Identify Learning Styles Research Articles

Toward a hybrid machine learning approach for extracting and clustering learners' behaviours in adaptive educational system

The learning style is a vital learner's characteristic that must be considered while recommending learning materials. In this paper we have proposed an approach to identify learning styles automatically. The first step of the proposed approach aims to preprocess the data extracted from the log file of the E-learning environment and capture the learners' sequences. The captured learners' sequences were given as an input to the K-means clustering algorithm to group them into sixteen clusters according to the FSLSM. Then the naive Bayes classifier was used to predict the learning style of a student in real time. To perform our approach, we used a real dataset extracted from an e-learning system's log file, and in order to evaluate the performance of the used classifier, the confusion matrix method was used. The obtained results demonstrate that our approach yields excellent results.

Relationship Between Learning Styles, Learning Strategies and Academic Performance of Chinese Students Who Learn Spanish as a Foreign Language

The present study aimed to examine the inherent relationships between learning styles, learning strategies, and academic performance of Chinese students who study Spanish as a foreign language. For this purpose, an adaptation of the Honey-Alonso Learning Styles Questionnaire (CHAEA) and an adaptation of the Strategy Inventory for Language Learning (SILL) questionnaire were used to identify learning styles and strategies, respectively. To measure academic performance, students’ qualifications of the National Examination for Undergraduate Students of Hispanic Philology were used. Our results indicated that the majority of Chinese students who study a foreign language had a combination of the studied styles. Moreover, most of them used all strategies with a medium or high frequency. Significant correlations were found between the learning styles and strategies, and it was confirmed that active style and metacognition strategies are influencing factors in the academic achievement of Chinese foreign language learners.

Estilos de aprendizaje en escolares indígenas del Gran Mallama

Fecha de recepción: 17 de junio de 2019Fecha de revisión: 16 de agosto de 2019Fecha de aprobación: 21 de octubre de 2019
 Objetivo: identificar los estilos de aprendizaje a través de los canales sensoriales de preferencia (visual, auditivo o kinestésico), considerando las características demográficas de los niños y niñas con edades de 8 a 10 años de la Institución Educativa San Juan Bautista de la Salle pertenecientes al grupo indígena de San Miguel Mallama. Metodología: estudio de carácter cuantitativo, de tipo descriptivo, con una muestra no probabilística seleccionada a conveniencia, considerando los criterios de inclusión. Se aplicó una encuesta de demografía y el test de VAK de Jorge Neira. Resultados: con relación a las características demográficas, se encontró que 38,1 % tenía 8 años de edad; el 57,1 % eran de género femenino, y la mayoría (57,1 %) se encontraba en grado tercero. Finalmente, el estilo de aprendizaje fue auditivo (71 %). Conclusiones: es necesario plantear estrategias acordes con los estilos de aprendizaje de preferencia de los escolares, teniendo en cuenta las características sociodemográficas, culturales y sociales.
 Palabras Clave: estilo de aprendizaje, canal sensorial, indígenas, terapia ocupacional, estudiantes, educación.
 Learning styles in indigenous students of Gran Mallama
 Objective: identify learning styles through the sensory channels of preference (visual, auditory or kinesthetic) considering the demographic characteristics of children aged 8 to 10 years of the San Juan Bautista de la Salle Educational Institution belonging to the indigenous group of San Miguel Mallama. Methodology: a quantitative study, of a descriptive nature, with a non-probabilistic sample selected for convenience, considering the inclusion criteria. A demographic survey and the VAK test by Jorge Neira were applied. Results: in relation to demographic characteristics, it was found that 38.1% were 8 years old; 57.1% were female, and the majority (57.1%) were in third grade. Finally, the learning style was auditory (71%). Conclusions: it is necessary to propose strategies in accordance with the student’s preferred learning styles, taking into account sociodemographic, cultural and social characteristics.
 Key words: learning style, sensory channel, indigenous, occupational therapy, students, education.
 Estilos de aprendizagem em estudantes indígenas do Gran Mallama
 Objetivo: identificar estilos de aprendizagem através dos canais sensoriais de preferência (visual, auditivo ou cinestésico), considerando as características demográficas de crianças de 8 a 10 anos da Instituição Educacional San Juan Bautista da Salle, pertencente à grupo indígena de San Miguel Mallama. Metodologia: estudo quantitativo, de natureza descritiva, com amostra não probabilística selecionada por conveniência, considerando os critérios de inclusão. O levantamento demográfico e o teste VAK de Jorge Neira foram aplicados. Resultados: em relação às características demográficas, verificou-se que 38,1% tinham 8 anos, 57,1% eram mulheres e a maioria (57,1%) estava no terceiro ano. Finalmente, o estilo de aprendizagem foi auditivo (71%). Conclusões: é necessário propor estratégias de acordo com os estilos de aprendizagem preferidos do aluno, considerando características sociodemográficas, culturais e sociais.
 Palavras-chave: estilo de aprendizagem, canal sensorial, indígena, terapia ocupacional, estudantes, educação.
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Identifying learning style through eye tracking technology in adaptive learning systems

