Interactive E-Learning

2011 ◽  
pp. 35-44
Author(s):  
Claude Ghaoui ◽  
W. A. Janvier
Keyword(s):  
Distance Learning ◽  
Learning Styles ◽  
Learning Style ◽  
Text Messaging ◽  
Memory Retention ◽  
Learning Tool ◽  
Neurolinguistic Programming ◽  
Interactive Interface ◽  
E Learning ◽  
Communication Preference

This paper introduces the concept of improving student memory retention using a distance learning tool by establishing the student’s communication preference and learning style before the student uses the module contents. It argues that incorporating a distance learning tool with an intelligent/interactive tutoring system using various components (psychometric tests, communication preference , learning styles, mapping learning/teaching styles, neurolinguistic programming language patterns, subliminal text messaging, motivational factors, novice/expert factor, student model, and the way we learn) combined in WISDeM to create a human-computer interactive interface distance learning tool does indeed enhance memory retention. The authors show that WISDeM’s initial evaluation indicates that a student’s retained knowledge has been improved from a mean average of 63.57% to 71.09% — moving the student from a B to an A.

Interactive E-Learning

2007 ◽  
pp. 189-202
Author(s):  
Claude Ghaoui ◽  
W. A. Janvier
Keyword(s):  
Distance Learning ◽  
Learning Styles ◽  
Learning Style ◽  
Text Messaging ◽  
Memory Retention ◽  
Learning Tool ◽  
Neurolinguistic Programming ◽  
Interactive Interface ◽  
E Learning ◽  
Communication Preference

This chapter introduces the concept of improving student memory retention using a distance learning tool by establishing the student’s communication preference and learning style before the student uses the module contents. It argues that incorporating a distance learning tool with an intelligent/interactive tutoring system using various components (psychometric tests, communication preference, learning styles, mapping learning/teaching styles, neurolinguistic programming language patterns, subliminal text messaging, motivational factors, novice/expert factor, student model, and the way we learn) combined in WISDeM to create a human-computer interactive interface distance learning tool does indeed enhance memory retention. The authors show that WISDeM’s initial evaluation indicates that a student’s retained knowledge has been improved from a mean average of 63.57% to 71.09% — moving the student from a B to an A.

Replicating Human Interaction to Support E-Learning

2006 ◽  
pp. 494-503
Author(s):  
William A. Janvier ◽  
Claude Ghaoui
Keyword(s):  
Learning Styles ◽  
Text Messaging ◽  
Learning Experience ◽  
Human Interaction ◽  
Learning Tools ◽  
Tutoring Systems ◽  
Language Patterns ◽  
E Learning ◽  
Communication Preference ◽  
The Way

HCI-related subjects need to be considered to make e-learning more effective; examples of such subjects are: psychology, sociology, cognitive science, ergonomics, computer science, software engineering, users, design, usability evaluation, learning styles, teaching styles, communication preference, personality types, and neuro-linguistic programming language patterns. This article discusses the way some components of HI can be introduced to increase the effectiveness of e-learning by using an intuitive interactive e-learning tool that incorporates communication preference (CP), specific learning styles (LS), neurolinguistic programming (NLP) language patterns, and subliminal text messaging. The article starts by looking at the current state of distance learning tools (DLTs), intelligent tutoring systems (ITS) and “the way we learn”. It then discusses HI and shows how this was implemented to enhance the learning experience.

E-Learning through HCI

2011 ◽  
pp. 607-614
Author(s):  
C. Ghaoui
Keyword(s):  
Learning Styles ◽  
Interpersonal Communication ◽  
Personality Types ◽  
Transfer Of Knowledge ◽  
Neurolinguistic Programming ◽  
Tutorial System ◽  
E Learning ◽  
Interactive Tutorial ◽  
Effective Transfer ◽  
Communication Preference

E-government is using electronic technology to streamline and/or to improve the business of government and, as a result, to improve its citizens’ personal services; for example, patient hospital appointment booking systems, electronic voting, and the development of e-education. E-education and, from this, e-learning depend not only on the quality of the content but also on that of the human-computer interaction (HCI) generated by designers and implementers of a system. HCI is a cross-discipline subject that covers a wide area of topics, many of which need to be considered in e-learning, that directly affect the student, the tutor, or the administrator. HCI delivering e-learning includes issues such as psychology, sociology, cognitive science, ergonomics, computer science, software engineering, users, design, usability evaluation, learning styles, teaching styles, communication preference, personality types, neurolinguistic programming language patterns, and so forth. This article considers interpersonal communication and the effective transfer of knowledge from one human to another (e.g., a teacher to a learner) in the real world; it then postulates on their replication in distance/e-learning. The article focuses on the factors required for effective e-learning in the field of HCI for education. In particular, it introduces the concept of using communication preference (CP) and learning styles/personality types (LS) in an intuitive interactive tutorial system (in TS). The development of WISDeM (Web Intuitive/interactive student distance education model) by the authors significantly exemplifies this in its evaluation results.

scholarly journals Saudi Nursing Students Experience of Distance Learning (e-learning) During COVID-19 2020 Pandemic Outbreak at KSAU-HS

