Experience beyond knowledge: Pragmatic e-learning systems design with learning experience

2013 ◽  
Vol 29 (3) ◽  
pp. 747-758 ◽  
Author(s):  
Norliza Katuk ◽  
Jieun Kim ◽  
Hokyoung Ryu
Keyword(s):  
Learning Experience ◽  
Systems Design ◽  
Learning Systems ◽  
E Learning

scholarly journals Design of an Adaptive e-Learning System based on Multi-Agent Approach and Reinforcement Learning

2021 ◽  
Vol 11 (1) ◽  
pp. 6637-6644
Author(s):  
H. El Fazazi ◽  
M. Elgarej ◽  
M. Qbadou ◽  
K. Mansouri
Keyword(s):  
Reinforcement Learning ◽  
Learning Style ◽  
Learning Process ◽  
Learning Experience ◽  
Personalized Learning ◽  
Learning Objects ◽  
Learning Systems ◽  
Learning System ◽  
E Learning ◽  
Multi Agent

Adaptive e-learning systems are created to facilitate the learning process. These systems are able to suggest the student the most suitable pedagogical strategy and to extract the information and characteristics of the learners. A multi-agent system is a collection of organized and independent agents that communicate with each other to resolve a problem or complete a well-defined objective. These agents are always in communication and they can be homogeneous or heterogeneous and may or may not have common objectives. The application of the multi-agent approach in adaptive e-learning systems can enhance the learning process quality by customizing the contents to students’ needs. The agents in these systems collaborate to provide a personalized learning experience. In this paper, a design of an adaptative e-learning system based on a multi-agent approach and reinforcement learning is presented. The main objective of this system is the recommendation to the students of a learning path that meets their characteristics and preferences using the Q-learning algorithm. The proposed system is focused on three principal characteristics, the learning style according to the Felder-Silverman learning style model, the knowledge level, and the student's possible disabilities. Three types of disabilities were taken into account, namely hearing impairments, visual impairments, and dyslexia. The system will be able to provide the students with a sequence of learning objects that matches their profiles for a personalized learning experience.

From Usability to User Experience

2010 ◽  
pp. 244-259 ◽  
Author(s):  
Costin Pribeanu ◽  
Dragos Daniel Iordache
Keyword(s):  
Augmented Reality ◽  
Secondary Schools ◽  
User Experience ◽  
Learning Experience ◽  
Interaction Space ◽  
Learning Systems ◽  
Educational Systems ◽  
E Learning ◽  
Learning Scenarios ◽  
User Learning

Augmented Reality (AR) is merging real and virtual environments within a single interaction space. This tight integration of computer technologies into a real environment is creating new opportunities and challenges for the designers of e-learning systems as well as a new kind of user experience (UX) for the learner. More recently, AR-based educational systems were developed that are implementing learning scenarios for primary and secondary schools. An important goal of these novel teaching platforms is to enhance the students’ motivation to learn. This chapter reports on the perceived educational and motivational value of an AR-based learning scenario for chemistry based on the results of a user-centered formative usability evaluation. Quantitative and qualitative data were collected during two experiments with students from secondary schools. While the comparison between the two types of measure increases confidence in the evaluation results, the qualitative measures also provide a detailed description of the user learning experience.

Standards? What and Why?

2011 ◽  
pp. 3433-3448
Author(s):  
Phil Long ◽  
Frank Tansey
Keyword(s):  
Systems Design ◽  
Building Blocks ◽  
Learning Systems ◽  
Next Generation ◽  
Complex Learning ◽  
E Learning ◽  
Learning Software

Specifications define the nature of the interconnections between the distinct parts of complex learning systems, but not their boundaries.  Next generation CMS tools are emerging from standards discussions that challenge current e-learning systems design boundaries. They raise the prospect of a complex but smoothly functioning set of components and services that aggregate in ways that best serve individual communities of users. Users need to engage in the process to express their requirements for e-learning software. These building blocks, produced by a small number of organizations, are establishing the framework that will enable CMS environments to become vastly different than the CMS you might now be using.

