Learning Style Model Research Articles

Evaluation of suitability, acceptance and use of personalised learning scenarios

The paper aims to present a methodology (i.e. model and method) to evaluate suitability, acceptance and use of personalised learning scenarios. High-quality learning scenarios should consist of the learning components (i.e. learning objects, learning activities, and learning environment) optimised to particular students according to their personal needs, e.g. learning styles. In the paper, optimised learning scenarios mean learning scenarios composed of the components having the highest probabilistic suitability indexes to particular students according to Felder–Silverman learning styles model. Personalised learning scenarios evaluation methodology presented in the paper is based on (1) probabilistic suitability indexes to identify learning components suitability to particular students needs according to their learning styles, and (2) Educational Technology Acceptance & Satisfaction Model (ETAS-M) based on well-known Unified Theory on Acceptance and Use of Technology (UTAUT) model.

Tongue Twister, Students’ Pronunciation Ability, and Learning Styles

In EFL context, considering appropriate technique in teaching pronunciation is a pivotal issue since it could help students to learn how to pronounce English sounds easy. This study aimed to investigate the effect of tongue twister technique on pronunciation ability of students across different learning styles. This study involved 34 first-year English major students taking Intensive English course at Universitas Lambung Mangkurat, one of leading universities in Indonesia. The students in the experimental group were taught by using tongue twister, while those in the control group were taught by using repetition technique. The students were also grouped based on two types of learning styles, namely active and reflective learning styles referring to Felder and Silverman’s (1988) learning style model. The findings of the study showed that there was no significant difference in pronunciation ability between the groups. No significant difference was either found in pronunciation ability between students with active learning style and those with reflective learning style. In spite of the insignificant results, tongue twister is considered beneficial by the students as they perceived that practicing tongue twisters cultivated joyful learning and it helped them to improve their pronunciation, fluency, and motivation in learning English pronunciation. Tongue twister practice could complement the use of repetition technique to enhance students’ learning experience and learning outcome.

Predicting Learning Styles Based on Students’ Learning Behaviour Using Correlation Analysis

Past research has proposed various approaches to automatically detect students' learning styles to address problems associated with traditional research methods (i.e. questionnaire). However, results obtained through traditional research methods have issues in terms of accuracy and precision which need to be addressed. In general, the existing automatic detection approaches are only able to provide satisfactory results for specific learning style models and/or dimensions, or even only work for certain learning management systems. The aim of this study is to propose an automatic detection of learning styles from the analysis of students' learning behaviour by constructing a mathematical model. This study specifically explores the relationship between students' learning behaviour and their learning styles. To investigate this relationship, a pilot experiment was conducted with 33 students. The students used Moodle platform, a learning management system, as supplementary online learning material for Java programing. The students' learning behaviour was tracked and recorded. Thirty students' data (i.e. their learning behaviour and learning styles; measured using the Index of Learning Styles (ILS) instrument) were analysed using the proposed correlation analysis to identify the relationship. The remaining three students' learning behaviour data were used to predict their learning styles. The findings are discussed with regard to accuracy of automatic detection of learning styles using the ILS instrument.

The influence of learning style on satisfaction and learning effectiveness in the asynchronous web-based learning system

PurposeThe purpose of this paper is to investigate the effect of user’s learning style (including accommodators, divergers, convergers, and assimilators) on user’s satisfaction on the web-based learning system and their learning effectiveness.Design/methodology/approachThis experimental research used the college students from a technology institute in Taiwan as the subject sources. By using the Kolb’s learning style model, the students are classified as four types of learners: convergers, divergers, assimilators, and accommodators. The authors analyzed the relationships among the different learning styles with their effectiveness of learning and satisfaction of using the web-based learning system. The mediation effect of gender is also presented.FindingsThis research indicates that: first, the satisfaction of the web-based learning system has significant influence on the learning performance of learners; second, different learning styles learners have no significant effect to the satisfaction on using the web-based learning system; third, learning effectiveness has significant difference among different learning style learners on the web-based learning system; the learning effectiveness of accommodators and divergers was significantly higher than the assimilators; fourth, different learning styles learners show significant difference in gender proportion. In addition to accommodators, whose proportion of women is higher than men, the other three learning styles’ proportions in men are higher than women.Research limitations/implicationsThis study was grounded in the Kolb’s learning style theory. The authors provide implications for academic studies in e-learning research stream that aimed at understanding the role of learning style as well as gender differences in the asynchronous web-based learning system.Practical implicationsResults from this study provided the implications for students, educators, and e-learning system designers. The design of teaching materials as well as functions of e-learning systems should take learners’ learning style into consideration to ensure the best learning outcome.Originality/valueThis study examined the students’ learning style as well as gender differences in the asynchronous web-based learning system. An experiment was conducted to ensure the data were collected in a controlled environment, thus, offer the value that most of the prior study lacks.

