Using students’ learning style to create effective learning groups in MCSCL environments

Students have different ways for learning and processing information. Some students prefer learning through seeing while others prefer learning through listening; some students prefer doing activities while other prefer reflecting.Some students reason logically, while others reason intuitively, etc. Identifying the learning style of each student, and providing learning content based on these styles represents a good method to enhance the learning quality. However, there are no efforts onhow to detect the students’ learning styles in mobile computer supported collaborative learning (MCSCL) environments. We present in this paper new ways for automatically detecting the learning styles of students in MCSCL environments based on the learning style model of Felder-Silverman. The identified learning styles of students could be then stored and used at anytime toassign each one of them to his/her appropriate learning group

The influence of collaborative learning style, reciprocity and extroversion on knowledge sharing via social media among UUM undergraduates

This quantitative study is aimed to study the relationship between collaborative learning style, reciprocity and extroversion on knowledge sharing via social media in Universiti Utara Malaysia (UUM). A total of 400 set of questionnaires were distributed to undergraduate students from three (3) academic colleges which are College of Business (COB), College of Arts and Sciences (CAS) and College of Law, Government and International Studies (COLGIS). However, only 363 set of questionnaires were return and usable for analysis. Regression analysis was performed to tests the hypotheses of the study. The result indicated that collaborative learning, reciprocity and extroversion were positively significant to knowledge sharing behavior via social media. The findings were discussed and recommendations for the future research were also addressed

Integrated Stochastic and Literate Based Driven Approaches in Learning Style Identification for Personalized E-Learning Purpose

This paper presents integrated stochastic and literate based driven approaches in learning style identification for personalized e-learning purpose. Shifting a paradigm in education from teacher learning to student learning center has encouraged that learning should follow and tailor learners’ characteristics in the form of personalized e-learning. There are several aspects to describe a condition of learners such as prior knowledge, learning goals, learning styles, cognitive ability, learning interest, and motivation. Even though, in many studies of the personalized e-learning, the learning style plays a significant role. In terms of e-learning, implementing several methods for identifying learner style becomes more challenging. Artificial intelligence and machine learning method give good accuracy, but they still have some issues in computation. Additionally, the stationary method is very hard to represent non-deterministic and dynamic data. Therefore, this research proposes the learning style identification by integrating stochastic and literate based driven approaches. Hidden Markov Model (HMM) and the Naïve Bayes as the Stochastic Approach have been implemented. Subsequently, learner behavior as the literate based data is used to get hints during accessing the learning objects. The proposed model has been implemented to VARK learning style. The accuracy is calculated by comparing the model results with the questionnaire results. When Using the HMM, the proposed model gives accuracy in the range of 95% up to 96.67%. Additionally, when using the Naïve Bayes; the accuracy is 93.33%. The results give better accuracy compared to previous studies. In conclusion, the proposed model is promising for modeling learner style in personalized e-learning

USING STUDENT MENTAL STATE AND LEARNING SENSORY MODALITIES TO IMPROVE ADAPTIVITY IN E-LEARNING

  In this paper, we present an innovative solution to improve adaptivity in an e-learning system using Brain Computer Interface (BCI) measures (Attention/Meditation) in order to detect changes in students’ preferred perceptual modes for learning information (VARK model). Our solution is also able to report course units and learning resources that could be difficult for the students

An Experimental Study of Learning Behaviour in an ELearning Environment

To reach an adaptive eLearning course, it is crucial to control and monitor the student behaviour dynamically to implicitly diagnose the student learning style. Eye tracing can serve that purpose by investigate the gaze data behaviour to the learning content. In this study, we conduct an eye tracking experiment to analyse the student pattern of behaviour to output his learning style as an aspect of personalisation in an eLearning course. We use the electroencephalography EEG Epoc that reflects users emotions to improve our result with more accurate data. Our objective is to test the hypothesis whether the verbal and visual learning Styles reflect actual preferences according to Felder and Silverman Learning Style Model in an eLearning environment. Another objective is to use the outcome presented in this experiment as the starting point for further exhaustive experiments. In this paper, we present the actual state of our experiment, conclusions, and plans for future development

