Felder-Silverman Model Research Articles

A Sustainable Educational Tool for Engineering Education Based on Learning Styles, AI, and Neural Networks Aligning with the UN 2030 Agenda for Sustainable Development

This study addresses the United Nations 2030 Agenda Sustainable Development Goals 4, 8, 10, and 12 by developing a resource-efficient tool that promotes equitable quality education and lifelong learning opportunities, supports decent work and economic growth, reduces inequalities, and ensures sustainable consumption and production patterns. This study contributes to sustainable education by providing a tool that is designed to be easy to use, easy to modify, and resource-efficient, making it accessible to institutions with limited technological resources. The tool uses artificial intelligence and a long short-term memory (LSTM) neural network to provide personalized teaching, adapting to the unique learning styles of its users. A custom survey adapted from the Felder–Silverman model was used to track weekly learning style transitions among 72 engineering students at the Faculty of Engineering at the University of Los Llanos. These data were used to build the LSTM model to predict learning style transitions over a 16-week semester. Two interfaces were created: one for instructors, integrating the LSTM model, and one for students, incorporating a custom survey. An OpenAI API-powered chat was also built into both interfaces to provide study advice to students according to their styles and enable professors to personalize their teaching methodologies in engineering education.

Identifying Student Learning Styles Using Support Vector Machine in Felder-Silverman Model

Identifying Student Learning Styles Using Support Vector Machine in Felder-Silverman Model

Teaching foreign-language semantic reading of professionally-oriented texts based on students’ learning styles

Importance. The principle of individualization in developing skills of foreign-language semantic reading professionally oriented texts based on students’ learning styles is studied. The relevance of the study is due to the demand for an adaptive home reading system for students majoring in nonlinguistic fields.Materials and Methods. The ways of diagnosing the learning styles of second-year engineering students using the questionnaire of R. Felder and B. Solomon are described and individualized tasks for improving the semantic reading skills in English according to students’ learning styles are proposed.Results and Discussion. The use of the Felder–Silverman model of learning styles, describing various styles of perception, input, processing and understanding of the information received seems to be an important variable to study, since it helps to individualize the various stages of working with a foreign language text. The results of the experimental training proved that the proposed approach positively influenced the students’ progress in foreign-language semantic reading.Conclusion. Determining the learning styles of students enables university lecturers to take into account the individual characteristics of students’ cognitive processes, their current cognitive strategies and, thus, individualize foreign language teaching.

Implementation of a Personalized Adaptive Mobile Learning System

A notable obstacle in the field of education is the restricted flexibility of traditional teaching approaches. These approaches frequently take a consistent stance, disregarding the wide range of learning preferences that pupils possess. This leads to a decrease in student motivation and engagement, which in turn produces below-average learning outcomes. This research focuses on creating an adaptive learning system that classifies learners using the Felder-Silverman model in order to overcome these problems. After then, this system creates customized recommendations based on user choices in an effort to improve learning results. In order to keep enhancing the system's efficacy, the study have also included a feedback mechanism and performance evaluation.

Learning Styles Impact Students’ Perceptions on Active Learning Methodologies: A Case Study on the Use of Live Coding and Short Programming Exercises

This research paper explores the effectiveness of live coding as an active learning methodology in teaching programming, particularly in the context of diverse learning styles. Live coding, characterized by real-time coding demonstrations by instructors, has been increasingly adopted to enhance the learning experience in programming education. It offers immediate feedback, demonstrates problem-solving in action, and allows instructors to incorporate student suggestions, making it a dynamic and engaging teaching tool. However, its effectiveness varies among students with different learning preferences. This study investigates the impact of various learning style dimensions, as defined by the Felder–Silverman model, on the effectiveness of live coding in an introductory object-oriented programming course. The study was conducted at Aalborg University, Denmark, with students from the BSc Software program. It aims to provide empirical evidence on how different learning style dimensions influence student preferences and the effectiveness of live coding, offering insights to educators for tailoring active learning methodologies in programming courses to diverse learner needs.

