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.

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.

E-learning in postgraduate urology training: a Covid-19 pandemic experience

2022 ◽  
Vol 71 (12) ◽  
Author(s):  
Mudassir Hussain ◽  
Abdul Khalique ◽  
Pardeep Kumar ◽  
Asad Shehzad Hassan ◽  
Altaf Hashmi ◽  
...  
Keyword(s):  
Medical Education ◽  
Learning Experience ◽  
Online Resources ◽  
New Methods ◽  
Online Lectures ◽  
E Learning ◽  
The Subject ◽  
Online Tests ◽  
The Way

Since the declaration of a COVID-19 pandemic in March 2020 teaching institutions started the process of adjusting to the new challenge. Medical education could not be imparted the way it used to be and some new methods had to be taken to adapt to the pandemic. At our institute, each week two lectures were recorded and later uploaded on the Youtube Channel and shared with students. This was followed by an MCQs based test using Google forms. Ten lectures were delivered in 5 weeks to 55 participants.  Majority of residents agreed that this activity increased their knowledge of the subject and opted to continue it in future.  With help of short online lectures (< 30 mins) and short online tests (5 MCQs), the learning experience of residents can be enhanced. In future, more online resources can be used to incorporate this method of teaching. 

scholarly journals The Application of Affective Computing Technology to E-Learning

2014 ◽  
pp. 109-128
Author(s):  
Nik Thompson ◽  
Tanya Jane McGill
Keyword(s):  
Affective Computing ◽  
Physiological State ◽  
Affective State ◽  
Significant Proportion ◽  
Human Interaction ◽  
Human Communication ◽  
Tutoring Systems ◽  
User Input ◽  
E Learning ◽  
New Generation

This chapter discusses the domain of affective computing and reviews the area of affective tutoring systems: e-learning applications that possess the ability to detect and appropriately respond to the affective state of the learner. A significant proportion of human communication is non-verbal or implicit, and the communication of affective state provides valuable context and insights. Computers are for all intents and purposes blind to this form of communication, creating what has been described as an “affective gap.” Affective computing aims to eliminate this gap and to foster the development of a new generation of computer interfaces that emulate a more natural human-human interaction paradigm. The domain of learning is considered to be of particular note due to the complex interplay between emotions and learning. This is discussed in this chapter along with the need for new theories of learning that incorporate affect. Next, the more commonly applicable means for inferring affective state are identified and discussed. These can be broadly categorized into methods that involve the user’s input and methods that acquire the information independent of any user input. This latter category is of interest as these approaches have the potential for more natural and unobtrusive implementation, and it includes techniques such as analysis of vocal patterns, facial expressions, and physiological state. The chapter concludes with a review of prominent affective tutoring systems in current research and promotes future directions for e-learning that capitalize on the strengths of affective computing.

An Ontology-Based Learning System in IT Project Management

2011 ◽  
pp. 191-210
Keyword(s):  
Learning Objects ◽  
Learning System ◽  
Learning Tools ◽  
It Project Management ◽  
Knowledge Domain ◽  
Tutoring Systems ◽  
Learner Model ◽  
Future Developments ◽  
E Learning ◽  
Competency Based

The development process is based on the state of the art IT technologies (metadata and ontology for knowledge manipulation, Web services, learner model, and intelligent tutoring systems). Besides interoperability and personalization, the proposed approach brings additional advantages, including: unitary interpretation of the content structure by different user categories or content providers; explicit specification of the knowledge domain, allowing the updating of the domain definition without major changes of e-learning tools and programmes; reuse of the learning objects with economical advantages by saving costs of (re)writing the content for the different course forms and strategies; reuse of the created tools in one domain in other domains; promoting the competency-based learning through the domain ontology and the relations between concepts and competencies. The results obtained in practice are very encouraging and suggest several future developments.

