Evaluation of learning styles adaption in the adopta e-learning platform

2011 ◽  
Author(s):  
Dessislava Vassileva
Keyword(s):  
Learning Styles ◽  
Learning Platform ◽  
E Learning ◽  
Evaluation Of Learning

scholarly journals E-learning Platforms and E-learning Students: Building the Bridge to Success

2013 ◽  
Vol 1 (2) ◽  
pp. 21-34 ◽  
Author(s):  
Manuel Rodrigues ◽  
Sérgio Gonçalves ◽  
Florentino Fdez-Riverola
Keyword(s):  
Online Learning ◽  
Learning Styles ◽  
Traditional Learning ◽  
Affective States ◽  
Non Invasive ◽  
Learning Platform ◽  
E Learning ◽  
Learning Platforms ◽  
Dynamic Recognition ◽  
Insight Into

E-learning platforms are becoming more and more common in education and with organisations. They are seen as a complementary tool to support learning or, as in many cases, as the primary tool to do it (possibly the only one). In traditional learning, teachers can easily get an insight into how their students work and learn, and how they interact in the classroom. However, in online learning, it is more difficult for teachers to see how individual students behave. Affective states and learning styles are determinant in students’ performance. Together with stress, these are crucial factor to success. It is believed that the sole use of an E-learning platform can in itself be a cause of stress for students. Estimating, in a non-invasive way, such parameters, and taking measures to deal with them, are then the goal of this paper. We do not consider the use of dedicated sensors (invasive) such as special gloves or wrist bracelets since we intend not to be dependent on specific hardware and also because we believe that such specific hardware can induce for itself some alteration in the parameters being analysed. Our work focuses on the development of a new module (Dynamic Recognition Module) to incorporate in Moodle E-learning platform, to accommodate individualized support to E-learning students.

An Adaptive e-learning Platform for Personalized Course Generation

2008 ◽  
pp. 262-282 ◽  
Author(s):  
Enver Sangineto
Keyword(s):  
Learning Styles ◽  
Learning Objects ◽  
Learning Preferences ◽  
Domain Specific ◽  
Learning Platform ◽  
E Learning ◽  
Funded Project ◽  
Learning Platforms ◽  
Didactic Material ◽  
Methodological Aspects

In this chapter we show the technical and methodological aspects of an e-learning platform for automatic course personalization built during the European funded project Diogene. The system we propose is composed of different knowledge modules and some inference tools. The knowledge modules represent the system’s information about both the domain-specific didactic material and the student model. By exploiting such information the system automatically builds courses whose didactic material is customized to meet the current student’s degree of knowledge and her/his learning preferences. Concerning the latter, we have adopted the Felder and Silverman’s pedagogical approach in order to match the student’s learning styles with the system Learning Objects’ types. Finally, we take care to describe the system’s didactic material by means of some present standards for e-learning in order to allow knowledge sharing with other e-learning platforms and knowledge searching by means of possible Semantic Web information retrieval facilities.

scholarly journals Online Learning Styles Identification Model, Based on the Analysis of User Interactions Within an E-Learning Platforms, Using Neural Networks and Fuzzy Logic

2018 ◽  
Vol 7 (3.13) ◽  
pp. 76
Author(s):  
L Alfaro ◽  
C Rivera ◽  
J Luna-Urquizo ◽  
E Castañeda ◽  
F Fialho
Keyword(s):  
Fuzzy Logic ◽  
Learning Styles ◽  
Learning Style ◽  
User Behavior ◽  
Backpropagation Neural Network ◽  
User Interactions ◽  
Learning Platform ◽  
Proposed Model ◽  
E Learning ◽  
Learning Platforms

Individual Learning Style identification is an essential aspect in the development of intelligent or adaptive e-Learning platforms. Traditional methods are based on the application of questionnaires or psychological tests, which may not be the most appropriate in all cases. The proposed model is based on the analysis of user behavior through the study of their interactions within an e-Learning platform, using a multilayer Backpropagation Neural Network and Fuzzy Logic concepts, for the preprocessing of the inputs and the categorization of the outputs.                                                                              

scholarly journals Analysing the relationship between learning styles and navigation behaviour in web-based educational system

2010 ◽  
pp. 400-421 ◽  
Keyword(s):  
Learning Styles ◽  
Computer Science ◽  
Learning Style ◽  
Web Based ◽  
Learning Platform ◽  
Web Based Learning ◽  
Index Of Learning Styles ◽  
Type Indicator ◽  
E Learning ◽  
The Relationship

The aim of our research is to automatically deduce the learning style from the analysis of browsing behaviour. To find how to deduce the learning style, we are investigating, in this paper, the relationships between the learner’s navigation behaviour and his/her learning style in web-based learning. To explore this relation, we carried out an experiment with 27 students of computer science at the engineering school (ESI-Algeria). The students used a hypermedia course on an e-learning platform. The learners’ navigation behaviour is evaluated using a navigation type indicator that we propose and calculate based on trace analysis. The findings are presented with regard to the learning styles measured using the Index of Learning Styles by (Felder and Solomon 1996). We conclude with a discussion of these results.