Adaptation with Four Dimensional Personalization Criteria Based on Felder Silverman Model

In the past decades, various systems have been proposed to provide students with a better learning environment by taking personal factors into account. Learning styles have been one of the widely adopted factors in the previous studies as a reference for adapting learning content or organizing the content. However, very few researchers give an idea of matching e-media with appropriate teaching and learning styles and very few studies give an idea of which appropriate combinations of electronic media and learning styles are more effective than other. In this paper, the authors aim to prototype an AFDPC-FS system (Adaptation with Four Dimensional Personalization Criteria based on Felder Silverman model). Their system presents a general framework for combining and adapting teaching strategies, learning styles and electronic media according to Felder-Silverman's learning style model. An experiment was designed to explore the effect of adaptation to different learning styles when learning materials were matched with learning styles. In particular it was set up to see whether there are significant differences in learning achievement and cognitive load between two groups, an experimental group who studied with learning style-fit version and a control group who studied with non-fit version of the system without adaptation to learning styles. The experimental results showed that the proposed system could improve the learning achievements of the students. Moreover, it was found that the students' cognitive load was significantly decreased.

Behavioral Tracking in E-Learning by Using Learning Styles Approach

Currently, e-learning is becoming an option as it can save the cost of education, time, and more flexible in its implementation. The main problem that arises is how to create e-learning content that is interesting and really fit the needs of the users. One way that can be done to optimize the content of e-learning is to analyze the user behavior. This study aims to analyze user (student) behavior in KALAM UMP, based on logs report (activity history), which is often called as behavioral tracking. First, the learning style of the students is determined based on Honey and Mumford Learning Styles Model by using Learning Styles Questionnaire. The analysis is done using SPSS 16.0 for Windows. The results shows that student with Reflector and Theorist learning styles access e-learning materials the most. From Spearman Correlation analysis, the relationship between learning styles and students’ behavior in e-learning is found to be very weak (r<sub>s</sub>=.276, p=.000), but statistically significant (p<0.05). In other words, students’ learning styles and behavior in e-learning have significant impacts on the improvement or degradation of students’ performance. Therefore, from the results of this study, an adaptive KALAM e-learning system which can suits the learning styles of UMP students is proposed. In adaptive e-learning system, students can access learning materials that match the students' learning needs and preferences.

Enhancing e-learning systems with personalized recommendation based on collaborative tagging techniques

Personalization of the e-learning systems according to the learner’s needs and knowledge level presents the key element in a learning process. E-learning systems with personalized recommendations should adapt the learning experience according to the goals of the individual learner. Aiming to facilitate personalization of a learning content, various kinds of techniques can be applied. Collaborative and social tagging techniques could be useful for enhancing recommendation of learning resources. In this paper, we analyze the suitability of different techniques for applying tag-based recommendations in e-learning environments. The most appropriate model ranking, based on tensor factorization technique, has been modified to gain the most efficient recommendation results. We propose reducing tag space with clustering technique based on learning style model, in order to improve execution time and decrease memory requirements, while preserving the quality of the recommendations. Such reduced model for providing tag-based recommendations has been used and evaluated in a programming tutoring system.

Relationship between the Learning Styles Preferences and Academic Achievement

The individual learning differences that have been much explored relate to differences in personality, learning styles, strategies and conceptual of learning. This article studies the learning style profile exhibited by students towards the academic achievement in Malaysian Polytechnic. The relationship between learning styles of Polytechnic students and their academic achievement based on VARK learning styles model. The target population was international business students of Malaysian Polytechnic. By means of randomly sampling method, 103 students were selected as sample of research. By descriptive - survey research method and a questionnaire adapted from VARK Learning Style Index, required data were collected. According to the results, no significantly difference between learning style and academic achievement of students. Students academic achievement was quite similar to their individual learning styles. These facts reveal that each learning style has its own strengths and weaknesses.

LEARNING STYLES AS A TOOL FOR THE EDUCATION OF CHEMICALENGINEERS

Adaptive and flexible learning environments are expected to be a critical strategic option for the competitiveness ofeducational institutes in the 21stcentury. In a multicultural society, as that of the European Union, as well as in global(open) educational systems, students have different backgrounds, levels of motivation, attitudes about learning, as wellas preferences and approaches to studying. Therefore, there is a need for educators to identify the factors that supportthe shift from lecture-based teaching to active learning strategies with such variable class audiences. Oneof the mostcritical factors is the identification of the students’ learning styles.This paper focuses on research models of learning styles and comments on the necessity, importance, and some positiveand negative elements of various related approaches.In particular we assess the profile of a class of ChemicalEngineering students who formed our research case study, through a pilot application of one of the approaches. Ourtarget group was formed by the participants in the elective course “Topics of Information and Knowledge Society”,offered at the 2ndsemester of the undergraduate curriculum of the School of Chemical Engineering at the NationalTechnical University of Athens (NTUA).The ultimate purpose of the overall approach was to improve theteaching methodology, course material, andcommunication channels by indexing the students’learning styles in order for the course to be more interesting and usefulto them.