Development and Testing of New E-Learning Hypermedia System

2015 ◽  
Vol 10 (2) ◽  
pp. 1-15
Author(s):  
Lamia Mahnane ◽  
Mohamed Hafidi
Keyword(s):  
Learning Styles ◽  
Learning Process ◽  
Learning Experience ◽  
Innovative Approach ◽  
Thinking Styles ◽  
New Approach ◽  
Learning Content ◽  
Hypermedia System ◽  
E Learning ◽  
Web Learning

Adaptive E-learning Hypermedia Systems (AEHS) are an innovative approach to a web learning experience delivery. They try to solve main shortcomings of classical hypermedia e-learning applications: “one-size-fits-all” approach and “lost-in-hyperspace” phenomena by adapting the learning content and its presentation to needs, goals, thinking styles and learning styles of every individual learner. This paper outlines a new approach to automatically detect learner's thinking and learning styles, and taking into account that thinking and learning styles may change during the learning process in an unexpected and unpredictable way. The authors' approach is based on the Felder Learning Styles Model and Hermann thinking styles model.

Open and Distance Learning Tools and Strategies

2008 ◽  
pp. 34-47
Author(s):  
Cristina Girona
Keyword(s):  
Teaching And Learning ◽  
Formal Education ◽  
Learning Experience ◽  
Learning Tools ◽  
Educational Models ◽  
E Learning ◽  
Technological Means ◽  
The University ◽  
Learning Institutions

There exists nowadays an enormous variety of models of e-leaning, from the technological, methodological and management perspective. At the university level, but also in company-training, in schools and formal education institutions, the different educational models appear, moving in a continuum from those who use technology as a complement or support to traditional attended sessions, to those that base the teaching and learning process in completely online environments. They try a variety of teaching methods while using differing degrees of virtualisation in the organisation (Bates, 2005). Years ago, when ICT in education started to be widely used, the success of the e-learning experience and the institutions themselves depended on their technological means; the platform was the most important of the model adopted by e-learning institutions. Initial efforts were put in market analysis aiming at finding out which was the best platform developed by ICT providers. Major investments in economical terms were dedicated to the acquisition of what was considered “the best” platform. Some years later, it was seen that institutions were different from the rest, and that not all educational platforms could cover all their needs. They realised that the success of their educational offer could not only be based on technology but in the learning materials provided. At that moment, the industry of online resources and hypermedia materials for educational uses grew up quickly. For some years, the success of e-learning mainly depended on the quality of the online materials provided, and that distinguish one institution from others.

Trend of E-Learning

2010 ◽  
Vol 8 (1) ◽  
pp. 69-88
Author(s):  
Neil Y. Yen ◽  
Timothy K. Shih ◽  
Qun Jin ◽  
Hui-Huang Hsu ◽  
Louis R. Chao
Keyword(s):  
Distance Learning ◽  
Learning Styles ◽  
Traditional Learning ◽  
Learning Tools ◽  
Web Based ◽  
Learning Standards ◽  
Multimedia Resources ◽  
Research Organizations ◽  
Technical Architecture ◽  
E Learning

With the improvement of internet technologies and multimedia resources, traditional learning has been replaced by distance learning, web-based learning or others’ e-learning learning styles. According to distance learning, there are many research organizations and companies who make efforts in developing the relevant systems. But they lack interoperability. The only way to reuse these applications is to redevelop them for specific purposes. In order to solve this situation and norm the various learning resources, IMS proposes a new e-learning standard named “Common Cartridge”. This standard not only integrates the past e-learning standards like LOM, SCORM and QTI but also proposes a technical architecture called Learning Tools Interoperability to allow applications to reuse different systems without reprogramming. In this paper, we firstly introduce the current e-learning environment. Then we pay attention on the usage of Common Cartridge standards and discuss the architecture of Learning Tools Interoperability. According to these standards, we will point out the e-learning standard that might be widely utilized in the future.