Learner learning style represents a key principle and core value of the adaptive learning systems (ALS). Moreover, understanding individual learner learning styles is a very good condition for having the best services of resource adaptation. However, the majority of the ALS, which consider learning styles, use questionnaires in order to detect it, whereas this method has a various disadvantages, For example, it is unsuitable for some kinds of respondents, time-consuming to complete, it may be misunderstood by respondent, etc. In the present paper, we propose an approach for automatically detecting learning styles in ALS based on eye tracking technology, because it represents one of the most informative characteristics of gaze behavior. The experimental results showed a high relationship among the Felder-Silverman Learning Style and the eye movements recorded whilst learning.

A Study to Assess the Learning Style Preference of Undergraduate Nursing Students in Southeast, Nigeria

The paradigm shift in education today has placed great emphasis on learning activities rather than teaching activities. Therefore, identification of undergraduate nursing students learning styles preference is necessary for both teachers and students. It enables teaching to be tailored to correlate with the learning styles preference of the students and students can as well be empowered to use the learning style best suited to them to increase their acquisition of knowledge and skills needed in professional nursing. This study identified the predominant learning styles of nursing students in a tertiary institution in the Southeastern part of Nigeria and the relationship between their learning styles and some demographic variables. A descriptive survey method using Kolb's Learning Style Inventory version 3 (K-LSI version 3) was used for the study. The subjects of the study were 310 undergraduate nursing students from 2nd-5th year. The K-LSI identified nursing students ’learning styles as diverger, assimilator, converger and accommodator. An information sheet was used to collect demographic variables. In analyzing the data, frequencies, and percentages were used. Chi-square method was used to test for a relationship between learning style and the students ’demographic variables. There was relatively even spread of the four learning styles: Diverger, 29.0%; Assimilator, 27.4%; Converger, 25.8%, and Accommodator, 17.7%. There was a statistically significant correlation between identified learning styles and mode of admission (P = 0.020) as well as the year of study and learning styles (P =.000). The K-LSI provides nursing students with an understanding on how they might learn best and the divergence of learning styles have shown that a variety of modes of teaching should be used in meeting the learning needs of students for effectiveness and increased academic performance.

Brain quadrant model learning styles

This work was carried out with the interest of identifying learning styles based on the brain quadrant model to improve teaching. Current pedagogical orientations show a growing interest in teaching strategies, which circumvent traditional models, benefiting learning effectively and innovatively. The brain quadrant model helps guide people's personal and professional roles based on existing Sperry models (left and right hemisphere) and the McLean model (cortical and limbic brain). Herrmann crossed these two models to find the source of personal creativity based on the dominance of one or the other quadrant in each person so that they can recognize what would be the effective way to improve teaching. Through explanatory inquiry, cognitive innovation developed in teaching and honing cognitive, affective-social psychomotor skills to achieve different styles of learning and applicability is evaluated in content in daily life.

