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Open AccessJournal ArticleDOI

Enhancing student learning through hypermedia courseware and incorporation of student learning styles

TLDR
An adaptive hypermedia interface was developed that provided dynamic tailoring of the presentation of course material based on the individual student's learning style, and the authors believe students learned more efficiently and more effectively.
Abstract
This paper outlines attempts to enhance student learning by addressing different learning styles through course hypermedia. Students learn by a variety of different learning styles. Previously, instructors were unable to effectively address these different learning styles outside the classroom. Two approaches were developed to address this problem. The first approach was the development of hypermedia courseware. This provided a wide variety of tools which students could use to prepare for lessons. In this way students retained complete control over how they prepared for a lesson and could choose those hypermedia tools that were most conducive to their learning. An assessment of the multimedia and hypertext documents in the course revealed that the value of a particular multimedia tool to a student varied widely. Each student was traversing the course material according to his/her unique learning style. Unfortunately, the plethora of tools confused some students because they were uncomfortable making active choices of what course material would be most conducive to their learning. As a result, a second approach was adopted. An adaptive hypermedia interface was developed that provided dynamic tailoring of the presentation of course material based on the individual student's learning style. By tailoring the presentation of material to the student's learning style, the authors believe students learned more efficiently and more effectively.

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Dissertation

Modelo de Sistema e-learning adaptativo para el nivel superior, utilizando aprendizaje colaborativo basado en proyectos, considerando estilos de aprendizaje y estilos de pensamiento

TL;DR: The proposed model is based on the analysis of user behavior through the study of their interactions within an eLearning platform, using a multilayer Backpropagation Neural Network and Fuzzy Logic concepts, for the preprocessing of the inputs and the categorization of the outputs.
Proceedings ArticleDOI

An intelligent etutor-student adaptive interaction framework

TL;DR: A framework of a Smart Cognitive Augmented Learning Object Repository (SCALOR) engine that augments the concepts of learning styles onto Hypermedia Learning Objects, which together with a Smart domain knowledge ontology compose the Smart e-Learning Knowledgebase (SELK).
Journal Article

A new framework for e-learning using Learning Style and Personality

TL;DR: This paper presents the new framework for providing the appropriate contents for users in an e-learning system based on their Learning Style and Personalities, and shows significant improvements of users satisfactions with elearning contents using users learning style and personality.
DissertationDOI

Developing employability and job-related skills at mobile learning environments:a case study at an industrial training centre

TL;DR: In this article, the authors investigated how mobile technologies play a role in the development of employability and job-related skills for trainees at Saudi Aramco's Industrial Training Centres (SAITCs) in Saudi Arabia.
Proceedings ArticleDOI

Measuring the learners' learning style based on tracks analysis in web based learning

TL;DR: The modeling of the learning style is proposed as a classification of the styles models proposed in the literature that can be detected in such a context and is implemented in IDLS, a track based system.
References
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Journal ArticleDOI

A Sign That Education is Maturing: Taxonomy of Educational Objectives, the Classification of Educational Goals, Handbook I: Cognitive Domain

TL;DR: Using Bloom's Taxonomy to Write Effective Learning Objectives: The Abcds of Writing Learning ObjectIVES: A Basic Guide.

Learning and Teaching Styles in Engineering Education.

TL;DR: A self-scoring web-based instrument called the Index of Learning Styles that assesses preferences on four scales of the learning style model developed in the paper currently gets about 100,000 hits a year and has been translated into half a dozen languages.
Journal Article

Reaching the Second Tier--Learning and Teaching Styles in College Science Education.

TL;DR: This paper defined two tiers of entering college students, the first consisting of those who go on to earn science degrees and the second those who have the initial intention and the ability to do so but instead switch to nonscientific fields.