A Fuzzy C-means Algorithm to Detect Learning Styles in Online Learning Environment

The ability to detect learners’ learning styles based on their learning behaviors is of utmost importance for online educational systems, as it greatly enhances student engagement, motivation, and overall learning outcomes. Knowing the learning preferences of learners may significantly aid in creating personalized learning recommendations and empower learners to identify their own learning styles. However, learners exhibit diverse behaviors in an online setting, which poses significant difficulties in detecting their learning style. This paper proposes a novel approach for detecting learning styles using graph representation learning techniques and machine learning algorithms. While our approach is not reliant on a particular learning style model, our approach may be divided into two distinct parts. Initially, we represented the behavior of learners as a bipartite graph and then transformed this graph into a lower-dimensional representation using the graph embedding approach. This lower-dimensional representation was then utilized for machine learning tasks. Furthermore, we categorize the encoded learner’s sequence using a clustering technique based on the selected learning style model. Our methodology uses the Felder-Silverman model as the learning style model and the Fuzzy C-means algorithm as a clustering technique. Our approach was evaluated using the 2015 KDD Cup dataset through a series of comprehensive experiments to showcase its effectiveness. The findings demonstrate that our approach surpasses the previous approach, achieving an average precision of 0.8737 and an accuracy of 0.9182.

A Study on Learning Strategies and Styles Segmentation of Learner Groups in Online Education Platforms

Abstract Online education is becoming a popular form of education day by day. In order to provide corresponding learning programs according to the types of learning styles and learning strategies of different learners, this paper proposes a learning style recognition algorithm based on the Felder-Silverman model. The Bayesian discriminant analysis algorithm is used to calculate the probability estimation of samples belonging to one category, and then the clustering center is calculated by k-means clustering analysis so as to complete the classification of learning styles. According to the results of learning style identification, the perceptual learning group has the highest level of cooperative communication strategies in the information perception style dimension, with a mean score of 3.575, the linguistic group is the least adept at all strategies in the information input dimension, with a mean score of only 3.55, the sequential group excels in a variety of strategies for information comprehension, and the active group is the most adept at performing information processing. The personalized learning experiment was conducted according to the characteristics of different style groups. It was found that students who received personalized teaching based on their learning styles improved their performance more significantly, with their average scores in each subject increasing from 73.174 to 82.247 in the pre-test. Their scores in the post-test were 5.729 points ahead of those of the control class. The application effects are significant when learning strategies and styles are classified, and teaching programs are improved accordingly.

AdPisika: an adaptive e-learning system utilizing k-means clustering, decision tree, and bayesian network based on felder-silverman model to enhance physics academic performance

Amid the shift to online learning during the COVID-19 outbreak, the academic performance of students has become a concern. To address this, Adaptive Learning Systems (ALS) have emerged, these help in assessing students and delivering personalized content. This study develops an ALS incorporating K-means Clustering, Decision Tree, and Bayesian Network techniques, based on the Felder-Silverman Learning Style Model (FSLSM). The aim is to optimize learning materials based on students' current Knowledge Level (KL) and their Learning Style (LS). The students who utilized the proposed system showed substantial improvements in their performance across the Electromagnetic Spectrum, Light, Electricity, and Magnetism modules, with increases of 28.8%, 41.4%, 31.9%, and 32.9%, respectively. These findings provide strong evidence that the adaptive e-learning system had a significant positive impact on post-test scores compared to pre-test scores, surpassing the outcomes achieved with the traditional learning approach. With a silhouette score of 0.7 for K-Means clustering, an accuracy of 87.5% for Decision Tree, and a 95.1% acceptance value for the distribution of learning objects using the Bayesian Network, the proposed adaptive system demonstrated successful implementation of these machine learning algorithms. Furthermore, the proposed system received "excellent" ratings for functional stability, performance efficiency, compatibility, and reliability, with mean values of 4.49, 4.43, 4.43, 4.8, and 4.47 respectively.