Enabling Tutors to Identify Learning Style Preferences in Students with Learning Disabilities

Enabling Tutors to Identify Learning Style Preferences in Students with Learning Disabilities

The Importance of Identifying Learning Styles in Medical Education

ABSTRACT Learning is a complex construct that involves several factors, mainly the interaction between teachers and students in the process of teaching and learning. Understanding how students learn and which factors influence academic performance is essential information for lesson planning and evaluation, in addition to allowing a better use of students’ learning potential and outcomes. The ability to constructively modify one’s behavior depends on how well we combine our experiences, reflections, conceptualizations, and planning to make improvements. This seems particularly relevant in medical education, where students are expected to retain, recall, and apply vast amounts of information assimilated throughout their training period. Over the years, there has being a gradual shift in medical education from a passive learning approach to an active learning approach. To support the learning environment, educators need to be aware of the different learning styles of their students to effectively tailor instructional strategies and methods to cater to students’ learning needs. However, the space for reflection on the process of teaching is still incipient in higher-education institutions in Brazil. The present article proposes a critical review of the importance of identifying students’ learning styles in undergraduate medical education. Different models exist for assessing learning styles. Different styles can coexist in equilibrium (multimodal style) or predominate (unimodal style) in the same individual. Assessing students’ learning styles can be a useful tool in education, once it is possible to analyze with what kind of learning students can better develop themselves, improving their knowledge and influencing positively in the process of learning. Over the last century, medical education experienced challenges to improve the learning process and curricular reform. Also, this has resulted in crucial changes in the field of medical education, with a shift from a teacher centered and subject based teaching to the use of interactive, problem based, student centered learning.

Learning Style of Grade V Students At Elementary Schools In Blitar

The purpose of this study is to identify learning styles of grade V students at elementary schools in Blitar. This is a quantitative study using descriptive methods involving grade V students at elementary schools in Blitar Sample of 199 students were selected using simple random sampling. Questionnaires were used to gather the data, and analyzed employing descriptive statistical techniques. Results showed that the auditorial learning style was used dominantly by all female and male students. Keywords: students, teachers, teaching, learning styles.

THE VARK LEARNING STYLE OF NURSING AND MIDWIFERY STUDENTS

Introduction. VARK is very important to facilitate the development of nursing and midwifery students, helping translate theory into practice, and making what is learned in the classroom a reality. It is important to assess the learning style ofstudents to cater for differing learning needs. The aim of the study were: 1) to identify learning style nursing and midwifery students, and 2) the differences of learning style between nursing and midwifery students. Method. A descriptive crosssectional study was performed by using VARK questionnaire version 7.8. A total of 241 students including 125 nursing and 116 midwifery students were participated in this study that selected using stratifi ed random sampling techniques. Data analysis used Chi square test and One Way ANOVA (α=0.05). Result. The study showed that 61.7% and 67.8% nursing and midwifery students in the fi rst year; 70.8% and 63.3% in the second-year were used multimodal learning preferences, respectively. There was no signifi cantly differences learning preferences between the fi rst and the second-year as well as between nursing and midwifery students (p>0.05). Discussion. The nursing and midwifery students have varies on the learning preferences however the majority of the students used multimodal. Therefore, an active learning method was recommended. Keywords: Learning style, Nursing and midwifery students, VARK

Enhancing the Efficiency of Massive Online Learning by Integrating Intelligent Analysis into MOOCs with an Application to Education of Sustainability

Massive Open Online Courses (MOOCs) is an innovative method in modern education, especially important for autonomous study and the sharing of global excellent education resources. However, it is not easy to implement the teaching process according to the specific characters of students by MOOCs because the number of participants is huge and the teacher cannot identify the characters of students through a face to face interaction. As a new subject combined with different areas, such as economics, sociology, environment, and even engineering, the education of sustainability-related courses requires elaborate consideration of individualized teaching for students from diverse backgrounds and with different learning styles. Although the major MOOC platforms or learning management systems (LMSs) have tried lots of efforts in the design of course system and the contents of the courses for sustainability education, the achievements are still unsatisfied, at least the issue of how to effectively take into account the individual characteristics of participants remains unsolved. A hybrid Neural Network (NN) model is proposed in this paper which integrates a Convolutional Neural Networks (CNN) and with a Gated Recurrent Unit (GRU) based Recurrent Neural Networks (RNN) in an effort to detect individual learning style dynamically. The model was trained by learners’ behavior data and applied to predicting their learning styles. With identified learning style for each learner, the power of MOOC platform can be greatly enhanced by being able to offer the capabilities of recommending specific learning path and the relevant contents individually according to their characters. The efficiency of learning can thus be significantly improved. The proposed model was applied to the online study of sustainability-related course based on a MOOC platform with more than 9,400,000 learners. The results revealed that the learners could effectively increase their learning efficiency and quality for the courses when the learning styles are identified, and proper recommendations are made by using our method.

