focus of this study was to identify the validity and reliability of an ILS instrument for middle school students (N=450) by investigating the factor structure through factor analysis. This includes internal consistency reliability and constructing validity report of the ILS. An exploratory and confirmatory factor analysis was undertaken to investigate the factor structure to establish validity. As a result of the study, the reliability of the instrument was established. Five-factors emerged through exploratory factor analysis (EFA), which were subjected to confirmatory factor analysis (CFA). The outcome provided five-factors (i.e., Comparative Fit Index (CFI), Tucker–Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA), Standardized Root Mean Residual (SRMR), and Goodness of Fit (GFI)), out of which four factors were related to the four dimensions of the Felder–Silverman model, and the fifth factor was related to the association of sensing/intuitive and sequential/global dimensions of the model, which is in agreement with the theoretical construct of ILS. As a result of CFA, ILS entailing 24 items indicates a good fit with five-factor structure. CFI=0.922;TLI=0.927; RMSEA=0.026; SRMR=0.585;GFI=0.911;X2=277;df=42;p=0.60. This study suggests that the ILS for the secondary-grade students needs to be revised with fewer items to improve the reliability, as supported by empirical evidence through the EFA and CFA.

Developing the adaptive materials based on learning style to increase student's conceptual understanding

This study aimed to develop an adaptive electrical material based on Felder Silverman's learning style that can improve students' conceptual understanding. The method used in this research was the ADDIE research and development method which consists of the analysis, design, development, implementation, and evaluation stages. The data were gathered using learning style tests, concept understanding tests, and questionnaires. The data were then analyzed using descriptive analysis and N-gains. The analysis of the improvement of electrical learning outcomes used normalized gains. Based on the explanation, it can be concluded that the dominant student learning style characteristics are visual and sensing. The test results of the quality of electrical teaching materials with an average of 85.38 categories are very feasible. The results of the conceptual understanding test of students who received adaptive electricity material with an average N-gains of 0.7 categories were very high. The highest N-gains are students with visual and verbal learning styles, namely, 0.72. Based on the combination of the Felder Silverman learning style model, it is 0.73 for students with visual sensing learning style types.

Exploring the Effectiveness of Learning Path Recommendation based on Felder-Silverman Learning Style Model: A Learning Analytics Intervention Approach

A fixed learning path for all learners is a major drawback of virtual learning systems. An online learning path recommendation system has the advantage of offering flexibility to select appropriate learning content. Learning Analytics Intervention (LAI) provides several educational benefits, particularly for low-performing students. Researchers employed an LAI approach in this work to recommend personalised learning paths to students pursuing online courses depending on their learning styles. It was accomplished by developing a Learning Path Recommendation Model (LPRM) based on the Felder–Silverman Learning Style Model (FSLSM) and evaluating its efficacy. The data were analysed with the help of a dataset from the Moodle Research Repository, and different learning paths were recommended using a sequence matching algorithm. The effectiveness of this approach was tested in two groups of learners using the independent two-sample t-test, a statistical testing tool. The experimental evaluation revealed that learners who followed the suggested learning path performed better than those who followed the learning path without any recommendations. This enhanced learning performance exemplifies the effects of learning analytics intervention .

Research on the differences of students’ learning styles and knowledge integration in SPOC learning: a case study of course “Basic Portuguese”

