Identify Learning Styles Research Articles

Learning styles According to the VARK Model among Intermediate Female School Students

The aim is to identify learning styles according to the VARK model among intermediate female school students. To achieve the objectives of the research, the descriptive method was used. The research sample consisted of 525 female students from morning intermediate and secondary schools for girls in the Al-Majr district, which is part of the General Directorate of Education in Maysan Governorate/Amarah. The research tool, consisting of 28 items, was prepared. The results showed that the research sample preferred the visual style, which ranked first and was the most used when studying mathematics. Regarding their use of VARK model learning styles according to the grade level variable, it was found that there is a difference. The result for the visual style indicated a significance in favor of the first grade. The verbal style showed a significance in favor of the second grade. The kinesthetic style showed a significance in favor of the third grade. However, for the auditory style, no difference was found according to the grade level.

Investigating the Impact of Machine Learning in Personalized Education Systems

Personalized education systems leverage robust capabilities of machine learning (ML). Tailoring learning experiences to individual needs preferences and abilities. This survey explores integration of ML in personalized education. It examines methodologies. It highlights applications impacts Future directions are discussed. Key ML techniques include supervised learning for performance prediction and feedback personalization. Unsupervised learning for identifying learning styles. Reinforcement learning.Personalized education systems leverage robust capabilities of machine learning (ML). Tailoring learning experiences to individual needs preferences and abilities. This survey explores integration of ML in personalized education. It examines methodologies. Highlighting applications. Impacts and future directions are discussed. Key ML techniques include supervised learning for performance prediction, feedback personalization unsupervised learning for identifying learning styles, reinforcement learning. Optimizing content sequences. Deep learning for tasks like automated grading. Intelligent tutoring included. Applications such as intelligent tutoring systems. Adaptive learning platforms. Learning analytics. Personalized learning paths and automated assessment systems discussed. The impact of ML-driven personalized education systems is profound. They enhance engagement. Motivation. Academic performance and reduce dropout rates. Challenges such as data privacy algorithmic bias, scalability and resistance to change are also addressed. Future research directions include interdisciplinary collaboration. Advancements in natural language processing are essential. Enhanced personalization algorithms are needed. Long-term studies. Considered crucial. Areas for ongoing work. Key Words: Machine learning, Personalized education, Adaptive learning, Intelligent tutoring systems, Predictive analytics, Recommendation systems, Ethical considerations, Student engagement, Educational technology

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.

Identifying learning styles in MOOCs environment through machine learning predictive modeling

Identifying learning styles in MOOCs environment through machine learning predictive modeling

From Self-Awareness/Perceptions to Future Directions: Analyzing Learning Styles and Attitudes of University Students

When identifying learning styles, attitudes, and self-awareness/perceptions for future directions in mastering English language skills, it is important to take into account psychological and sociolinguistic elements as well as other external factors that can impact students’ abilities and understanding. This study employed a Likert-based survey approach to examine significant challenges encountered by students, and the quantitative and qualitative findings were analyzed and interpreted by the professors. The researchers were able to observe that visual aids, class participation, positive attitudes in academic activities, positive attitudes towards other students' contributions and non-verbal reactions, and being team players are important factors in achieving success in the classroom. It was evident that students showed moderate performance in these areas. However, the researchers, who are an English language educator and a psychologist, still view the students' individual and group evaluation feedback as a way to create support methods for boosting student engagement and personal development, ultimately leading to higher ratings, and for encouraging reflective practice among professors.

The learning styles of Australian exercise science and clinical exercise physiology students

ABSTRACT There has been a shift in tertiary education, whereby large portions of theory are now taught online, and the practical skills underpinned by this knowledge taught in clinical environments. It is unclear if this flipped approach is appropriate for the learning styles Exercise Science (ES) and Clinical Exercise Physiology (CEP) students. First- and second-year ES and CEP students at a South Australian metropolitan university were invited to participate between March 2021 and October 2022. Eligible participants completed the Kolb’s Learning Style Inventory (KLSI) (Version 3.1) to identify learning styles. Descriptive statistics were calculated for the four KLSI learning orientations. Group scores for the learning orientations were used to identify group preference for abstractness over concreteness and action over reflection. A total of 69 students completed the KLSI fully. As a single cohort, Accommodators were the most common learning style, and there was a preference for concreteness over abstractness when gaining experience and a preference for action over reflection when transferring experience into knowledge. These findings indicate that Australian ES and CEP students have a unique learning style that may be suited to flipped classroom approaches, but only if they include opportunity to experience skill learning in a hands-on manner, while also allowing time for reflection.