PROFIL KEMAMPUAN PENALARAN MATEMATIS SISWA DITINJAU DARI GAYA BELAJAR

Students learn according to their learning style, and every learning styles are affecting the process of mathematical reasoning and learning outcomes. The combination of how to absorb, manage and process information is the definition of learning styles. To maximize the students' ability to absorb, manage and process information, first identified learning styles of the students are visual, auditory or kinesthetic (V-A-K). This research aims to reveal the profile of mathematical reasoning abilities of students in terms of learning styles visual, audio and kinesthetic on the material function composition and inverse function. This research is a qualitative descriptive approach ethnography and research subject is grade XI of senior high school. The results of the research of profile learning styles (V-A-K) that profile visual students' mathematical reasoning skill, have the ability to manipulate, draw conclusions, giving reasons or evidence is sufficient. While the ability to deliver his argument lacking. Profile auditory students' mathematical reasoning skills, have the ability to manipulate, giving reason or evidence, and provide argument or the validity of the answer is both. While the ability gets conclusion to enough. Profile kinesthetic students' mathematical reasoning skills have the ability to manipulate and give reasons or evidence is sufficient. The ability to draw conclusions while the less, as well as the ability to provide an answer or the validity of the argument, he answered with a unique and clear

Learning Styles and Use of Clinical Knowledge Sources among Junior Doctors

Objective: The primary aim of this study was to explore the use of clinical knowledge sources among junior doctors in relation to their learning styles. The secondary aim was to describe how junior doctors value different qualities of knowledge sources in clinical situations. Methods: A cross-sectional study among 63 junior doctors in a Swedish hospital. A questionnaire was used to collect information about the use of knowledge sources, to identify learning styles according to Kolb and to record the value of qualities of knowledge sources in clinical situations. The 1-Sample Sign Test and the Mann-Whitney test were used to analyse the non-parametric data. Results: The most frequently used knowledge sources were personal contacts with colleagues (n=14, p=0.0002) followed by internet-based guidelines (n=14, p=0.0034) and printed reference literature (n=14, p=0.0225). Learning styles aggregated into two main clusters, converging (54%) and assimilating (30%). Access was the most valued quality of knowledge sources. Conclusions: Although personal contacts were most frequently used, internet-based sources reached almost the same level. Learning styles among junior doctors were in accordance with the findings in previous studies. Convergers seemed to use computer-based knowledge sources less than the assimilators. This needs further investigation. Access should be addressed in the design of knowledge tools since this was overall the most valued quality in clinical situations.

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

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

Learning styles of entrepreneurs in knowledge-intensive industries

PurposeThe purpose of this paper is to explore whether there is a prevalent entrepreneurial learning style trait associated with successful knowledge industry entrepreneurial practice.Design/methodology/approachThe paper reviews prior entrepreneurship studies utilizing experiential learning theory and examines the learning style preferences of 168 knowledge industry entrepreneurs to deduce a hypothesized entrepreneurial learning style. The entrepreneur participants’ Kolb Learning Style Inventory scores are modeled to explore causal links to individual and firm level entrepreneurial success.FindingsPreference for the Kolb Active Experimentation (AE) learning mode over Reflective Observation (RO) predicts adoption of a key entrepreneurial innovation behavior and significant entrepreneurial performance benefits. In contrast to published theories, the RO learning mode exhibits surprising negative effects on entrepreneurial performance. Data analysis also reveals that 90 percent of sampled co-founder/partners had at least one partner with the hypothesized entrepreneurial style.Research limitations/implicationsThe study fills a major research gap in entrepreneurial learning literature by identifying learning style traits associated with entrepreneurial success. The study findings can also be used by educators, practitioners and investors to help identify, appraise and develop entrepreneurial talent.Originality/valueThe study provides novel insights into the learning styles of practicing technology entrepreneurs by establishing a significant preference within this community for the AE and Concrete Experience learning modes. The study illustrates the negative effects of the RO learning mode which has previously linked to successful entrepreneurial practice.

