scholarly journals Requirements Monitoring and Diagnosis for Improving Adaptive E-Learning Systems Design

10.28945/4270 ◽  
2019 ◽  
Vol 18 ◽  
pp. 161-184 ◽  
Author(s):  
Lamiae dounas ◽  
Camille Salinesi ◽  
Omar EL beqqali
Keyword(s):  
Adaptive Systems ◽  
Probabilistic Reasoning ◽  
Systems Design ◽  
Learning Systems ◽  
Learning System ◽  
Future Research ◽  
Design Problems ◽  
Systems Research ◽  
E Learning ◽  
Systems Requirements

Aim/Purpose: In this paper, we highlight the need to monitor and diagnose adaptive e-learning systems requirements at runtime to develop a better understanding of their behavior during learning activities and improve their design. Our focus is to reveal which learning requirements the adaptive system is satisfying while still evolving and to provide specific recommendations regarding what actions should be taken and which relevant features are needed to help meet the specified learning requirements. Background: Adaptive e-learning systems research has long focused on user modeling and social learning to personalize each learner experience, while fewer instruments are reported to assess the quality of the solutions provided by such adaptive systems and to investigate their design problems. The design problems may emerge due to ever-evolving requirements being statically specified at design stages and to the changing environments that can be difficult to control and observe. The combination of some or all of these factors can lead to a definition of inconsistent or insufficient adaptation rules, which in turn may prevent these systems from providing appropriate resources to learners even if the needed ones have been accounted for within the knowledge space. Methodology: An empirical study has been performed to check and validate the behavior of a real-world adaptive e-learning system under four stated requirements. The study used a novel monitoring and diagnosing tool that reads the collected data from the system and checks its behavior against constraints that are derived automatically from the requirements specification. Contribution: The results provide statistical insights and highlight some issues related to requirements compliance at runtime, which helped us detect unforeseen instructional design issues. Recommendations for Practitioners: The study suggests that diagnosing requirements compliance at runtime can be an essential means to increase the confidence about their adaptive e-learning systems capabilities at runtime. Recommendation for Researchers: The study suggests that further research for developing specific indicators related to requirements compliance is needed in the field of adaptive e-learning systems. Future Research: Future work includes the study of possible improvement of our diagnostic tool using probabilistic reasoning.

scholarly journals E-Learning Systems Requirements Elicitation

2017 ◽  
Vol 7 (1) ◽  
pp. 44-55 ◽  
Author(s):  
Shaikha B. AlKhuder ◽  
Fatma H. AlAli
Keyword(s):  
Knowledge Exchange ◽  
Requirements Elicitation ◽  
Learning Systems ◽  
Learning System ◽  
Functional Requirements ◽  
Training And Education ◽  
E Learning ◽  
Systems Requirements ◽  
The Right ◽  
Simple Materials

Training and education have evolved far beyond black boards and chalk boxes. The environment of knowledge exchange requires more than simple materials and assessments. This article is an attempt of parsing through the different aspects of e-learning, understanding the real needs, and conducting the right requirements to build the appropriate e-learning system. E-learning systems, unlike the normally developed systems, have variable customers and on-going demands. It is not the easiest task to elicit unambiguous functional and non-functional requirements for such systems. However, a brief exploration of some of the e-learning characteristics may tremendously decrease the difficulty of prioritizing the most important requirements.

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 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.

Factors Associated with students’ Intention to use E-Learning System: A Study from Public Universities of Lahore

2021 ◽  
Vol 9 (2) ◽  
pp. 167-173
Author(s):  
Shagufta Shaheen ◽  
Mubasher Muhammad Kamran ◽  
Saira Naeem ◽  
Tahir Mahmood
Keyword(s):  
Undergraduate Students ◽  
Public Universities ◽  
Positive Impact ◽  
Cross Sectional Study ◽  
Learning Systems ◽  
Learning System ◽  
Policy Implications ◽  
Intention To Use ◽  
Cross Sectional ◽  
E Learning

The study's primary purpose is to explore the factors affecting the students' intention to use e-learning systems in the COVID pandemic. The model of the “Unified theory of acceptance and use of technology” (UTAUT) was used as a theoretical underpinning. The Independent variables include “performance expectancy, effort expectancy, social influence, facilitating condition,” and the dependent variable is the intention to use e-learning systems. The quantitative data were collected from the postgraduate and undergraduate students of the public universities of Lahore. A total of n=411 students were approached, out of which the responses of only 399 were considered valid and were used for Multiple linear regression through SPSS 25. It was a cross-sectional study. It was found that almost all constructs of the model have a significant positive impact on intention to use e-learning systems.  The study's main contribution is exposing the factors that affect the acceptance and use of e-learning systems. This study has several policy implications for policy experts of higher education”.

scholarly journals POSSIBILITIES OF DEVELOPING INTERACTIVE E-LEARNING SYSTEM FOR BORDER GUARDS TRAINING IN THE STATE BORDER GUARD COLLEGE OF THE REPUBLIC OF LATVIA