The Effect of a training program based on Felder-Silverman model on improving reflective practices of Muslim bin Yasar Middle School teachers in Riyadh

هدف البحث إلى التعرف على أثر برنامج تدريبي قائم على نموذج فيلدر- سيلفرمان في تنمية الممارسات التأملية لدى معلمي مدرسة مسلم بن يسار المتوسطة، ولتحقيق هدف البحث استخدم الباحث المنهج شبه التجريبي من خلال التصميم ذو المجموعة الواحدة ذات القياس القبلي والبعدي، وقد طبق البرنامج التدريبي بواقع (16) جلسة تدربية خلال (8) أسابيع بواقع جلستين لكل أسبوع، مدة كل جلسة تدريبية ساعة واحدة، اشتملت على التعريف بأبعاد وأنماط نموذج فيلدر سيلفرمان، مدعومة بالأمثلة والأنشطة، وتكونت العينة من (15) معلماً بمدرسة مسلم بن يسار المتوسطة بمدينة الرياض، واستخدمت استبانة ممارسات تأملية كأداة لجمع البيانات قبل البرنامج وبعده، ولتحليل البيانات الكمية تم استخدام اختبار T Test لمعرفة مدى دلالة الفروق بين الاختبارين القبلي- البعدي، ومربع إيتا لقياس فاعلية البرنامج التدريبي. وبعد تحليل البيانات باستخدام اختبار ولككسون لحساب الفروق بين درجات التطبيق القبلي والبعدي لأداة البحث تم التوصل لوجود فروق ذات دلالة إحصائية بين التطبيقين القبلي والبعدي لصالح التطبيق البعدي للبرنامج التدريبي القائم على نموذج فيلدر – سيلفرمان في تنمية الممارسات التأملية لدى معلمي المرحلة المتوسطة في محاور الثلاثة (التخطيط، والتنفيذ، والتقويم). في ضوء النتائج أوصى الباحث بالاستفادة من البرنامج التدريبي القائم على نموذج فيلدر ــــ سيلفرمان في مجالي التدريب والتدريس، كما أوصى بمراعاة المعلمين لأنماط تعلم الطلاب، وجعله محتوى متاح من خلال الواجبات والأنشطة والإثراءات والتقويم ليضمنها المعلمون في التحضير الإلكتروني في منصة مدرستي، كما اقترح دراسات مكملة في الموضوع بتطبيق دراسة مشابهة لمعلمي المرحلتين الابتدائية والثانوية.

A Student Learning Style Auto-Detection Model in a Learning Management System

Learning style plays an important role in enabling students to have an efficient learning process. This paper proposes an auto-detection model of student learning styles in learning management systems based on student learning activities. A literature review was conducted to investigate the components of online learning activities. The search terms used were "online learning activities", "learning management systems", and "Felder-Silverman Learning Style Model (FSLSM)." A combination of the search terms above was also executed to enhance the search process. Based on the results of the review, eleven classes of online learning activities were identified, namely forum, chat, mail, reading materials, exam delivery time, exercises, access to examples, answer changes, learning materials, exam results, and information access. The online learning activities identified were then mapped to the Felder-Silverman model based on four model dimensions: processing, perception, input, and understanding. The proposed model shows the attributes of the online learning activities based on the dimensions in the FSLSM. The proposed model can assist educators to improve learning content according to the suitability of students and recommend appropriate learning materials to students based on their characteristics and preferences. Future studies include the use of machine learning algorithms such as decision trees to auto-detect student learning styles in learning management systems.