Improvement of Requirement Elicitation Process through Cognitive Psychology

Proper requirement elicitation is necessary for client satisfaction along with the overall project success, but requirement engineers face problems in understanding user requirements and the users of the required system fail to make requirement engineering team understand what they actually want. It is then responsibility of requirement engineers to extract proper requirements. This paper discusses how to use cognitive psychology and learning style models (LSM) to understand the psychology of clients. Moreover, it also discusses usage of proper elicitation technique according to one’s learning style and gather the right requirements.

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.

El estilo de aprendizaje en educación virtual: breve revisión de la literatura

La enseñanza a través de los años se ha transformado increíblemente, hasta llegar al punto de estudiar las características del cerebro para poder comprender su complejidad y potenciar el aprendizaje; esto motivó la presente investigación de los modelos más representativos que existen en el cerebro humano y las distintas formas y estilos de aprendizaje. Al analizarlos pudimos observar lo importante que es conocer al estudiante, sus capacidades y sus habilidades cognitivas. El modelo de estilos de aprendizaje propuesto por Ned Hermann nos lleva a conocer las capacidades y forma de absorber información de cada estudiante y, en este sentido, a generar mejoras para que el proceso de aprendizaje sea eficaz en un ambiente de aprendizaje lleno de retos: el virtual.

On the way to learning style models integration: a Learner's Characteristics Ontology

On the way to increasing customization in e-learning systems, the learner model is the main source of variability. Such a model includes a number of psychological characteristics and study preferences that describe the learner's personality traits related to learning. During the last decades, the design methods and tools for e-learning have been designed assuming specific learner models. Therefore, in the search for a learning environment suitable for as many learner models as possible, we need tools to explore -and exploit- such models. In general, the learner's characteristics can be linked to the so-called learner's learning style (which is a part of the learner model) to provide the instructor with extensive knowledge about the learner's characterization in perceiving and processing information. Numerous learning styles have been proposed in the last decades, in some cases with overlapping characteristics with the same or different names. Thus, the heterogeneity of the learning style space makes it difficult to handle customization effectively. In this paper, we introduce a Learner's Characteristics Ontology based on creating interconnections between the different learning style model dimensions and learning styles with the relevant learner's characteristics, that: (1) helps instructors to improve and personalize the learning content; (2) can recommend learning materials to learners according to their learning characteristics and preferences; (3) can provide both instructors and learners with extensive knowledge about how they can improve their teaching and learning abilities; and (4) can improve communications and interaction between humans and computers by specifying the semantics of the learning style models' characteristics.

Comparing Science Virtual and Paper-Based Test to Measure Students’ Critical Thinking based on VAK Learning Style Model

This research was comparing virtual and paper-based test to measure students’ critical thinking based on VAK (Visual-Auditory-Kynesthetic) learning style model. Quasi experiment method with one group post-test only design is applied in this research in order to analyze the data. There was 40 eight grade students at one of public junior high school in Bandung becoming the sample in this research. The quantitative data was obtained through 26 questions about living thing and environment sustainability which is constructed based on the eight elements of critical thinking and be provided in the form of virtual and paper-based test. Based on analysis of the result, it is shown that within visual, auditory, and kinesthetic were not significantly difference in virtual and paper-based test. Besides, all result was supported by quistionnaire about students’ respond on virtual test which shows 3.47 in the scale of 4. Means that student showed positive respond in all aspet measured, which are interest, impression, and expectation.

Tongue Twister, Studentss Pronunciation Ability, and Learning Styles

In EFL context, considering appropriate technique in teaching pronunciation is a pivotal issue since it could help students to learn how to pronounce English sounds easy. This study aimed to investigate the effect of tongue twister technique on pronunciation ability of students across different learning styles. This study involved 34 first-year English major students taking Intensive English course at Universitas Lambung Mangkurat, one of leading universities in Indonesia. The students in the experimental group were taught by using tongue twister, while those in the control group were taught by using repetition technique. The students were also grouped based on two types of learning styles, namely active and reflective learning styles referring to Felder and Silverman’s (1988) learning style model. The findings of the study showed that there was no significant difference in pronunciation ability between the groups. No significant difference was either found in pronunciation ability between students with active learning style and those with reflective learning style. In spite of the insignificant results, tongue twister is considered beneficial by the students as they perceived that practicing tongue twisters cultivated joyful learning and it helped them to improve their pronunciation, fluency, and motivation in learning English pronunciation. Tongue twister practice could complement the use of repetition technique to enhance students’ learning experience and learning outcome.