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 Adaptive Upgradation of Personalized E-Learning Portal using Data Mining

2020 ◽  
Vol 10 (1) ◽  
pp. 224-227
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Clustering Algorithm ◽  
Learning Experience ◽  
Learning Systems ◽  
Machine Learning Techniques ◽  
Development Phase ◽  
Data Mining Techniques ◽  
E Learning ◽  
Learning Portal

Implementation of data mining techniques in elearning is a trending research area, due to the increasing popularity of e-learning systems. E-learning systems provide increased portability, convenience and better learning experience. In this research, we proposed two novel schemes for upgrading the e-learning portals based on the learner’s data for improving the quality of e-learning. The first scheme is Learner History-based E-learning Portal Up-gradation (LHEPU). In this scheme, the web log history data of the learner is acquired. Using this data, various useful attributes are extracted. Using these attributes, the data mining techniques like pattern analysis, machine learning, frequency distribution, correlation analysis, sequential mining and machine learning techniques are applied. The results of these data mining techniques are used for the improvement of e-learning portal like topic recommendations, learner grade prediction, etc. The second scheme is Learner Assessment-based E-Learning Portal Up-gradation (LAEPU). This scheme is implemented in two phases, namely, the development phase and the deployment phase. In the development phase, the learner is made to attend a short pretraining program. Followed by the program, the learner must attend an assessment test. Based on the learner’s performance in this test, the learners are clustered into different groups using clustering algorithm such as K-Means clustering or DBSCAN algorithms. The portal is designed to support each group of learners. In the deployment phase, a new learner is mapped to a particular group based on his/her performance in the pretraining program.

scholarly journals Sequencing educational resources with Seqins

2014 ◽  
Vol 11 (4) ◽  
pp. 1479-1497 ◽  
Author(s):  
Ricardo Queirós ◽  
Paulo Leal ◽  
José Campos
Keyword(s):  
Learning Experience ◽  
Learning Management Systems ◽  
Learning Systems ◽  
Educational Resources ◽  
Management Systems ◽  
Learning Resources ◽  
Automatic Evaluation ◽  
Learning Tools ◽  
Evaluation Systems ◽  
E Learning

Existing adaptive educational hypermedia systems have been using learning resources sequencing approaches in order to enrich the learning experience. In this context, educational resources, either expository or evaluative, play a central role. However, there is a lack of tools that support sequencing essentially due to the fact that existing specifications are complex. This paper presents Seqins as a sequencing tool of digital educational resources. Seqins includes a simple and flexible sequencing model that will foster heterogeneous students to learn at different rhythms. The tool communicates through the IMS Learning Tools Interoperability specification with a plethora of e-learning systems such as learning management systems, repositories, authoring and automatic evaluation systems. In order to validate Seqins we integrate it in an e-learning Ensemble framework instance for the computer programming learning domain.

scholarly journals THE EMOTIONAL IN E-LEARNING

2019 ◽  
Vol 13 (4) ◽  
Author(s):  
Raafat George Saadé ◽  
Dennis Kira
Keyword(s):  
Online Learning ◽  
Systems Design ◽  
Ease Of Use ◽  
Learning Systems ◽  
Perceived Ease Of Use ◽  
Emotional Factors ◽  
Software Interfaces ◽  
E Learning

This study investigates perceived ease of use and overall computer/internet experience as emotional factors that affect e-learning. Results suggest that online learning systems design should address typical software interfaces so that students feel more comfortable using them.

Evaluation Methods for E-Learning Applications in Terms of User Satisfaction and Interface Usability

2017 ◽  
pp. 427-448
Author(s):  
Nouzha Harrati ◽  
Imed Bouchrika ◽  
Zohra Mahfouf ◽  
Ammar Ladjailia
Keyword(s):  
User Satisfaction ◽  
Daily Life ◽  
Learning Experience ◽  
Educational Process ◽  
Learning Systems ◽  
Course Delivery ◽  
Assessment And Evaluation ◽  
Online Technology ◽  
E Learning ◽  
Interface Usability

The use of online technology has become ubiquitous and integral part of our daily life from education to entertainment. Because of the ubiquity of e-learning and vital influence for engineering the educational process, it is no surprise that many research studies are conducted to explore different aspects covering the use of e-learning in higher education. The assessment and evaluation aspects are considered arguably the most influential part for measuring the success and effectiveness of e-learning experience. As more and more universities worldwide have opted to use online technology for their course delivery, research in e-learning systems have attracted considerable interest in order to apprehend how effective and usable e-learning systems in terms of principles related to human computer interaction.

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