EXPLORING PREFERRED STYLES OF LEARNING AND TEACHING AMONG COLLEGE STUDENTS IN VIEW OF NEW EDUCATION POLICY 2020

With the advent of New Education Policy (NEP) 2020, India has a vision to transform its education system by shifting its attention to learner-centred approach for holistic understanding of subjects using different styles of teaching and learning like activity-based learning and teaching in one’s own mother tongue. Students use different modalities to perceive, understand and learn different subjects taught in class. Based on the modalities used (visual, auditory or tactile), students may develop visual, auditory or kinesthetic styles of learning. This study aimed to identify learning styles and preferred teaching styles of current college students between the ages 18-25. In addition, the study seeks to understand the preferred teaching style opted by students. A comparison in terms gender, and educational level were made. Through convenient sampling technique willing participants were administered Barsch Learning Styles Inventory (Barsch, J. 1980) and were asked to rank various teaching styles like visual, auditory and kinesthetic in the order of their preference. Appropriate statistics were used for data analysis. The results of the study showed that college student’s preferences for learning styles were not influenced by gender or educational level. The order of preference of students in terms of preferred teaching style was found to be visual followed by auditory followed by tactile.

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.

COMPARISON OF NAIVE BAYES, DECISION TREE, AND RANDOM FOREST ALGORITHMS IN CLASSIFYING LEARNING STYLES OF UNIVERSITAS KRISTEN INDONESIA TORAJA STUDENTS

Learning style is an individual's habit or way of absorbing, processing, and managing information. This factor is very important in achieving learning goals. However, in reality, learning styles are often overlooked in the learning process, which can lead to suboptimal absorption of lessons and affect the quality of education. Various models have been developed by educational experts to identify students' learning styles, one of which is the VAK model (Visualization Auditory Kinesthetic) for grouping learning styles. This study compares algorithms in classifying learning styles using the VAK model. The results showed that the most dominant learning style was kinesthetic with a percentage of 46.9% or 478 students. The algorithm modeling showed that Naive Bayes had the highest accuracy with a value of 75%, while Random Forest had the lowest accuracy with a value of 59%. This suggests that Naive Bayes is more suitable for classifying students' learning styles. In conclusion, understanding students' learning styles is crucial for effective education. The VAK model is one way to identify learning styles, and Naive Bayes is a suitable algorithm for classifying students' learning styles. By considering learning styles, educators can tailor their teaching methods to better suit their students' needs and improve the quality of education.

Identification of learning styles in agricultural producers of the productive sectors in the department of Caquetá

The agricultural sector in the department of Caquetá is crucial for the regional economy and food security. Understanding the learning styles of agricultural producers is fundamental for designing effective pedagogical strategies. According to Honey and Mumford (1968), learning styles refer to individual preferences in the acquisition and processing of information. Identifying them makes it possible to adapt teaching methods to maximize their effectiveness. Previous research, such as that of Smith (2012), highlights that knowing the learning styles of producers can avoid barriers in the educational process, improving the acquisition of technical knowledge. Brown (2015) suggests that educational strategies appropriate to these styles can foster the transfer of knowledge to daily practices, improving livestock productivity and sustainability. This study explores learning styles in the agricultural sector of Caquetá, with the objective of designing training strategies adapted to the needs of producers. The findings could strengthen agricultural production and foster more sustainable development. Recent data indicate that crop and livestock production occupies vast tracts of land in countries around the world. In Caquetá, the production of sugarcane, cacao, bananas, and cattle is significant. However, these sectors face challenges such as access to financing, climate change, obsolete production techniques, and competition in international markets. The study was conducted in the municipalities of Albania, San José del Fragua, Belén de los Andaquíes, Doncello and San Vicente del Caguán. The methodology includes surveys and interviews to identify learning styles of approximately 600 producers. The research seeks to provide specific recommendations to improve pedagogical practices in the agricultural sector of Caquetá, considering the diversity of learning styles. Understanding and adapting to these styles not only promotes the effectiveness of educational programs, but also strengthens the resilience and sustainability of agricultural practices in the region, contributing to sustainable agricultural 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.

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.

Personalized Adaptive Learning Technologies Based on Machine Learning Techniques to Identify Learning Styles: A Systematic Literature Review

Personalized Adaptive Learning Technologies Based on Machine Learning Techniques to Identify Learning Styles: A Systematic Literature Review

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%.

