A Conceptual Model of E-learning Systems Success and Its Implication for Future Research

2018 ◽  
Author(s):  
Yonas Hagos ◽  
Salehu Anteneh ◽  
Monica J. Garfield
Keyword(s):  
Conceptual Model ◽  
Learning Systems ◽  
Future Research ◽  
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.

The General Conceptual Model

2006 ◽  
pp. 90-115
Author(s):  
Yair Levy
Keyword(s):  
Conceptual Model ◽  
Research Question ◽  
Individual Characteristics ◽  
Learning Systems ◽  
Perceived Effectiveness ◽  
Specific Research ◽  
Perceived Satisfaction ◽  
E Learning ◽  
Research Questions ◽  
The Individual

In this chapter, a general theoretical model is proposed that links learners’ satisfaction and learners’ value of e-learning systems in order to assess learners’ perceived effectiveness of such systems. The central research question in this study is: Is there a relationship between learners’ perceived satisfaction with e-learning systems and learners’ perceived value for learners’ perceived effectiveness of e-learning systems? The significance of the value construct in the context of e-learning systems has never been evaluated. How the value of e-learning systems relates to other constructs, such as satisfaction with e-learning systems and ultimately whether the value of e-learning systems can be used to indicate learners’ perceived IS effectiveness remains open. In this chapter, a general conceptual model or framework is proposed to address this phenomenon in the context of e-learning systems. The proposed model or framework will provide procedures to identify and measure the key constructs (satisfaction with e-learning systems, value of e-learning systems, and effectiveness of e-learning systems). This chapter also defines precisely the individual characteristics and four major dimensions (categories) for evaluating value of e-learning systems and satisfaction with e-learning systems based on comprehensive literature reviewed in Chapters II and III. Additionally, this chapter proposes five specific research questions that are addressed in Chapter VII. Two additional specific research questions are proposed in Chapters V and VI.

Review of Data Mining Techniques and Parameters for Recommendation of Effective Adaptive E-Learning System

2017 ◽  
pp. 1-23 ◽  
Author(s):  
Renuka Mahajan
Keyword(s):  
Data Mining ◽  
Recommender System ◽  
Learning Systems ◽  
Learning System ◽  
Future Research ◽  
Comprehensive List ◽  
Research Areas ◽  
New Models ◽  
E Learning ◽  
Research Intensity

This chapter revolves around the synthesis of three research areas- data mining, personalization, recommendation systems and adaptive e-Learning systems. It also introduces a comprehensive list of parameters, extricated by reviewing the existing research intensity during the period of 2000 to October 2014, for understanding what should be essential parameters for adapting an e-learning. In general, we can consider and answer few questions to answer this body of literature ‘what' can be adapted? What can we adapt to? How do we adapt? This review tries to answer on ‘what' can be adapted. Thus, it advances earlier personalization studies. The gaps in the previous studies in building adaptive e-learning systems were also reviewed. It can help in designing new models for adaptation and formulating novel recommender system techniques. This will provide a foundation to industry experts and scientists for future research in adaptive e-learning.

scholarly journals Systematic Literature Review of E-Learning Capabilities to Enhance Organizational Learning

2021 ◽  
Author(s):  
Michail N. Giannakos ◽  
Patrick Mikalef ◽  
Ilias O. Pappas
Keyword(s):  
Organizational Learning ◽  
Empirical Studies ◽  
Learning Technologies ◽  
Learning Systems ◽  
Future Research ◽  
Learning Capabilities ◽  
E Learning ◽  
Tremendous Importance ◽  
Training Courses ◽  
Organizational Value