2021 ◽  
Vol 6 (03) ◽  
pp. 212-218
Author(s):  
Najla A. Barnawi ◽  
Angham Al-Mutair ◽  
Hind Al-Ghadeer
Keyword(s):  
Distance Learning ◽  
Nursing Students ◽  
Learning Styles ◽  
Learning Style ◽  
Cross Sectional Study ◽  
Learning Needs ◽  
Learning Approach ◽  
Cross Sectional ◽  
Level Of Satisfaction ◽  
E Learning

Objectives: This study aims to assess the experience of the full Distance Learning approach among nursing students during COVID-19 Outbreak at KSAU-HS in the three regions (Riyadh, Jeddah, and Al-Ahsa). The study covers the following objectives; measuring the students’ level of satisfaction, perception, and interest in integrating Distance Learning, their learning needs accomplishments; and determine the association between the student’s level of satisfaction, perception/interest in integrating Distance Learning, and learning needs accomplishments. Methods: A cross-sectional study was conducted among 800 nursing students in the CON campuses. The validated 5-Lickert scale DSLLA, was used to assess the student’s demographical data, satisfaction, and learning achievements. The r values were calculated to examine the correlation between the students' satisfaction and other study variables. Results: The majority of the participants were satisfied 70.9% and 90% were toward integrating the e-learning and 89.4% reported that they meet their learning needs. There was a statistically significant relationship between the participants’ satisfaction with learning styles (r=0.305, P= 0.000) and their learning needs achievements (r=.600, P=0.000). Conclusion: Integrating the companied learning style is the best learning option to deliver the pedagogy process within the Nursing curricula; however, the full distance learning approach is an effective tool on delivering the pedagogy process mainly during COVID-19.

Design of an Adaptive Mobile Learning Management System

2008 ◽  
pp. 286-301 ◽  
Author(s):  
Hyungsung Park ◽  
Young Kyun Baek ◽  
David Gibson
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Mobile Learning ◽  
Management System ◽  
Learning Management System ◽  
Analysis Tool ◽  
User Interactions ◽  
E Learning ◽  
Adaptive Environment ◽  
Learning Management

This chapter introduces the application of an artificial intelligence technique to a mobile educational device in order to provide a learning management system platform that is adaptive to students’ learning styles. The key concepts of the adaptive mobile learning management system (AM-LMS) platform are outlined and explained. The AM-LMS provides an adaptive environment that continually sets a mobile device’s use of remote learning resources to the needs and requirements of individual learners. The platform identifies a user’s learning style based on an analysis tool provided by Felder & Soloman (2005) and updates the profile as the learner engages with e-learning content. A novel computational mechanism continuously provides interfaces specific to the user’s learning style and supports unique user interactions. The platform’s interfaces include strategies for learning activities, contents, menus, and supporting functions for learning through a mobile device.

scholarly journals Learning Styles and Dyslexia Types – Understanding Their Relationship and its Benefits in Adaptive E-learning Systems

2020 ◽  
Vol 14 (15) ◽  
pp. 25
Author(s):  
Aisha Y Alsobhi ◽  
Khaled H Alyoubi
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Learning Systems ◽  
Learning System ◽  
Knowledge Level ◽  
Educational Establishment ◽  
E Learning ◽  
User Learning ◽  
The Relationship ◽  
Better Than

Learning is a fundamental element of people’s everyday lives. Learning experiences can take the form of our interactions with others, through attending an educational establishment, etc. Not everyone learns in the same way, and even people who are considered to have a similar standard of abilities or proficiency will exhibit different learning styles. This does not necessarily mean that some students are better than others; it means that students are different from one another. Adaptive e-learning system should be capable of adapting the content to the user learning style, abilities and knowledge level. In this paper, we investigate the benefits of incorporating learning styles and dyslexia type in adaptive e-learning systems. Adaptivity aspects based on dyslexia type and learning styles enrich each other, enabling systems to provide learners with materials which fit their needs more accurately. Besides, consideration of learning styles and dyslexia type can contribute to more accurate student modelling. In this paper, the relationship between learning styles, the Felder–Silverman learning style model (FSLSM), and dyslexia type, is investigated. These relationships will lead to a more reliable student model.

scholarly journals Are learning style preferences of health science students predictive of their attitudes towards e-learning?

2009 ◽  
Vol 25 (4) ◽  
Author(s):  
Ted Brown ◽  
Maryam Zoghi ◽  
Brett Williams ◽  
Shapour Jaberzadeh ◽  
Louis Roller ◽  
...  
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Linear Regression Analysis ◽  
Health Science ◽  
Science Students ◽  
Health Science Students ◽  
Learning Style Preferences ◽  
E Learning ◽  
Students Attitudes ◽  
Style Preferences