2020 ◽  
Vol 3 (8) ◽  
pp. 45-53
Author(s):  
Mārtiņš Spridzāns ◽  
Jans Pavlovičs ◽  
Diāna Soboļeva
Keyword(s):  
Scientific Literature ◽  
Online Communication ◽  
The State ◽  
Learning Systems ◽  
Learning System ◽  
Digital Learning ◽  
E Learning ◽  
Research Findings ◽  
The Republic ◽  
Learning Development

Efficient use of educational technology and digital learning possibilities has always been the strategic area of high importance in border guards training at the State Border Guard College of Latvia. Recently, issues related to training during the Covid-19, have spurred and revived the discussion, topicality and practical need to use the potential of e-learning opportunities which brought up unexpected, additional, previously unsolved, unexplored, challenges and tasks to border guards training. New opportunities and challenges for trainers, learners and administration of training process both in online communication and learning administration contexts. In order to find out and define further e-learning development possibilities at the State Border Guard College the authors of this research explore the scientific literature on the current research findings, methodologies, approaches on developing interactive e-learning systems in educational contexts, particularly within the sphere of law enforcement. Based on scientific literature research findings authors put forward suggestions on improving the e-learning systems for border guards training.

scholarly journals INCA: A cognitive multi-agents architecture for designing intelligent & adaptive learning systems

2005 ◽  
Vol 2 (2) ◽  
pp. 99-114 ◽  
Author(s):  
Thierry Nabeth ◽  
Liana Razmerita ◽  
Albert Angehrn ◽  
Claudia Roda
Keyword(s):  
Knowledge Sharing ◽  
Adaptive Learning ◽  
Management Practices ◽  
Learning Systems ◽  
Learning System ◽  
Cognitive Agents ◽  
E Learning ◽  
Personal Agent ◽  
Adaptive Learning Systems ◽  
Multi Agents

This paper presents a cognitive multi-agents architecture called Intelligent Cognitive Agents (InCA) that was elaborated for the design of Intelligent Adaptive Learning Systems. The InCA architecture relies on a personal agent that is aware of the user's characteristics, and that coordinates the intervention of a set of expert cognitive agents (such as story telling agents, assessment agents, stimulation agents or help agents). This InCA architecture has been applied for the design of K"InCA, an e-learning system aimed at helping people to learn and adopt knowledge-sharing management practices.

Role of Usability in E-Learning System

2015 ◽  
pp. 295-312
Author(s):  
Muhammad Ahmad Amin ◽  
Saqib Saeed
Keyword(s):  
Web Application ◽  
Learning Systems ◽  
Learning System ◽  
Web Application Security ◽  
Usability Problems ◽  
Future Version ◽  
E Learning ◽  
Testing Techniques ◽  
User Friendly

Amongst open-source e-learning systems, WebGoat, a progression of OWASP, provides some room for teaching the penetration testing techniques. Yet, it is a major concern of its learners as to whether the WebGoat interface is user-friendly enough to help them acquaint themselves of the desired Web application security knowledge. This chapter encompasses a heuristic evaluation of this application to acquire the usability of contemporary version of WebGoat. In this context of evaluation, the in-house formal lab testing of WebGoat was conducted by the authors. The results highlight some important issues and usability problems that frequently pop-up in the contemporary version. The research results would be pivotal to the embedding of an operational as well as user-friendly interface for its future version.

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.

Value-Satisfaction Grid of E-Learning Systems

2006 ◽  
pp. 116-122
Author(s):  
Yair Levy
Keyword(s):  
System Approach ◽  
Two Dimensions ◽  
Learning Systems ◽  
Learning System ◽  
Assessment Tools ◽  
Mediated Learning ◽  
Perceived Effectiveness ◽  
E Learning ◽  
Significant Achievement ◽  
Complete Set

This chapter provides the rationale of the first of three tools suggested in this book to assess value and satisfaction of e-learning systems in order to provide an assessment of the effectiveness of such systems. The other two tools are presented in the following chapter. The first tool proposed by the conceptual model is the Value-Satisfaction grid which aggregates the learners’ value and satisfaction with e-learning systems in order to indicate the learners’ perceived effectiveness of e-learning systems. The Value-Satisfaction grid also helps indicate the action and improvement priorities that are needed for the characteristics and dimensions of an e-learning system under study. A proposed method of aggregation of learners’ perceived value of e-learning systems and satisfaction with e-learning systems to construct the Value-Satisfaction grid and the two tools presented in the following chapter is also presented in this chapter. The understanding of the Value-Satisfaction grid provides the first building block toward a complete set of assessment tools of learners’ perceived effectiveness of e-learning systems. The development of this set of tools is a significant achievement as scholars have suggested that prior research in technology mediated learning (TML) lacked the overall system approach and concentrated only on one or two dimensions at a time (Alavi & Leidner, 2001a, p. 9).

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