Felder-Silverman Learning Style Research Articles

Effects of a grouping intervention using the Felder-Silverman learning style model on problem-based learning among nursing students: A randomized controlled trial

Effects of a grouping intervention using the Felder-Silverman learning style model on problem-based learning among nursing students: A randomized controlled trial

Effectiveness of Virtual Labs for Physics Learning in Moroccan Secondary Schools

The teaching of physical sciences primarily relies on the use of experimental activities to explain the phenomena and physical concepts taught theoretically in class. Nevertheless, this conventional approach faces various challenges and problems, which limit its ability to achieve the different pedagogical objectives set by teachers. In this study, we evaluated the effectiveness of virtual laboratories (“VLABs”) as an alternative tool to traditional practices for teaching physics concepts in Moroccan secondary schools. The methodology followed in this study is quantitative and based on two questionnaires. The first is intended for teachers to evaluate the experimental activities on VLABs based on a socio-technical and pedagogical heuristic study and to verify their conformity with the expectations and characteristics of each type of learner according to the Felder-Silverman learning style preferences. The second was administered in the form of an evaluation to compare the performance of the control group with the experimental group that integrated the opportunities offered by the VLAB LABSTER. The results show that VLABs are not only comparable to conventional experimental activities in terms of applicability, ease of use, and effectiveness in achieving the learning objectives for which they were initially designated but surpass them in terms of performance, as evidenced by the significant improvement of the mean obtained by the group that integrated the experimental activities on the VLAB. Thus, VLABs can be used not only as an alternative to traditional experimental activities in distance learning but also as a viable option for potential adoption even in traditional face-to-face physics instruction.

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.

Analysis of Project-Based Learning Model on the Learning Styles of PPLG SMK Negeri Students in Surakarta

<p dir="ltr"><span id="docs-internal-guid-6d3934c3-7fff-f21d-464d-9069653d7c70"><span>Project-based learning (PjBL) is one of the learning models that can optimize students' learning styles. PjBL provides students with the opportunity to learn actively, independently, and creatively. This study aims to analyze the effect of the PjBL learning model on the learning styles of PPLG SMK Negeri students in Surakarta. This study used a quantitative method with an experimental approach. The research sample was 36 XI grade PPLG SMK Negeri students in Surakarta. This study used the Felder-Silverman learning style instrument to measure students' learning styles. The results of the study showed that the PjBL learning model had a positive effect on students' learning styles. Students who followed PjBL learning were more likely to have kinesthetic, visual, and auditory learning styles. This is because PjBL provides students with the opportunity to learn actively through activities that involve body movement, vision, and hearing. Based on the results of this study, it is suggested that the PjBL learning model can be applied more widely in schools, especially in SMK which have students with various learning styles.</span></span></p>

An evolving learning style detection approach for online education using bipartite graph embedding

The increasingly widespread online education recently enabled a new form of teaching, learning, and overall educational outcome. However, the need of personalization is required as learners learn differently and learning follows a one-size-fits-all approach. Learning style is the most used personalization that plays an important role in learning. This learning style is a changeable trait that is influenced by the learner's behavior based on past experiences. As a result, knowing it over time helps to point learners in the right direction, motivate them, and enhance their learning outcomes. Research done so far does not take into account the changes in the behavior of learners, and the behavioral data is at large volume, making the existing approaches fail to capture and extract the behavior of learners efficiently. Inspired by these constraints, we propose an incremental learning style detection approach for online education with a bipartite graph embedding technique. We first construct a dynamic bipartite graph to represent the incremental interaction between learners and learning resources while learning. Then, introduce a dynamic bipartite graph embedding to learn the low dimensional representation of the constructed graph from the current and previous time. Finally, the low dimensional features are mapped to the selected Felder-Silverman learning style model (FSLSM) dimension each time to identify and group similar learners using the k-means clustering algorithm. The proposed approach can be integrated into different educational systems. Extensive experiments conducted using three datasets from 2015 KDD Cup courses demonstrate the effectiveness of our approach. Average of 93.19%, 95.76%, and 98.48% are achieved across the three datasets in accuracy compared to the existing approaches.

GAT-LS: A Graph Attention Network for Learning Style Detection in Online Learning Environment

In the context of the information age, the rapid growth and increasing diversity of learning resources underscore the urgency of personalized learning, while learning style is the most crucial factor to consider in personalized learning as it significantly influences students’ academic achievements and learning experiences. Traditional methods of assessing learning styles, such as completing questionnaires, have many drawbacks, including subjectivity and time costs. Therefore, in recent years, researchers have been exploring automatic methods to identify learning styles by analyzing students’ interactive behaviors. Motivated by these limitations, we propose a learning style detection method using a graph attention network (GAT), named GAT-LS. We originally constructed a bipartite graph between learners and learning materials, utilizing node features to represent the students’ behavior. Subsequently, we employ GAT to obtain hidden vectors for the graph nodes. These hidden vectors encapsulate both the overall graph information and the importance of neighboring nodes. We employ a multi-head attention network to process student nodes and combine a dropout mechanism with a single-layer attention network to process learning material nodes. Finally, we map the obtained hidden node features to the Felder-Silverman learning style model (FSLSM) and use K-means clustering to detect learning styles. The proposed method can be integrated into various types of educational systems or online learning platforms, providing a better educational experience and learning resource recommendations for both teachers and students. Experiments on the real-world dataset, KDD CUP 2015, demonstrated the superiority of our method. Our proposed approach achieved outstanding results with average values of 0.9647 accuracy, 0.9478 precision, 0.9171 recall, and 0.9346 F1 score.