AbstractE-learning systems are receiving ever increasing attention in academia, business and public administration. Major crises, like the pandemic, highlight the tremendous importance of the appropriate development of e-learning systems and its adoption and processes in organizations. Managers and employees who need efficient forms of training and learning flow within organizations do not have to gather in one place at the same time or to travel far away to attend courses. Contemporary affordances of e-learning systems allow users to perform different jobs or tasks for training courses according to their own scheduling, as well as to collaborate and share knowledge and experiences that result in rich learning flows within organizations. The purpose of this article is to provide a systematic review of empirical studies at the intersection of e-learning and organizational learning in order to summarize the current findings and guide future research. Forty-seven peer-reviewed articles were collected from a systematic literature search and analyzed based on a categorization of their main elements. This survey identifies five major directions of the research on the confluence of e-learning and organizational learning during the last decade. Future research should leverage big data produced from the platforms and investigate how the incorporation of advanced learning technologies (e.g., learning analytics, personalized learning) can help increase organizational value.

scholarly journals The research trends in recommender systems for e-learning

2019 ◽  
Vol 14 (1) ◽  
pp. 12-27
Author(s):  
Jiemin Zhong ◽  
Haoran Xie ◽  
Fu Lee Wang
Keyword(s):  
Learning Outcomes ◽  
Recommender Systems ◽  
Learning Systems ◽  
Learning System ◽  
Future Research ◽  
Content Type ◽  
Evaluation Indicator ◽  
Recommendation Algorithms ◽  
E Learning ◽  
Key Indicators

Purpose A recommendation algorithm is typically applied to speculate on users’ preferences based on their behavioral characteristics. The purpose of this paper is to provide a systematic review of recommendation systems by collecting related journal articles from the last five years (i.e. from 2014 to 2018). This paper aims to study the correlations between recommendation technologies and e-learning systems. Design/methodology/approach The paper reviews the relevant articles using five assessment aspects. A coding scheme was put forward that includes the following: the metrics for the e-learning system, the evaluation metrics for the recommendation algorithms, the recommendation filtering technology, the phases of the recommendation process and the learning outcomes of the system. Findings The research indicates that most e-learning systems will adopt the adaptive mechanism as a primary metric, and accuracy is a vital evaluation indicator for recommendation algorithms. In existing e-learning recommender systems, the most common recommendation filtering technology is hybrid filtering. The information collection phase is an important process recognized by most studies. Finally, the learning outcomes of the recommender system can be achieved through two key indicators: affections and correlations. Originality/value The recommendation technology works effectively in closing the gap between the information producer and the information consumer. This technology could help learners find the information they are interested in as well as send them a valuable message. The opportunities and challenges of the current study are discussed; the results of this study could provide a guideline for future research.

The Role of Multi-Agent Social Networking Systems in Ubiquitous Education

2010 ◽  
pp. 72-88 ◽  
Author(s):  
Jonathan Bishop
Keyword(s):  
Social Networking ◽  
Information Science ◽  
Learning Systems ◽  
Learning System ◽  
Future Research ◽  
System A ◽  
E Learning ◽  
Multi Agent ◽  
Social Networking Technology ◽  
Networks Of Practice

Knowledge it could be argued is constructed from the information actors pick up from the environments they are in. Assessing this knowledge can be problematic in ubiquitous e-learning systems, but a method of supporting the critical marking of e-learning exercises is the Circle of Friends social networking technology. Understanding the networks of practice in which these e-learning systems are part of requires a deeper understanding of information science frameworks. The Ecological Cognition Framework (ECF) provides a thorough understanding of how actors respond to and influence their environment. Forerunners to ecological cognition, such as activity theory have suggested that the computer is just a tool that mediates between the actor and the physical environment. Utilising the ECF it can be seen that for an e-learning system to be an effective teacher it needs to be able to create five effects in the actors that use it, with those being the belonging effect, the demonstration effect, the inspiration effect, the mobilisation effect, and the confirmation effect. In designing the system a developer would have to consider who the system is going to teach, what it is going to teach, why it is teaching, which techniques it is going to use to teach and finally whether it has been successful. This chapter proposes a multi-agent e-learning system called the Portable Assistant for Intelligently Guided Education (PAIGE), which is based around a 3D anthropomorphic avatar for educating actors ubiquitously. An investigation into the market for PAIGE was carried out. The data showed that those that thought their peers were the best form of support were less likely to spend more of their free time on homework. The chapter suggests that future research could investigate the usage of systems like PAIGE in educational settings and the effect they have on learning outcomes.

