The Experts below are selected from a list of 93267 Experts worldwide ranked by ideXlab platform
Sabine Graf - One of the best experts on this subject based on the ideXlab platform.
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use of felder and silverman Learning Style model for online course design
Educational Technology Research and Development, 2019Co-Authors: Moushir M Elbishouty, Tingwen Chang, Ahmed Aldraiweesh, Uthman Alturki, Richard A W Tortorella, Junfeng Yang, Sabine GrafAbstract:Learning Management Systems are used in millions of higher education courses, across various countries and disciplines. Teachers build courses reflecting their individual teaching methods, which may not always fit students’ different Learning Styles. However, limited information is known about how well these courses support the learners. The study aims to explore the use of Felder and Silverman Learning Style for online course design. The study has used linear transfer function system models to develop fundamentals of feedback by a course analyzer tool. This interactive tool allows teachers to determine a course’s support level for specific Learning Styles, based on the Felder and Silverman Learning Style model. The Felder and Silverman Learning Style model in this study is used to visualize the fit between course and Learning Style to help teachers improve their course’s support for diverse Learning Styles. The results of a pilot study successfully validated the course analyzer tool, as it has potential to improve the design of the course in future and allow more insight into overall student performance. The findings suggest that a course designed with certain Learning Styles in mind can improve Learning of the students with those specific Learning Styles.
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Learning Style identifier improving the precision of Learning Style identification through computational intelligence algorithms
Expert Systems With Applications, 2017Co-Authors: Jason Bernard, Elvira Popescu, Tingwen Chang, Sabine GrafAbstract:Abstract 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.
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in depth analysis of the felder silverman Learning Style dimensions
Journal of research on technology in education, 2007Co-Authors: Sabine Graf, Silvia Rita Viola, Tommaso LeoAbstract:AbstractLearning Styles are increasingly being incorporated into technology-enhanced Learning. Appropriately, a great deal of recent research work is occurring in this area. As more information and details about Learning Styles becomes available, Learning Styles can be better accommodated and integrated into all aspects of educational technology. The aim of this paper is to analyse data about Learning Styles with respect to the Felder-Silverman Learning Style model (FSLSM) in order to provide a more detailed description of Learning Style dimensions. The analyses show the most representative characteristics of each Learning Style dimension as well as how representative these characteristics are. As a result, we provide additional information about the Learning Style dimensions of FSLSM. This information is especially important when Learning Styles are incorporated in technology-enhanced Learning.
Tingwen Chang - One of the best experts on this subject based on the ideXlab platform.
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use of felder and silverman Learning Style model for online course design
Educational Technology Research and Development, 2019Co-Authors: Moushir M Elbishouty, Tingwen Chang, Ahmed Aldraiweesh, Uthman Alturki, Richard A W Tortorella, Junfeng Yang, Sabine GrafAbstract:Learning Management Systems are used in millions of higher education courses, across various countries and disciplines. Teachers build courses reflecting their individual teaching methods, which may not always fit students’ different Learning Styles. However, limited information is known about how well these courses support the learners. The study aims to explore the use of Felder and Silverman Learning Style for online course design. The study has used linear transfer function system models to develop fundamentals of feedback by a course analyzer tool. This interactive tool allows teachers to determine a course’s support level for specific Learning Styles, based on the Felder and Silverman Learning Style model. The Felder and Silverman Learning Style model in this study is used to visualize the fit between course and Learning Style to help teachers improve their course’s support for diverse Learning Styles. The results of a pilot study successfully validated the course analyzer tool, as it has potential to improve the design of the course in future and allow more insight into overall student performance. The findings suggest that a course designed with certain Learning Styles in mind can improve Learning of the students with those specific Learning Styles.
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Learning Style identifier improving the precision of Learning Style identification through computational intelligence algorithms
Expert Systems With Applications, 2017Co-Authors: Jason Bernard, Elvira Popescu, Tingwen Chang, Sabine GrafAbstract:Abstract 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.
Zahra Abdollahzade - One of the best experts on this subject based on the ideXlab platform.