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.

J48-Based Decision Tree Algorithm in Detecting Kolb’s Learning Style Preferences of Information Technology Students

Teacher education institutions developed interventions to augment both the Teacher’s teaching and the Learner’s learning process. Previous studies focused on detecting learning styles in e-learning using learning management systems and adaptive learning in the online learning process, specifically using the Felder-Silverman learning style model. On the contrary, this study aimed to develop a decision tree-based model to detect the Learner’s learning style inspired by Kolb’s learning style in a face-to-face learning environment. Knowledge discovery in databases through data mining was utilized using the J48 algorithm to develop a decision tree-based model. This study was participated by 408 out of 462 information technology students in a state university in the Philippines. The study’s result was able to develop four J48-based decision trees with conditional rule models for activist, reflector, theorist and pragmatist learners. The evaluation of the decision tree models using confusion matrix and receiver operating curve showed a very high accuracy detection of every learning style (weighted average of 88-96%). This result recommends applying this in an actual system or computer application for easy and fast learning style detection based on the characteristics of the learners.

UI/UX Design for Student Discussion Applications Based Felder Silverman Learning Style with the Design Thinking Method

Telkom University is one of the educational institutions that implement asynchronous learning through its Learning Management System (LMS). Based on preliminary research conducted with 37 respondents, just 13.5% of the participants LMS using a smartphone, because responsiveness issue. The Discussion Forum is a frequently used feature with high student interaction. However, this feature has several shortcomings, such as the lack of responsiveness in the mobile web interface and the limited interaction between users and professors. This research will employ the Design Thinking methodology and adopt the Felder Silverman Learning Style (FSLS). The evaluation of the prototype design resulted in a System Usability Scale (SUS) testing score of 72.22 for the LMS Celoe website and 85.65 for the proposed UI/UX application. The SUS testing score for the LMS Celoe website falls within Quadrant C, indicating an acceptable level of acceptance with a grade C scale and a rating of "Good." On the other hand, the SUS testing score for the proposed UI/UX application falls within Quadrant B, with an acceptable level of acceptance, a grade B scale, and an "Excellent" rating.

Recommendation Model for Learning Material Using the Felder Silverman Learning Style Approach

The biggest obstacle that students have when participating in a virtual learning environment (e-learning) is discovering a platform that has functionalities that can be customized to fit their needs. This is usually accomplished in several ways using educational resources such as learning materials and virtual classroom design elements. Our research has tried to meet this demand by suggesting an extra element in the virtual classroom design, i.e., classifying the students’ learning styles through machine-learning techniques based on information gathered from questionnaires. This feature allows teachers or instructors to modify their lesson plans to better suit the learning preferences of their students. Additionally, this feature aids in the creation of a learning path that serves as a guide for students as they choose their course materials. In this study, we have selected the Felder-Silverman Learning Style Model (FSLSM) in the questionnaire design, which focuses on identifying the students' learning styles. After that, we employ several machine learning algorithms to create a prediction model for the students’ learning styles. The algorithms include Decision Tree, Support Vector Machines, K-Nearest Neighbors, Naïve Bayes, Linear Discriminant Analysis, Random Forest, and Logistic Regression. The best prediction model from this exercise contributes to the recommendation model that was created using a collaborative filtering algorithm. We have carried out a pre-test and post-test method to evaluate our suggestions. There were 138 learners who were following a learning path and participated in this study. The findings of the pretest and post-test indicated a notable increase in students' motivation to study. This is confirmed by the fact that learners' satisfaction with online learning climbed to 87% when the learning style was considered, from 60% when it wasn't. Doi: 10.28991/HIJ-2023-04-04-010 Full Text: PDF

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.

Machine Learning Prediction Model for Early Student Academic Performance Evaluation in Video-Based Learning

The transformation of education norms from face-to-face teaching era to the Massive Open Online Courses (MOOCs) has created the emergence of educational technology domain for many students to access e-learning platforms. However, there are some drawbacks especially in asynchronous video-based learning. A sense of isolation could occur between teacher and students if the teachers do not interact much with the students in the asynchronous video-based learning. Consequently, the knowledge that is delivered by the teacher may not reach students effectively and cause a drop in student performance in the coming examination. Moreover, the growth of video-based learning has created a huge amount of data on the student learning process on the educational video which may provide a boost for educational data mining research. Therefore, this research study aims to introduce a predictive model that scrutinize the number of video view data based on each chapter in the video as well as student learning style, Felder-Silverman (FS) learning style model to deliver a prediction on individual student early performance in asynchronous video-based learning. This research has tested the different combination of feature selection methods with several handle of imbalance data methods such as Synthetic Minority Oversampling Technique (SMOTE), SMOTE-TOMEK and Adaptive Synthetic (ADASYN) algorithms to build the machine learning model and compare the model performance. As a result, proposed machine learning classifier algorithms with the combination of Maximum Relevance and Minimum Redundancy (MRMR) as feature selection method and SMOTE has been achieved the highest Area Under Curve (AUC) rate of 0.93.

