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.

Usage of augmented reality (AR) and development of e-learning outcomes: An empirical evaluation of students’ e-learning experience

2021 ◽  
pp. 104383
Author(s):  
Abdullah M. Baabdullah ◽  
Abdulellah A. Alsulaimani ◽  
Alhasan Allamnakhrah ◽  
Ali Abdallah Alalwan ◽  
Yogesh K. Dwivedi ◽  
...  
Keyword(s):  
Augmented Reality ◽  
Learning Outcomes ◽  
Learning Experience ◽  
Empirical Evaluation ◽  
E Learning

Techniques, Technologies and Patents Related to Intelligent Educational Systems

2011 ◽  
pp. 1-28
Author(s):  
Jim Prentzas ◽  
Ioannis Hatzilygeroudis
Keyword(s):  
Artificial Intelligence ◽  
Learning Systems ◽  
Future Research ◽  
Educational Systems ◽  
Learner Characteristics ◽  
Tutoring Systems ◽  
Artificial Intelligence Methods ◽  
E Learning ◽  
Future Research Directions ◽  
Intelligent Methods

E-learning systems play an increasingly important role in lifelong learning. Tailoring the learning process to individual needs is a key issue in such systems. Intelligent Educational Systems (IESs) are e-learning systems employing Artificial Intelligence methods to effectively adapt to learner characteristics. Main types of IESs are Intelligent Tutoring Systems (ITSs) and Adaptive Educational Hypermedia Systems (AEHSs) incorporating intelligent methods. In this chapter, the authors present technologies and techniques used in the primary modules of IESs and survey corresponding patents. They present issues and problems involving specific IES modules as well as the overall IES. The authors discuss solutions offered for such issues by Artificial Intelligence methods and patents. They also discuss categorization aspects of patents related to IESs and briefly present the work described in some representative patents. Lastly, the authors outline future research directions regarding IESs.

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 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.

An Australian University Implementing E-Learning System

2012 ◽  
Vol 3 (2) ◽  
pp. 18-26 ◽  
Author(s):  
Kamaljeet Sandhu
Keyword(s):  
Learning Process ◽  
Learning Experience ◽  
Prior Experience ◽  
Learning System ◽  
Task Environment ◽  
Web Based ◽  
Web Based Learning ◽  
E Learning ◽  
Learner Perception ◽  
User Learning

User learning experience may be considered as one of the prominent factors shaping the adoption of web-based systems. Web-based learners interfacing with large amounts of information the rationale is to deduce the effect in the current web-based task environment. Understanding Web-based learner perception on the basis of the prior experience with information may provide insights into what constitutes in driving those perceptions and their effect in the current and future web-based learning process. The paper demonstrates theoretical context of user learning experience with information and proceeds in an attempt to distinguish factors in using web-based systems.

Artificial Intelligence Methods in E-Learning

2019 ◽  
pp. 287-307
Author(s):  
Meltem Eryılmaz ◽  
Afaf Muftah Adabashi ◽  
Ali Yazıcı
Keyword(s):  
Artificial Intelligence ◽  
Teaching And Learning ◽  
Intelligent Tutoring ◽  
Learning Systems ◽  
Educational Systems ◽  
Educational Materials ◽  
Tutoring Systems ◽  
Challenges And Opportunities ◽  
E Learning ◽  
Effective Teaching And Learning

Gathering and extracting knowledge from the large amount of data available today is becoming more and more important in our information society, and similarly, learning is an essential important part of our everyday lives. The new requirements of the competing world and the development of more advanced technologies have also changed traditional educational systems, which now employ better and more effective teaching and learning methods. In this regard, the integration of artificial intelligence (AI) technologies in the field of education offers both great challenges and opportunities in building e-learning systems. E-learning systems allow learners to access the educational materials ubiquitously from anywhere at any time. Therefore, these systems have to become adaptive to the needs and preferences of each individual learner. This chapter presents a review of the important concepts and background for research to include introduction and examination of e-learning systems and intelligent tutoring systems (ITSs), available today.

scholarly journals Development of E-learning systems in technical universities

2018 ◽  
Vol 178 ◽  
pp. 07004
Author(s):  
Gavril Musca ◽  
Andrei Marius Mihalache ◽  
Lucian Tabacaru
Keyword(s):  
Collaborative Work ◽  
Direct Interaction ◽  
Learning Systems ◽  
Educational Systems ◽  
Learning Collaborative ◽  
Electronic Learning ◽  
Online Systems ◽  
Distant Learning ◽  
E Learning ◽  
Teaching Programs

Electronic learning, distant learning, collaborative work, mobile learning and internet based learning concepts are ever more present in superior learning systems. At this date for study or for finding different information we may access different search engines (like Google) where we can consult information based websites, blogs or online tutorials. Elearning systems can be grouped in online systems that have no direct interaction between the students and the professor, face to face systems that use both electronic resources and student-professor interaction, selflearning systems based on blogs, tutorials, teaching programs or web based systems. We can safely assume that e-learning is rapidly growing and that many universities have online free platforms developed for aiding students. The technological leap of current industries poses new challenges for the traditional educational systems. Efforts are known and acknowledged for changing and enhancing the existent educational systems and the orientation towards online learning processes.

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 Computer-Human Interaction and Collaboration: Challenges and Prospects

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 616
Author(s):  
Manuel Ortega
Keyword(s):  
Artificial Intelligence ◽  
Augmented Reality ◽  
Learning Environments ◽  
Learning Systems ◽  
Human Interaction ◽  
Collaborative Systems ◽  
Learner Support ◽  
La Mancha ◽  
E Learning ◽  
The University

Through a series of projects carried out by the Computer–Human Interaction and COllaboration (CHICO) group of the University of Castilla-La Mancha, some proposals are presented to improve the current e-Learning systems by making use of different paradigms of human-computer interaction. Synchronous and asynchronous collaborative systems, ubiquitous computing, and augmented reality can improve the current learning environments. The use of artificial intelligence mechanisms for both learner support and assessment complements these techniques. Emphasis is also placed on the use of automatic application generation techniques using models.

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