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Investigating the relationship between Learning Style and game type in the game-based Learning environment
Education and Information Technologies, 2019Co-Authors: Seyed Mohammadbagher Jafari, Zahra AbdollahzadeAbstract:The game-based Learning, which uses computer games to improve performance and Learning, is a new field which can be used as a powerful educational tool. To increase the effectiveness of educational games, new games fit the Learning Styles of each individual can be made to have a customized Learning environment. Currently, playing computer games have become far more widespread among Iranian youth and teenagers who are mostly students. Accordingly, this study investigated the relationship between the Felder-Silverman Learning Styles model (FSLSM) and four types of games. To this end, the research data were collected from 121 students at the universities in Qom province. Then the results are analyzed using Pearson’s chi-squared test and Crosstab. Three of seven hypotheses (the relationship between visual Learning Style and simulation game, sequential Learning Style and puzzle game, sensing Learning Style and casual game) were confirmed which can be the guide to design games in the environment of game-based Learning more effectively.
Maria Virvou - One of the best experts on this subject based on the ideXlab platform.
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Personalized tutoring through a stereotype student model incorporating a hybrid Learning Style instrument
Education and Information Technologies, 2020Co-Authors: Christos Troussas, Konstantina Chrysafiadi, Maria VirvouAbstract:Personalized computer-based tutoring demands Learning systems and applications that identify and keep personal characteristics and features for each individual learner. This is achieved by the technology of student modeling. One prevalent technique of student modeling is stereotypes. Furthermore, individuals differ in how they learn. So, the way that helps an individual to learn best is crucial for offering her/him an effective tutoring experience. As a consequence, students’ preferable Styles of Learning should be incorporated into the student model. However, some researchers have concluded that there are individuals that have a mixture of Learning Styles. That is the reason for the combination of two different Learning Style models in the presented approach. Particularly, in this paper we present a stereotype student model that combines the Visual, Auditory, Reading/Writing and Kinesthetic (VARK) Learning Style model and the Herrmann Brain Dominance Instrument (HBDI). The aim of this article is to further enhance the personalization to students’ needs and preferences by introducing this hybrid instrument and using the technology of stereotypes. The gain from this hybrid Learning Style approach is that we model two different dimensions of the way that a student prefers to learn: i) the sensory modalities of Learning and ii) the way of thinking. In this way, the offered tutoring process can be more tailored to each individual student’s needs, respecting the distinct pace of her/his Learning. Our novel approach has been incorporated in an e-Learning system and was evaluated by 60 undergraduate students in Greece. The evaluation results show a great acceptance rate of the novel hybrid Learning Style model by students and underline its pedagogical potential.
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Artificial Immune System-Based Learning Style Stereotypes
International Journal on Artificial Intelligence Tools, 2019Co-Authors: Dionisios N. Sotiropoulos, Efthimios Alepis, Katerina Kabassi, Maria Virvou, George A. Tsihrintzis, Evangelos SakkopoulosAbstract:This paper addresses the problem of extracting fundamental Learning Style stereotypes through the exploitation of the biologically-inspired pattern recognition paradigm of Artificial Immune Systems...
James E Dyer - One of the best experts on this subject based on the ideXlab platform.
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the influence of student Learning Style on critical thinking skill
Journal of Agricultural Education, 2006Co-Authors: Brian E Myers, James E DyerAbstract:The purpose of this study was to determine the influence of student Learning Style on critical thinking skill. The target population for this ex post facto study was 135 students enrolled in a college of agriculture and life sciences leadership development course at the University of Florida. Results showed that no critical thinking skill differences existed between male and female students in this study. Students with deeply embedded Abstract Sequential Learning Style preferences exhibited significantly higher critical thinking skill scores. No differences in critical thinking ability existed between students of other Learning Styles. These findings have implications for faculty with teaching appointments in colleges of agriculture. If Abstract Sequential learners are inherently adept at thinking critically, teachers may not need to focus as intently on teaching strategies that address this Learning Style. By contrast, however, Concrete Sequential, Abstract Random, and Concrete Random learners may need additional attention through instructional methods and techniques that enhance the critical thinking skills of these learners.