Personalization of Distance Learning: Using PLS-SEM for Analyzing the Possibility of Differentiating Students with the Felder-Silverman Learning Style Model

Personalization of learning, that is, taking into account various characteristics and needs of students, is a critical issue in organizing education and determines the relevance of finding ways to differentiate students for predicting the vector of their integration into the learning process, including in a distance learning format. This article discusses the possibility of using the Felder-Silverman model for personalizing distance learning. The purpose of the study is to explore the relationship between individual learning styles and factors causing anxiety in distance learning and shaping students' attitudes towards distance learning. The PLS-SEM method was used as a research tool. It is shown that the stylistic features of students determine the direction of their attitudes towards the negative and positive aspects of distance learning. Students' attitudes towards such social phenomena in distance learning as difficulties in communicating with other students, lack of student activity, social isolation, absence of competition between students, and difficulties in communicating with pedagogues depended on the stylistic features of the students. At the same time, the stronger these social phenomena caused anxiety in students, the higher their negative attitude towards the positive aspects of distance learning and the possibility of a full transition to distance learning or a full replacement of in-person learning with distance learning. Moreover, the higher the importance of classroom lessons and the presence of printed textbooks for students, the more anxiety the mentioned social phenomena caused in students. It is shown that such constructs of the PLS-SEM structural model as Social factors of distance learning anxiety and Importance of material factors in learning have a negative relationship with Attitude towards distance learning.

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.

How Transformational Leadership Can Help Increase Effectiveness In Teaching?

Drawing on the learning theory of the Felder-Silverman model, transformational leadership theory, and the current scholarly works, this paper provides a teaching and learning model for management education. Professors, trainers, and consultants demonstrate a number of learning, teaching, and leadership styles and there is a strong responsibility to organize and present a course or training platform to satisfy participants’ varied learning styles. After a brief review of the literature, this paper presents a model to assess the leadership, teaching, and learning styles of management educators and participants.

Validation of ILS through Exploratory Factor Analysis and Internal Consistency Reliability for Secondary Grade

There is a continuous debate on the validity of learning styles in literature. Several models have been proposed for the learning styles, corresponding to the psychometric assessment instrument. Among these models, Felder-Silverman model is widely used by educators to identify the learning styles of the engineering students. The instrument that measures the learning styles is the index of learning style (ILS). This study focuses to identify the validity and reliability of the ILS instrument for middle school students (N=260). This includes internal consistency reliability and construct validity report of the ILS. As a result of the study, the reliability of the instrument was established, however, it was found that there were cross loadings in the 14-factor solution and the 4-factor solution. Thus, the instrument validity for secondary grade students was not established.

Ensemble Learning Using Fuzzy Weights to Improve Learning Style Identification for Adapted Instructional Routines.

Mobile personalized learning can be achieved by the identification of students’ learning styles; however, this happens with the completion of large questionnaires. This task has been reported as tedious and time-consuming, causing random selection of the questionnaires’ choices, and thus, erroneous adaptation to students’ needs, endangering knowledge acquisition. Moreover, mobile environments render the selection of questionnaires’ choices impractical due to confined mobile user interfaces. In view of the above, this paper presents Learnglish, a fully developed mobile language learning system incorporating automatic identification of students’ learning styles according to the Felder-Silverman model (FSLSM) using ensemble classification. In particular, three classifiers, namely SVM, NB and KNN, are combined based on the majority voting rule. The major innovation of this task, apart from the ensemble classification and the mobile learning environment, is that Learnglish takes as input a minimum number of personal (i.e., age and gender) and cognitive characteristics (i.e., prior academic performance categorized using fuzzy weights), and solely four questions pertaining to the FSLSM dimensions, to identify the learning style. Furthermore, Learnglish incorporates adapted instructional routines to create an individualized learning environment based on students’ learning preferences as determined by their style. Learnglish was fully evaluated with very encouraging results.