scholarly journals An ESL Online Classroom Experience in Oman during Covid-19

2020 ◽  
Author(s):  
Syerina Syahrin ◽  
Abdelrahman Abdalla Salih
Keyword(s):  
Online Learning ◽  
Learning Styles ◽  
Learning Style ◽  
Undergraduate Students ◽  
Learning Experience ◽  
Online Classroom ◽  
Learning Platform ◽  
E Learning ◽  
Learning Styles Inventory ◽  
The Relationship

This paper aimed to investigate the online learning experience of a group of ESL students at a higher learning institution in Oman during the Covid-19. The paper studied the interaction between the students’ preferred online learning style and the technologies the students experienced on the e-learning platform (Moodle) for the particular ESL course. The rationale for investigating the relationship between the students’ learning styles and the technologies the students experienced is to evaluate if the learning style and the technologies complement each other. It is also aimed to provide an evaluation of an ESL e-learning course by considering the different technologies that can be incorporated into the e-learning classroom to meet the different learning styles. Data was gathered from 32 undergraduate students by utilizing Kolb’s Learning Styles Inventory. The study included analysis of Moodle utilizing Warburton’s Technologies in Use (2007) to develop an understanding of the technologies the students experienced online. The results of the study revealed that the majority of the students’ preferred learning style is reflected in the technologies they experienced in the online classroom. As the relationship of the technology in use and the students learning style preference in the classroom complements each other, the study revealed that the emphasis of the particular skill-based pedagogy ESL classroom is on receptive skills (listening and reading). The lack of the students’ productive skills (speaking and writing) is a cause for concern to the ESL course instructors, policymakers, and the wider community.

scholarly journals Background Similarities as a Way to Predict Students’ Behaviour

2019 ◽  
Vol 11 (24) ◽  
pp. 6883 ◽  
Author(s):  
Daniel Burgos
Keyword(s):  
Learning Styles ◽  
Student Retention ◽  
Model Building ◽  
Online Courses ◽  
Attribute Selection ◽  
Data Logging ◽  
Data Set ◽  
Learning Platform ◽  
E Learning ◽  
Low Performance

The number of students opting for online educational platforms has been on the rise in recent years. Despite the upsurge, student retention is still a challenging task, with some students recording low-performance margins on online courses. This paper aims to predict students’ performance and behaviour based on their online activities on an e-learning platform. The paper will focus on the data logging history and utilise the learning management system (LMS) data set that is available on the Sakai platform. The data obtained from the LMS will be classified based on students’ learning styles in the e-learning environment. This classification will help students, teachers, and other stakeholders to engage early with students who are more likely to excel in selected topics. Therefore, clustering students based on their cognitive styles and overall performance will enable better adaption of the learning materials to their learning styles. The model-building steps include data preprocessing, parameter optimisation, and attribute selection procedures.

scholarly journals Evaluating the Effect of Arabic Engineering Students’ Learning Styles in Blended Programming Courses

10.28945/3423 ◽  
2016 ◽  
Vol 15 ◽  
pp. 109-130 ◽  
Author(s):  
Ahmed Al-Azawei ◽  
Ali kadhim Al-Bermani ◽  
Karsten Lundqvist
Keyword(s):  
Academic Performance ◽  
Learning Styles ◽  
Learning Style ◽  
Structural Equation ◽  
Engineering Students ◽  
Learning Context ◽  
Learning Platform ◽  
Perceived Satisfaction ◽  
E Learning ◽  
Programming Courses

This study investigated the complex relationship among learning styles, gender, perceived satisfaction, and academic performance across four programming courses supported by an e-learning platform. A total of 219 undergraduate students from a public Iraqi university who recently experienced e-learning voluntarily took place in the study. The integrated courses adopted a blended learning mode and all learners were provided the same learning content and pathway irrespective of their individual styles. Data were gathered using the Index of Learning Styles (ILS), three closed-ended questions, and the academic record. Traditional statistics and partial least squares structural equation modelling (PLS-SEM) were performed to examine the proposed hypotheses. The findings of this research suggested that, overall, learning style dimensions are uncorrelated with either academic performance or perceived satisfaction, except for the processing dimension (active/reflective) that has a significant effect on the latter. Furthermore, gender is unassociated with any of the proposed model’s constructs. Finally, there is no significant correlation between academic performance and perceived satisfaction. These results led to the conclusion that even though Arabic engineering students prefer active, sensing, visual, and sequential learning as do other engineering students from different backgrounds, they can adapt to a learning context even if their preferences are not met. The research contributes empirically to the existing debate regarding the potential implications of learning styles and for the Arabic context in particular, since respective research remains rare.

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