Problem and goal. With the advent of the information age, Internet-based online learning has also become one of the learning methods chosen by many learners. They can use these online learning platforms to complete knowledge construction while learning offline. Methodology. Most studies of learning behaviors focus on the discovery of the best learning model and disregard the possible impact of different learning behaviors on knowledge construction. Therefore, based on the Felder - Silverman learning style model, this article uses the Solomon learning style scale to improve the design of the questionnaire and collect four-dimensional differential learning behaviors data. In order to further understand the influence of learning styles on the effectiveness of online learning, we also use online learning data on the Small Private Online Course platform and general cognitive intelligence knowledge integration theory to clarify the relation between learning modes and individuals differences. Results. This study observes and analyzes the learning behavior data of 46 students of Nankai University in the SPOC learning platform, also analyzes the differences in learning styles and knowledge construction of students in the SPOC environment. Compared with the traditional Basic Portuguese teaching method, the blended teaching model based on the Chaoxing Learning platform has unparalleled advantages. Interactions inside and outside the classroom, improving student participation and promoting teaching diagnosis. Conclusion. Through a comprehensive analysis of questionnaire data and online data, we found that some learning styles have different effects on the effectiveness of online learning, ignoring the individual differences of learners will still cause problems in knowledge construction.

Cluster Analysis for Student Grouping Based on Index of Learning Styles

This research aims to group students of Information and Computer Education, Universitas Sebelas Maret into classes based on the similarity of student’s learning style, and to analyze the characteristics of each group. In this research we used Felder and Silverman learning style model (FSLSM) that includes four dimensions, which are (1) learning process; (2) learning perception; (3) information input; and (4) information understanding. We used a quantitative descriptive method combined with cluster analysis technique. The subject of this research were 58 2nd year students of the Department of Computer and Informatics Education Universitas Sebelas Maret. Assessment of learning style characteristics was done by using the Index of Learning Styles (ILS) instrument developed by Felder and Soloman. We applied a hierarchical clustering method to the student’s ILS data for the cluster analysis. This research indicated that two dominant clusters were formed with different characteristics in the dimensions of learning styles. Cluster 1 has a reflective preference in the dimension of learning process with an average of -3.76; a sensing preference in the dimension of learning perception with an average of 4.24; a visual preference in the dimension of information input with an average of 5.21; and a global preference in the dimensions of information understanding with an average of -2.66. Whereas cluster 2 has an active preference in the dimension of learning process with an average of 2.52; a sensing preference in the dimension of learning perception with an average of 1.48; a visual preference in the dimension of information input with an average of 5.08, and a global tendency in the dimensions of understanding information with an average of -0.04. We either found some outliers with different characteristics of learning style outside the two clusters. The results of this research can be used by lecturers to determine the appropriate learning strategy for each class.

Framework for UX Based M-Learning Using Learning Style and Recommendation System

M-learning plays the key role in the educational industry is increasingly. However, the current use of M-learning is not designed for individual learner style. Consequently, it causes many students not to understand lessons precisely.Not only learning style which determines the course material but also user interface and interaction called user experience (UX). Therefore, m-learning combined learning style and user experience (UX) are also proposed to learners in order to obtain better understanding towards lessons. This study conducts sample group in basic programming subject with pre-test and modeling students' learning style using Felder & Silverman learning style. The results showed that students' have different learning styles. This prototype of User Experience of M-learning (UXML) related to this subject was developed to deliver the appropriate content match to each student's learning style. This courseware serves 4 patterns of derived learning style they are Active, Sensing, Visual andSequential.Hence, the students are able to understand topic easier from recommended course material with mobility features.

Uma revisão da literatura sobre os estilos de aprendizagem em cursos técnicos, superiores e de especialização e sobre os impactos do ensino remoto emergencial

A maneira pela qual cada pessoa aprende ao longo de sua vida é influenciada por características do próprio indivíduo, fatores externos relacionados ao meio em que vive e suas experiências. Cada indivíduo apresenta suas próprias preferências sobre como receber e processar informações, consolidando seu aprendizado sobre determinado assunto. Essas diferentes formas preferenciais constituem os Estilos de Aprendizagem. O conhecimento dos estilos de seus alunos possibilita ao professor adotar estratégias que promovam melhorias no desempenho da turma, direcionando o conteúdo conforme as preferências de seus alunos. A importância das práticas pedagógicas dos docentes ganhou ainda mais importância no ano de 2020 devido à pandemia da COVID-19. Como as atividades presenciais de ensino foram suspensas, as instituições adotaram o Ensino Remoto Emergencial. Foi necessária uma adaptação rápida dos profissionais da educação ao uso das Tecnologias Digitais da Informação e Comunicação, caracterizando assim um grande desafio aos docentes nas práticas pedagógicas. Dentro deste contexto, este trabalho tem o objetivo de fazer uma revisão da literatura sobre os Estilos de Aprendizagem de alunos de cursos técnicos, superiores e de especialização, nas modalidades presenciais e à distância, de acordo com o Índice de Estilos de Aprendizagem, de Felder e Silverman, e também apresentar investigações e relatos sobre os impactos do Ensino Remoto Emergencial na vida dos docentes.