Improving online education through automatic learning style identification using a multi-step architecture with ant colony system and artificial neural networks

Learning style is one of the individual differences which play an important role in learning. Being aware of it helps the student to understand their strengths and weaknesses, and the teacher to provide more valuable personalized interventions. Furthermore, learning style-based adaptive educational systems can be designed, which have been shown to increase student satisfaction or learning gain, while reducing the time needed to learn. It is therefore important to have an accurate method for identifying students’ learning styles. Since the traditional approach of filling in dedicated psychological questionnaires has several disadvantages, automatic methods have been proposed, based on investigating student observable behavior in a learning environment. Research done so far generally takes a mono-algorithmic approach to identify learning styles, and the precision rates leave room for improvement. Hence, in this paper we propose a novel hybrid multi-step architecture based on ant colony system and artificial neural networks to increase the precision of learning styles identification. Two different variants are proposed and evaluated with data from 75 students; results show high precision values, outperforming existing automatic approaches for learning style identification. The proposed architecture can be integrated into widely used educational systems (e.g., learning management systems) to provide learners and/or teachers with information about students’ learning styles. In addition, it can be integrated into adaptive educational systems and plugins of learning management systems to automatically identify learning styles and personalize instruction respectively.

Student Behaviour Analysis To Detect Learning Styles Using Decision Tree, Naïve Bayes, And K-Nearest Neighbor Method In Moodle Learning Management System

A learning management system (LMS) manages online learning and facilitates inter- action in the teaching and learning processes. Teachers can use LMS to determine student activities or interactions with their courses. Everyone learns uniquely. It is necessary to understand their learning style to apply it in students’ learning activi- ties. One factor contributing to learning success is the use of an appropriate learning style, which allows the information received to be appropriately conveyed and clearly understood. As a result, we require a mechanism to identify learning styles. This study develops a learning style detection system based on learning behavior at the LMS of Christian Vocational School Petra Surabaya for the subject of Network System Administration using the Decision Tree, Naïve Bayes, and K-Nearest Neigh- bor. The results of the study showed that the Decision Tree method could better detect and predict learning styles, namely using the 80:20 train split test, which obtained an accuracy of 0.96 process time of 0.000998 seconds, while the K-Fold 10 Cross-Validation test obtained an accuracy of 0.98 and a processing time of 0.04033 seconds.

Clustering Of Student Learning Styles in the industri 4.0 Using KMeans Algorithm

Clustering is a technique for grouping homogeneous data so that the points in each cluster are as similar as possible according to convenience measures such as Euclidean-based distance or correlation-based distance. In the industrial era 4.0, learning media, the environment, the way teachers teach will affect student learning styles. From research on learning styles, many researchers agree on the importance of identifying learning styles to accelerate their learning performance. The purpose of this study is to classify student learning styles in the industrial era 4.0 using the Kmeans algorithm and the elbow method. The research method used is a waterfall. The number of research subjects was 108 students. the results of the research on the number of clusters (K), namely 6, obtained cluster 1 as many as 27 students, cluster 2 as many as 24 students, cluster 3 as many as 21 students, cluster 4 as many as 17 students, cluster 5 as many as 11 students and cluster 6 as many as 8 students. The performance of the grouping results based on the silhouette coefficient is 0.302, which means the grouping structure is weak. In cluster 1, the highest number has auditory elements, followed by kinesthetic and visual elements. The development of ICT-based media is one of the factors of student learning styles in the industrial era 4.0

A study oriented to significant learning: Learning styles

Education is a priority axis for the government of our country, Mexico. The teaching-learning process is a challenge in each classroom, its importance lies mainly in raising the productivity of society. In this context, the application of pedagogical approaches and environments that allow meaningful learning are sought. For its part, we identify learning styles as cognitive, physiological and affective traits, of how students perceive, interact and respond to different learning environments. The objective of this research is to analyze and evaluate learning styles as an indicator that serves as a starting point to distinguish the way in which students learn and guide the teaching act in the design of learning strategies according to a segmentation of students of according to their learning styles or their predominance to achieve meaningful learning. The study proposes a field investigation using the Honey Alonso Learning Styles Questionnaire (CHAEA) as a data collection instrument. The methodological process of the research was divided into two phases: the first covers the design and application of the CHAEA instrument. In the second, the results obtained by means of a frequency analysis are exhibited.