RELATIONSHIP BETWEEN LEARNING STYLES GRAMMAR STUDENTS AND SCHOOL ACHIEVEMENT/ПОВЕЗАНОСТ СТИЛОВА УЧЕЊА УЧЕНИКА ГИМНАЗИЈЕ СА ШКОЛСКИМ УСПЕХОМ

The research focuses on identifying learning styles of students using Experiential Learning Theory. Тhe paper explores the connection between the preferred learning styles of students with academic achievement аnd gender differences. The findings indicate that assimilating learning style most preferred by students. Furthermore, the study shows that there are significant differences in learning styles of students in terms of gender differences of students. The results suggest that within the school context in Serbia such teaching strategies are applied that encourage the development of the assimilating style – lectures through which a number of information systematized in a logical structure are transferred to students. Also, most of the students who have excellent school achievements actually prefer the assimilating learning style. This finding can be explained by the assumption that students develop their own learning styles in accordance with teachers' expectations.

Learning style Identifier: Improving the precision of learning style identification through computational intelligence algorithms

Identifying students’ learning styles has several benefits such as making students aware of their strengths and weaknesses when it comes to learning and the possibility to personalize their learning environment to their learning styles. While there exist learning style questionnaires for identifying a student's learning style, such questionnaires have several disadvantages and therefore, research has been conducted on automatically identifying learning styles from students’ behavior in a learning environment. Current approaches to automatically identify learning styles have an average precision between 66% and 77%, which shows the need for improvements in order to use such automatic approaches reliably in learning environments. In this paper, four computational intelligence algorithms (artificial neural network, genetic algorithm, ant colony system and particle swarm optimization) have been investigated with respect to their potential to improve the precision of automatic learning style identification. Each algorithm was evaluated with data from 75 students. The artificial neural network shows the most promising results with an average precision of 80.7%, followed by particle swarm optimization with an average precision of 79.1%. Improving the precision of automatic learning style identification allows more students to benefit from more accurate information about their learning styles as well as more accurate personalization towards accommodating their learning styles in a learning environment. Furthermore, teachers can have a better understanding of their students and be able to provide more appropriate interventions.

Identifying learning styles.

What was the nature of the CPD activity, practice-related feedback and/or event and/or experience in your practice? The article explored different learning styles and outlined some of the models that can be used to identify them. It discussed the limitations of these models, indicating that although they can be helpful in identifying a student's preferred learning style, this is not 'fixed' and might change over time. Learning is also influenced by other factors, such as culture and age.

IDENTIFIKASI GAYA BELAJAR MAHASISWA

The objective of this study was to identify learning styles of students. The approach used in this study is a quantitative approach with survey method. The variable in this study is a single variable , namely the identification of student learning styles. The study population is students of Guidance and Counseling FKIP Pattimura University totaling 388 people. Samples were used as samples in the amount of 10% of the population of 39 people. The sampling technique using Simple Random Sampling. Learning styles questionnaire prepared based on the opinion of De Poter & Hernacki is used as a data collection tool. Learning styles identified as visual learning style, auditorial learning style, and kinesthetic learning style. Data analysis techniques in this study is the descriptive statistical analysis methods. Based on the survey results revealed that students of guidance and counseling FKIP Pattimura University has a tendency to one learning style. Of the 39 students found that 6 students had a tendency visual learning style, 20 students have a tendency to auditory learning style, 1 student has a tendency kinestheticlearning style, and 12 students have a tendency to mix between learning styles and learning styles visual auditorylearning styles.

Identifying different learning styles to enhance the learning experience.

Identifying your preferred learning style can be a useful way to optimise learning opportunities, and can help learners to recognise their strengths and areas for development in the way that learning takes place. It can also help teachers (educators) to recognise where additional activities are required to ensure the learning experience is robust and effective. There are several models available that may be used to identify learning styles. This article discusses these models and considers their usefulness in healthcare education. Models of teaching styles are also considered.