Adaptation algorithms for selecting personalised learning experience based on learning style and dyslexia type

2019 ◽  
Vol 53 (2) ◽  
pp. 189-200 ◽  
Author(s):  
Aisha Yaquob Alsobhi ◽  
Khaled Hamed Alyoubi
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Learning Experience ◽  
Assistive Technologies ◽  
Learning Systems ◽  
Learning System ◽  
Fixed Cost ◽  
Learning Material ◽  
Content Type ◽  
E Learning

PurposeThrough harnessing the benefits of the internet, e-learning systems provide flexible learning opportunities that can be delivered at a fixed cost at a time and place to suit the user. As such, e-learning systems can allow students to learn at their own pace while also being suitable for both distance and classroom-based learning activities. Adaptive educational hypermedia systems are e-learning systems that employ artificial intelligence. They deliver personalised online learning interventions that extend electronic learning experiences beyond a mere computerised book through the use of intelligence that adapts the content presented to a user according to a range of factors including individual needs, learning styles and existing knowledge. The purpose of this paper is to describe a novel adaptive e-learning system called dyslexia adaptive e-learning management system (DAELMS). For the purpose of this paper, the term DAELMS will be employed to describe the overall e-learning system that incorporates the required functionality to adapt to students’ learning styles and dyslexia type.Design/methodology/approachThe DAELMS is a complex system that will require a significant amount of time and expertise in knowledge engineering and formatting (i.e. dyslexia type, learning styles, domain knowledge) to develop. One of the most effective methods of approaching this complex task is to formalise the development of a DAELMS that can be applied to different learning styles models and education domains. Four distinct phases of development are proposed for creating the DAELMS. In this paper, we will discuss Phase 3 which is the implementation and some adaption algorithms while in future papers will discuss the other phases.FindingsAn experimental study was conducted to validate the proposed generic methodology and the architecture of the DAELMS. The system has been evaluated by group of university students studying a Computer Science related majors. The evaluation results proves that when the system provide the user with learning materials matches their learning style or dyslexia type it enhances their learning outcomes.Originality/valueThe DAELMS correlates each given dyslexia type with its associated preferred learning style and subsequently adapts the learning material presented to the student. The DAELMS represents an adaptive e-learning system that incorporates several personalisation options including navigation, structure of curriculum, presentation, guidance and assistive technologies that are designed to ensure the learning experience is directly aligned with the user's dyslexia type and associated preferred learning style.

scholarly journals Sustainable Education through E-Learning: The Case Study of iLearn2.0

2021 ◽  
Vol 13 (18) ◽  
pp. 10186
Author(s):  
Fu-Hsuan Chen
Keyword(s):  
Citizen Participation ◽  
Flipped Classroom ◽  
Learning Experience ◽  
Cloud Services ◽  
Learning Performance ◽  
Learning Tools ◽  
Course Content ◽  
Learning Platform ◽  
E Learning ◽  
Central Taiwan

The present paper studies a blended learning approach provided by a university in central Taiwan from 2018 to 2020. In this approach, a Moodle E-learning platform called iLearn2.0 was used along with an onsite classroom. iLearn 2.0 has four major features, including cloud services, mobile learning, flipped classroom, and data analysis. The platform was used during 2018–2020 in the Citizen Participation course, helping the researcher/teacher to design an interactive course content and aiding students to complete classroom activities through their devices. In total, 127 students enrolled in citizen participation courses were taught during 2018–2020, using different teaching methods. In 2018, students studied in a physical classroom. iLearn2.0 was integrated with the physical classroom for the 2019 course; lastly, the iLearn2.0 platform was used alone in 2020. To evaluate the effect of virtual teaching on the students’ performance, the researcher used summative assessment as the dependent variable. The findings show that the class that received the iLearn2.0- assisted teaching had a significantly better learning performance than the other two classes. However, when researchers used iLearn2.0 alone, both the scores and the feedback from students were lower than those in blended and face-to-face teaching. The results suggest the effectiveness of iLearn2.0 assistance, while learners’ performance did not show any significant change in a totally online class. Results were assessed in the view of sustainability, and three sustainability dimensions were found to be improved in the hybrid classroom. The researcher suggests that iLearn2.0 be integrated with other learning tools for maximum results, as it allows students to have a more diverse learning experience, strengthen sustainable learning, and grasp the progress of their courses and learning activities in a timely manner.

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