The Learning Preferences among Nursing Students in the King Saud University in Saudi Arabia: A Cross-Sectional Survey

Objective The present study aimed to identify the most common learning preferences among the nursing students in Saudi Arabia and to investigate the associations of certain demographic variables with the learning preferences. Methods All the undergraduate nursing students in the nursing college were requested to participate in this descriptive cross-sectional study. An Arabic version of the Felder-Silverman learning style model (FSLSM) questionnaire was used to examine the learning preferences among undergraduate nursing students. Results A total of 56 (43%) completed questionnaires were included in the final analysis. Results of the present study indicate that the most common learning preferences among the nursing students were visual (67.9%), followed by active (50%) and sequential (37.5%) learning preferences. The verbal style was the least common learning preference (3.6%) among the nursing students. There was no association between gender and learning preferences (p > .05). Conclusion The present study concluded that the visual, active, and sequential styles are the commonest learning preferences among the nursing students. The nursing educators should emphasize the use of this information in their teaching methods to improve learning skills among the nursing students.

Index of mathematics learning activities suitability to learners

The paper aims to present an original probabilistic method to creating personalised learning activities and units. The method is based on evaluating learning components suitability to students needs according to their learning styles. In the paper, Felder–Silverman Learning Styles Model and Inquiry Based Learning method suitable to learn Math are used. Expert evaluation method based on Trapezoidal Fuzzy Numbers theory is applied in the research to obtain numerical values of suitability of IBL activities to students learning styles. Personalised learning units should consist of learning components (i.e. learning activities) that are optimal (i.e. the most suitable) to particular students according to their learning styles. Optimal means having the highest suitability index. In the paper, this original probabilistic method is applied to establish probabilistic suitability of IBL activities to particular students.

Learning Object Retrieval and Aggregation Based on Learning Styles

The main goal of this article is to develop a Management System for Merging Learning Objects (msMLO), which offers an approach that retrieves learning objects (LOs) based on students’ learning styles and term-based queries, which produces a new outcome with a better score. The msMLO faces the task of retrieving LOs via two steps: The first step ranks LOs using a unified learning style model and creates better LOs by merging the top-ranked LOs. The second step maps LOs onto a hierarchy of concepts to avoid duplicated topics. An experiment was conducted to evaluate this approach in an applied computing course. A total of 84 students were randomly split into four groups. The experimental results demonstrated that the msMLO is a promising approach that provides useful LOs based on students’ learning styles and the merging process for reusing stored LOs. Furthermore, this approach improves overall student learning performance and reduces the number of LOs reviewed.

A hybrid approach for supporting adaptivity in e-learning environments

Purpose The purpose of this paper is to identify a framework to support adaptivity in e-learning environments. The framework reflects a novel hybrid approach incorporating the concept of the event-condition-action (ECA) model and intelligent agents. Moreover, a system prototype is developed reflecting the hybrid approach to supporting adaptivity in any given learning management system based on learners’ learning styles. Design/methodology/approach This paper offers a brief review of current frameworks and systems to support adaptivity in e-learning environments. A framework to support adaptivity is designed and discussed, reflecting the hybrid approach in detail. A system prototype is developed incorporating different adaptive features based on the Felder-Silverman learning styles model. Finally, the prototype is implemented in Moodle. Findings The system prototype supports real-time adaptivity in any given learning management system based on learners’ learning styles. It can deal with any type of content provided by course designers and instructors in the learning management system. Moreover, it can support adaptivity at both course and learner levels. Originality/value To the best of the authors’ knowledge, no previous work has been done incorporating the concept of the ECA model and intelligent agents as hybrid architecture to support adaptivity in e-learning environments. The system prototype has wider applicability and can be adapted to support different types of adaptivity.

Clustering learner profiles based on usage data in adaptive e-learning

Adaptive e-learning systems enhance the efficiency of online education by providing personalised, adaptive contents and user interfaces which change with respect to learner's requirements. In order to understand the learner's requirements, learners with similar learning behaviour have to be grouped into clusters based on the usage data of each learner. In this paper, a clustering technique to group learner's profiles is proposed where learners will be grouped based on similar sequences of accesses to learning material and time spent. A learner's model is designed based on Felder and Silverman learning style model. The clustering algorithm has two different phases, where the first phase considers the all sequences of access of learners which are in the chronological order of accessing the learning components and learning materials on the portal. The second phase considers the time spent on each learning components as a fuzzy membership function and groups the similar sequences of learners into three clusters. Learners in the clusters have similar learning behaviour for providing adaptive interfaces and contents.