The Learning Styles of Electrical Technology Students in Malaysia’s Northern Zone

This article presents the findings of a study analyzing the learning styles of students in electrical technology (ET) program in a Vocational College (VC) in Northern Zone, Malaysia. The study utilized the Felder-Silverman model and also the Index of Learning Styles (ILS) as a survey instrument. The sample consisted 57 students. The raw ILS responses were collected from the students. Data were analyzed descriptively and compared to both the ET students and to the similar studies done by other researchers. Results from the analysis revealed that the fourth-year students of this college are more visual (91%), active (86%), sensing (61%) and sequential (54%) learners. This study suggest that the college lecturers might align their teaching approaches, method and strategies with the dominant ET students learning style for each dimension in order to make the learning process more meaningful in terms of learning outcomes for learners and lecturers as well

A fuzzy classification approach for learning style prediction based on web mining technique in e-learning environments

Adaptive E-learning platforms provide personalized learning process relying mainly on learning styles. The traditional approach to find learning styles depends on asking learners to self-evaluate their own attitudes and behaviors through surveys and questionnaires. This approach presents several weaknesses including the lack of self-awareness of learners of their own preferences. Furthermore, the vast majority of learners experience boredom when they are asked to fill out the corresponding questionnaire. Besides that, traditional approach assumes that learning styles are fixed, and cannot change over time. In this paper, we propose a generic approach for detecting learning styles automatically according to a given learning styles model. In fact, our approach does not depend on a specific LSM. This work consists of two major steps. First, we extract learning sequences from learners log files using web usage mining techniques. Second, we classify the extracted learners’ sequences according to a specific learning style model using clustering algorithms. To perform our approach we use Felder-Silverman Model as LSM and Fuzzy C-Means as a clustering algorithm. We have conducted an experimental study using a real-world dataset. The obtained results show that our approach outperforms traditional approach and provides promising results.

The Learning Styles of Undergraduate Students in CM Bachelor’s Degree Programs in the U.S.

ABSTRACTThis article presents the findings of a study analyzing the learning styles of undergraduate construction management (CM) students in bachelor’s degree programs in the United States. The study utilized the Felder-Silverman model and the Index of Learning Styles (ILS) as a survey instrument. The survey population consisted of 1,069 CM students from 36 university CM programs across the Associated Schools of Construction regions. Demographic information, the raw ILS responses, and the ILS web-based survey report were collected from the students. The results were analyzed and compared to both the CM students themselves and to similar studies done with engineering students. It was found that CM students were visual, active, sensing, and sequential learners. This study provides recommendations for how CM instructors might align their teaching styles with CM-student learning styles, and discusses impacts on the CM industry.

AR‐based value‐added visualization of infographic for enhancing learning performance

AbstractAugmented reality‐based infographic is an edutainment media that can play a significant role in transferring complicated issues for learning purposes. It has the potential to draw participant's attention to visualize various kinds of information including text, image, animation, 3D objects, etc. on infographic using smatphones and tablets. This paper discusses the effect of AR‐based infographic on improving learning performance via T‐Paired test and in the meantime the correlation between participants’ learning style based on Felder–Silverman model with their quadratic Bloom‐based knowledge. Experimental results confirm the usability of AR‐based infographic in improving learning performance and its impact on visual type participants no matter which kinds of knowledge they are equipped with.

Effectiveness of Learning Style in Popularity of Personalized Mobile Intelligent Tutoring System from View of Learners

Objectives: Each learner shows unique behavior in educational environments to acquire knowledge and skills. It leads to form variant learning styles. Methods/Analysis: This paper develops a tutoring system called the Personalized Mobile Intelligent Tutoring System (PMITS). The purpose is evaluating the effectiveness of the learning style (according to the Felder-Silverman model) in rate desire users to use from PMITS. In this paper 93 users exercises by PMITS Then a questionnaire was distributed between them, and the results obtained by the software SPSS Version 22.0 and analyzed by Kruskal-Wallis test. Findings: In this analysis p-value=0.000 calculated and the null hypothesis was rejected. Therefore, PMITS popularity was different between users with different learning style; Also, most of the learning style of combined was {Active, Sensing, Visual, Sequential}. Finally, this paper suggests that a PMITS based on learning style is so useful experience to users. Novelty/Improvement: PMITS can support learning styles to provide an effective education system for users (according to the Felder-Silverman model).