Integrating 'context' in e-learning systems design

2004 ◽  
Author(s):  
T. Nabeth ◽  
A.A. Angehrn ◽  
R. Balakrishnan
Keyword(s):  
Systems Design ◽  
Learning Systems ◽  
E Learning

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.

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.

Standards? What and Why?

2011 ◽  
pp. 14-38
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.

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.

HCI Education to Support Collaborative e-Learning Systems Design

eLearn ◽  
2010 ◽  
Vol 2010 (9) ◽  
Author(s):  
Niki Lambropoulos ◽  
Fintan Culwin ◽  
Margarida Romero
Keyword(s):  
Systems Design ◽  
Learning Systems ◽  
E Learning

Platform for Analysing and Encouraging Student Activity on Contest and E-learning Systems

2018 ◽  
Vol 12 ◽  
pp. 85-98
Author(s):  
Bojan Kostadinov ◽  
Mile Jovanov ◽  
Emil STANKOV
Keyword(s):  
Machine Learning ◽  
Data Collection ◽  
Educational Policy ◽  
Learning Systems ◽  
Data Sources ◽  
Or Education ◽  
Student Activity ◽  
The World ◽  
E Learning ◽  
Analyse Data

Data collection and machine learning are changing the world. Whether it is medicine, sports or education, companies and institutions are investing a lot of time and money in systems that gather, process and analyse data. Likewise, to improve competitiveness, a lot of countries are making changes to their educational policy by supporting STEM disciplines. Therefore, it’s important to put effort into using various data sources to help students succeed in STEM. In this paper, we present a platform that can analyse student’s activity on various contest and e-learning systems, combine and process the data, and then present it in various ways that are easy to understand. This in turn enables teachers and organizers to recognize talented and hardworking students, identify issues, and/or motivate students to practice and work on areas where they’re weaker.

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