The role of usability on e-learning user interactions and satisfaction: a literature review

2019 ◽  
Vol 21 (3) ◽  
pp. 368-394 ◽  
Author(s):  
Asela Indunil Gunesekera ◽  
Yukun Bao ◽  
Mboni Kibelloh
Keyword(s):  
User Satisfaction ◽  
Analytical Framework ◽  
Learning Systems ◽  
Future Research ◽  
Instructional Designers ◽  
Analysis Framework ◽  
Research Attention ◽  
Content Type ◽  
E Learning ◽  
Different Dimensions

Purpose The purpose of this study is to review the effect of usability factors on e-learning user relationships, namely, student–student interaction (SSI), student–instructor interaction (SII) and student–content interaction (SCI), in the existing e-learning literature. Further, this study intended to identify whether usability contributes to the satisfaction of e-learners. Design/methodology/approach This study has undertaken a systematic review using the PRISMA methodology to filter the literature in the domain of e-learning with respect to usability concerns using six databases. An analytical framework has been formulated to evaluate the literature against different dimensions of interactions and usability. Findings Results reveal that while SSI has grabbed 71.4 per cent research attention with respect to usability factors of e-learning systems, SCI has been given the least focus, i.e. 26.6 per cent. According to the results, e-learning systems’ usability issues influence the user relationships and affect the user satisfaction, which will lead to lack of user continuity. Practical implications The findings of this review will provide insights to instructional designers to construct more satisfied learning content for the users. The analysis framework of this study will encourage researchers to drive future research in e-learning along with the concern of usability. Originality/value This research emphasizes on the importance of SCI to focus future e-learning research on a different angle, in addition to SSI and SII. The analysis framework of this study will provide different dimensions, specifically for the empirical research in the domain of e-learning.

Ensuring Usability in International Web-Based E-Learning Systems

2004 ◽  
pp. 275-290
Author(s):  
Andy Smith
Keyword(s):  
Cultural Differences ◽  
Ethical Issues ◽  
Evaluation Studies ◽  
Learning Systems ◽  
Future Research ◽  
Cultural Issues ◽  
Web Based ◽  
E Learning ◽  
Cultural Usability ◽  
New Research

E-learning systems cross national borders, are used by people in different cultures, and are applied in culturally different contexts. A number of factors highlight the need to be acutely aware of the role of culture as part of the whole e-learning environment. In mainstream systems development, effective strategies that address cultural issues in both the product and the process of development now often are critical to systems success. In relation to the product of development, cultural differences in signs, meanings, actions, conventions, norms or values, etc., raise new research issues ranging from technical usability to methodological and ethical issues of culture in information systems. In relation to the process of development, cultural differences affect the manner in which users are able to participate in design and to act as subjects in evaluation studies. This chapter provides a summary of the main issues within cross-cultural usability with an emphasis on web-based systems. It discusses the application of generic models and theories to the field of e-learning systems and provides an agenda for future research required to ensure usability in international web-based e-learning systems.

scholarly journals Gamification Framework for E-Learning Systems in Higher Education

2021 ◽  
Vol 15 (2) ◽  
pp. 184-190
Author(s):  
Andrija Bernik
Keyword(s):  
Higher Education ◽  
Conceptual Model ◽  
Computer Games ◽  
Scientific Literature ◽  
Higher Education Institutions ◽  
Learning Systems ◽  
E Learning ◽  
Logical Representation

This paper explains the concept of gamification, lists the current models according to which educational e-courses can be designed, and proposes a conceptual eRIOOS model aimed at higher education. The aim of the research as well as the purpose of creating a conceptual model of gamification is to standardize the elements of computer games that can be used in educational e-courses at higher education institutions. In the preparation of this research, the emphasis was placed on the invention and analysis of professional and scientific literature for creating a conceptual model. The model contains a logical representation of two levels of complexity. Three separate e-courses have been created in different courses within the two University institutions, which serve as a tool to check the correctness of the conceptual eRIOOS model. The result of the research is a confirmed conceptual model that is suitable for creating Moodle e-courses of IT teaching orientation in higher education institutions.

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