Model detecting learning styles with artificial neural network

Currently the detection of learning styles from the external aspect has not produced optimal results. This research tries to solve the problem by using an internal approach. The internal approach is one that derives from the personality of the learner. One of the personality traits that each learner possesses is prior knowledge. This research starts with the prior knowledge generation process using the Latent Semantic Indexing (LSI) method. LSI is a technique using Singular Value Decomposition (SVD) to find meaning in a sentence. LSI works to generate the prior knowledge of each learner. After the prior knowledge is raised, then one can predict learning style using the artificial neural network (ANN) method. The results of this study are more accurate than the results of detection conducted with an external approach

State of the art of learning styles-based adaptive educational hypermedia systems (Ls-Baehss)

The notion that learning can be enhanced when a teaching approach matches a learner’s learning style has been widely accepted in classroom settings since the latter represents a predictor of student’s attitude and preferences. As such, the traditional approach of ‘one-size-fits-all’ as may be applied to teaching delivery in Educational Hypermedia Systems (EHSs) has to be changed with an approach that responds to users’ needs by exploiting their individual differences. However, establishing and implementing reliable approaches for matching the teaching delivery and modalities to learning styles still represents an innovation challenge which has to be tackled. In this paper, seventy six studies are objectively analysed for several goals. In order to reveal the value of integrating learning styles in EHSs, different perspectives in this context are discussed. Identifying the most effective learning style models as incorporated within AEHSs. Investigating the effectiveness of different approaches for modelling students’ individual learning traits is another goal of this study. Thus, the paper highlights a number of theoretical and technical issues of LS-BAEHSs to serve as a comprehensive guidance for researchers who interest in this area

AVALIAÇÃO DA UTILIZAÇÃO DE RECURSOS DE ENSINO ON-LINE RELACIONADOS A DIFERENTES ESTILOS DE APRENDIZAGEM

O presente trabalho apresenta os resultados de uma pesquisa que objetivou a criação de recursos on-line para apoio ao ensino de uma disciplina presencial de cursos de engenharia, com foco na criação de objetos de aprendizagem que pudessem atender aos diferentes estilos de aprendizagem dos estudantes. A partir da identificação dos estilos de aprendizagem de cada aluno, realizou-se a avaliação do seu comportamento com relação aos diferentes objetos. Mais do que avaliar o comportamento dos estudantes, o principal foco do trabalho foi desenvolver recursos de ensino que pudessem responder aos diferentes perfis de aprendizagem, de modo a contribuir para a melhoria do processo de ensino-aprendizagem

Considerando Estilos de Aprendizagem, Emoções e Personalidade em Informática na Educação

Muitas vezes tratam-se turmas homogeneamente e as TDIC (Tecnologias Digitais de Informação e Comunicação) são utilizadas pelos alunos desconsiderando que eles possuem particularidades (tais como: estilos de aprendizagem, emoções e traços de personalidade) que influenciam em sua aquisição de conhecimento. Neste trabalho, são apresentados conceitos e reflexões visando incentivar discussões e estudos teóricos ou empíricos que considerem essas particularidade dos alunos durante o processo de ensino e aprendizagem. É apresentada também uma breve análise de dados de alunos de computação, em diferentes níveis de ensino, os quais responderam a questionários visando identificar perfis referentes a seus estilos de aprendizagem e a seus traços de personalidade. A análise corrobora com a ideia da heterogeneidade de perfis, mesmo considerando alunos da mesma área/nível de ensino. Percebe-se que ainda há várias possibilidades de pesquisas sobre aliar as TDIC e as particularidades de alunos objetivando a melhoria do processo de ensino e aprendizagem