<span>The objective for this study was to determine whether learning style preferences of health science students could predict their attitudes to e-learning. A survey comprising the </span><em>Index of Learning Styles</em><span> (ILS) and the </span><em>Online Learning Environment Survey</em><span> (OLES) was distributed to 2885 students enrolled in 10 different health science programs at an Australian university. A total of 822 useable surveys were returned generating a response rate of 29.3%. Using </span><em>SPSS</em><span>, a linear regression analysis was completed. On the ILS Active-Reflective dimension, 44% of health science students reported a preference as being active learners, 60% as sensing learners, and 64% as sequential learners. Students' attitudes toward e-learning using the OLES showed that their </span><em>preferred</em><span> scores for all 9 subscales were higher than their </span><em>actual</em><span> scores. The linear regression analysis results indicated that ILS learning styles accounted for a small percentage of the OLES </span><em>actual</em><span> and </span><em>preferred</em><span> subscales' variance. For the OLES </span><em>actual</em><span> subscales, the ILS Active-Reflective and Sensing-Intuitive learning style dimensions were the most frequent predictors of health science students' attitudes towards e-learning. For the OLES </span><em>preferred</em><span> subscales, ILS Active-Reflective and Sequential-Global learning style dimensions accounted for the most frequent source of variance. It appears that the learning styles of health science students (as measured by the ILS) can be used only to a limited extent as a predictor of students' attitudes towards e-learning. Nevertheless, educators should still consider student learning styles in the context of using technology for instructional purposes.</span>

scholarly journals Learning Styles And The Online Classroom: Implications For Business Students

2008 ◽  
Vol 5 (10) ◽  
Author(s):  
Michael Nastanski ◽  
Thomas Slick
Keyword(s):  
Distance Learning ◽  
Learning Styles ◽  
Learning Style ◽  
Course Design ◽  
Business Students ◽  
The United States ◽  
Grade Point ◽  
Significant Difference ◽  
Online Business ◽  
Learning Style Preference

This paper discusses the importance of student learning styles within a Distance Learning (DL) classroom. The study examines the learning style preferences of online business students as measured by the Kolb Learning Style Inventory and determines if a significant difference in course grades and course completion rates exist between students when they are sorted by learning style preference. Subjects in the study were 344 online business students from a southeastern university in the United States. Examination of the quantitative data indicated a significant difference existed for Diverger Style Preference learners compared to the Assimilator, Accommodator and Converger learning styles.  They had a lower Mean Grade Point (GP) earned.  The study revealed approximately one out of five (20%) of the respondents had a Diverger Learning Style Preference. Respondents with this learning style preference appear to be somewhat less likely to be successful in a distance learning environment. A Chi Square calculation showed no significant difference existed among learning styles for those dropping a course although one group (Accomodators) had approximately twice the drop rate of the others. This paper and corresponding study offers university administrators who seek to maintain quality instruction evidence and suggestions for addressing 20 percent of their online population who may be at risk of not obtaining content mastery.  This includes implications for DL course design and pedagogy.

scholarly journals Development & Evaluation of E-Learning Module Based on Visual and Global Preferences Using a User-Centered Design Approach

2021 ◽  
Vol 16 (15) ◽  
pp. 139
Author(s):  
Harry Budi Santoso ◽  
Panca O. Hadi Putra ◽  
Febrian Fikar Farras Hendra S
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Interaction Design ◽  
Interface Design ◽  
Learning Objects ◽  
User Centered Design ◽  
Alternative Design ◽  
Learning Module ◽  
E Learning ◽  
User Centered

Students develop various learning styles based on their preferences and learning habits. To serve different learning styles in a class with a number of students using the conventional face-to-face teaching method is not practical; therefore, the idea of personalized e-Learning to accommodate differences in learning style has arisen. Building on this idea, this research intends to provide an alternative interaction design for e-Learning modules by developing content based on user needs using the User-Centered Design methodology. Due to a lack of e-Learning content for visual and global preferences in the Felder-Silverman learning styles, User-Centered Design is chosen as the basis to design the e-Learning module. The result consists of an alternative design and a proposed interface design. The alternative design describes learning objects and navigation of the e-Learning module. The proposed interface design is a prototype of an interactive e-Learning module. After being evaluated, the prototype satisfies the user's expectations in terms of content translation, content navigation, and interactivity throughout the module.

scholarly journals College Students Attitudes Toward Learning Process And Outcome Of Online Instruction And Distance Learning Across Learning Styles

2011 ◽  
Vol 8 (12) ◽  
pp. 35-42 ◽  
Author(s):  
Dat-Dao Nguyen ◽  
Yue Jeff Zhang
Keyword(s):  
Distance Learning ◽  
Learning Styles ◽  
Learning Style ◽  
Learning Process ◽  
Online Instruction ◽  
Student Attitudes ◽  
Learning Style Inventory ◽  
Attitudes Toward Learning ◽  
Process And Outcome ◽  
Students Attitudes

This study uses the Learning-Style Inventory LSI (Smith & Kolb, 1985) to explore to what extent student attitudes toward learning process and outcome of online instruction and Distance Learning are affected by their cognitive styles and learning behaviors. It finds that there are not much statistically significant differences in perceptions on many learning process and outcome indicators across learning styles. However, students who learn from concrete experience and reflective experimentation/observation didnt appreciate the flexible class schedule, need instant questions and feedback, and expect more leniency from the instructor.

Sign in / Sign up

Export Citation Format

Share Document