Effectiveness of an Adaptive Learning Chatbot on Students’ Learning Outcomes Based on Learning Styles

Intelligent learning systems provide relevant learning materials to students based on their individual pedagogical needs and preferences. However, providing personalized learning objects based on learners’ preferences, such as learning styles which are particularly important for the recommendation of learning objects, re-mains a challenge. Recommending the most appropriate learning objects for learners has always been a challenge in the field of e-learning. This challenge has driven educators and researchers to implement new ideas to help learners improve their learning experience and knowledge. New solutions use artificial intelligence (AI) techniques such as machine learning (ML) and natural language processing (NLP). In this paper, we propose and develop a new personalization approach for recommendation that implements the adaptation of learning objects according to the learners’ learning style mainly focused on the use of a chatbot, named LearningPartnerBot, which will be integrated into the Moodle platform. We use the Felder-Silverman Learning Styles Model to determine learners’ learning styles in order to recommend learning objects, and also to overcome the cold start problem. A chatbot is an automated communication tool that attempts to imitate a conversation by detecting the intentions of its user. The proposed LearningPartnerBot should be able to answer learners’ questions in real time and provide a relevant set of suggestions according to their needs.

HyFlex learning in higher education: What is the conceptual model for realizing equitable learning?

HyFlex learning is a hybrid learning transformation where this term is new, but the modality has been applied during the COVID-19 Pandemic and is being prepared for future learning. However, the main problems and challenges related to student’s needs, expectations, cognitive learning styles, and pedagogical equity. This must be the highest consideration in determining the content, strategy, and choice of the best HyFlex learning mode. The student preferences need to be applied face-to-face, online synchronous, or online asynchronous. Therefore, this study is critical to providing solutions to the problems. This study uses a theoretical approach to cognitive learning styles, HyFlex learning modes, and pedagogy equity. This study produces a conceptual model of the interaction of HyFlex learning with an equitable cognitive learning style. This study becomes the essential reference for further empirical studies to make a final model that will become a reference for institutions, lecturers, and learning designers to formulate policies, establish strategies/methods and provide feedback in the development of systems, content, and adaptive learning processes in an effective HyFlex learning environment equitable today and in the future. Keywords: HyFlex learning; Kirton’s cognitive theory; Felder-Silverman learning styles; equitable learning

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.

Design and Application of Hybrid Personalized Effective Teaching Strategy based on Learning Style Classification

With the continuous advancement of information technology in vocational education, personalized learning teaching models are deeply integrated with mobile teaching platforms. The online and offline blended teaching model has become a new perspective for higher education teaching reform. However, blended teaching has not yet achieved the expected teaching results in practical applications. How to further improve the effectiveness of blended teaching has become a problem to be solved. After analyzing the necessity of integrating learning styles into effective blended personalized teaching, this article draws on the Felder-Silverman learning style model to guide blended personalized teaching strategies. From four aspects: learning content organization, learning tool support, learning resource organization, and learning evaluation organization, teaching support strategies are designed for different types of learning styles, Finally, taking the task of calculating internal rate of return of investment management project in the course of financial management as an example, this paper introduces the specific application of this teaching strategy in hybrid teaching.

The Learning Styles of Prospective Biology Teachers at Islamic University in Indonesia

The aim of this study was to investigate the learning styles of prospective biology teachers in Islamic universities (PBTs-IU) in Indonesia during the COVID-19 pandemic. 195 PBTs-IU were surveyed by using the Felder-Silverman Learning Style instruments. The results show that in online learning PBTs-IU practice and like dimensions of learning styles, such as in active – social oriented learning, in sensing – existing ways, concrete material, careful with details, in visual – pictures, and in sequential – sequential progress.

Context and Learning Style Aware Recommender System for Improving the E-Learning Environment

The learning management system (LMS) is an e-learning software that raised the interest of disparate learners’ groups. However, learners have difficulties in finding learning resources tailored to their preferences in the best way at the right time. Making the learning process more efficient and pleasant for learners can be achieved by using context and learning styles such as customizing aspects. This study proposes a new data-driven approach to retrieve learners' characteristics using traces of their activities based on the Felder-Silverman Learning Style Model (FSLSM). In this research, the traces of 714 learners who enrolled in three agronomy courses taught at IAV HASSAN II (winter session 2019, 2020, and 2021) were analyzed. Learners are categorized into clusters by their preference level for global/sequential learning styles, using an unsupervised clustering method. Then a classifier model tailored to our requirements was trained and based on the learner's learning style and their current context, a learning object recommendation list is proposed for them. The results revealed that the k-means algorithm performed well in identifying learning styles (LS) and the use of context features defined from the learners' adaptive close environments improved learning performance with an accuracy of over 96% given that most of the learners preferred a global learning style.