Development of E-learning System Using Felder and Silverman’s Index of Learning Styles Model

The existing online education system mostly provides users with undistinguished learning content. The existing online education system assumes that users have the same characteristics or tendencies as one another. Every user is not the same; they have different characteristics, tendencies, and learning preferences. Each user has different personal characteristics, but today's online education systems ignore them. This study use Felder Silverman's learning style model. Through characteristic learning styles,relationships, and content types result in more personalized e-learning. Researchers distributed questionnaires to Informatics Engineering students at, University of August 17, Surabaya, and managed to get 277 student respondents. This data uses to determine student learning styles using a questionnaire-based psychometric instrument called the index of learning style (ILS), which uses to determine learning styles. The questionnaire, validity, reliability, determination of learning styles, learning content, and material requirements are testing. This study concludes that the questionnaire used meets the standards for determining learning styles for users. The system that builds has five modules to facilitate construction. These modules include the media library, domain repository, student model, instruction model, adaptive engine, and user interface. It can conclude that the modules made can help lecturers to distribute material according to the material content that is suitable for student learning styles and helps students to study more effectively according to their preferences

Learning Styles and Dyslexia Types – Understanding Their Relationship and its Benefits in Adaptive E-learning Systems

Learning is a fundamental element of people’s everyday lives. Learning experiences can take the form of our interactions with others, through attending an educational establishment, etc. Not everyone learns in the same way, and even people who are considered to have a similar standard of abilities or proficiency will exhibit different learning styles. This does not necessarily mean that some students are better than others; it means that students are different from one another. Adaptive e-learning system should be capable of adapting the content to the user learning style, abilities and knowledge level. In this paper, we investigate the benefits of incorporating learning styles and dyslexia type in adaptive e-learning systems. Adaptivity aspects based on dyslexia type and learning styles enrich each other, enabling systems to provide learners with materials which fit their needs more accurately. Besides, consideration of learning styles and dyslexia type can contribute to more accurate student modelling. In this paper, the relationship between learning styles, the Felder–Silverman learning style model (FSLSM), and dyslexia type, is investigated. These relationships will lead to a more reliable student model.

Motivation of University Students Towards the Use of Information and Communication Technologies and Their Relation to Learning Styles

Motivation can influence the success of the teaching-learning process. The use of Information and Communication Technology (ICT) in Education is becoming more and more frequent. In fact, its presence is very engaging and may improve learning. This study is aimed at analysing the relation between university students' intrinsic motivation and their learning styles, where variables such as gender and the universities at which they study can be essential. For that purpose, the answers provided in both the IMI (Intrinsic Motivation Inventory) questionnaire and the Felder and Silverman's Learning Style Inventory by a sample of 272 students from two different universities have been taken into account. The two questionnaires were completed online, allowing access from any device with an internet connection. Once the data obtained was analysed, a high correlation among the six dimensions of the motivation questionnaire was observed. Said correlation does not apply to Felder and Silverman's test, where there is little correspondence among the eight styles in the inventory. A clear relation between two learning styles (Visual and Reflective) and intrinsic motivation is also noted. It should be highlighted that on examination of the variables corresponding to the university where student study and the latter’s gender were examined, certain differences appeared, such as the prevalence of some learning styles in male participants.