Enhancing the Sustainability of OER Through Context-Aware and Intelligent E-Learning Systems Specific to India

2020 ◽  
pp. 18-29
Author(s):  
Shruti Tripathi ◽  
Archana Thakran ◽  
Monika Sharma
Keyword(s):  
Industry 4.0 ◽  
Learning Systems ◽  
Education Sector ◽  
Context Aware ◽  
Developing Economy ◽  
New Techniques ◽  
E Learning ◽  
Relevant Context ◽  
General Aspects

The chapter discusses the essence of “Openness” in the education sector and how it is transforming the landscape worldwide, along with the opportunities for educators and learners moving towards Industry 4.0. E-learning applications need to be dynamically adjusted not only according to the learner's knowledge but also depending on a relevant context which is imperative for a diverse country like India. The general aspects of OER, its meaning, and different CC licenses are discussed. The authors also deliberate upon the licensing issues in the use and reuse of OER. In a fast-developing economy, it is important to not only adopt the new techniques in education but also assess the strengths and weaknesses of OER usage.

E-Learning and the Semantic Web

2015 ◽  
pp. 66-100 ◽  
Author(s):  
Raadila Bibi Mahmud Hajee Ahmud-Boodoo
Keyword(s):  
Higher Education ◽  
Semantic Web ◽  
Success Factors ◽  
Comprehensive Analysis ◽  
Learning Systems ◽  
Education Sector ◽  
Learning Models ◽  
Semantic Web Technology ◽  
Critical Success ◽  
E Learning

A number of 3.0 e-learning systems have been proposed in the literature to capture the numerous benefits that the Semantic Web has to offer to the higher education sector. These 3.0 e-learning systems identify some essential Semantic Web characteristics that are either discussed as stand-alone factors or tend to revolve around the complexities of the Semantic Web technology and its implementation, often disregarding users' needs. Conversely, a comprehensive analysis of e-learning models for higher education in the literature revealed several Critical Success Factors (CSFs) that are relevant to the Semantic Web but often overlooked in 3.0 e-learning models. Consequently, this chapter provides an overview of the CSFs of e-learning relevant to 3.0 e-learning systems as well as an overview of the main Semantic Web characteristics for e-learning to define a new and combined set of 3.0 e-learning characteristics that will holistically represent 3.0 e-learning systems capturing the needs and expectations of users. The new initial 3.0 e-learning model proposed is evaluated within the higher education sector in Mauritius.

E-Learning and the Semantic Web

2016 ◽  
pp. 24-55
Author(s):  
Raadila Bibi Mahmud Hajee Ahmud-Boodoo
Keyword(s):  
Higher Education ◽  
Semantic Web ◽  
Success Factors ◽  
Comprehensive Analysis ◽  
Learning Systems ◽  
Education Sector ◽  
Learning Models ◽  
Semantic Web Technology ◽  
Critical Success ◽  
E Learning

A number of 3.0 e-learning systems have been proposed in the literature to capture the numerous benefits that the Semantic Web has to offer to the higher education sector. These 3.0 e-learning systems identify some essential Semantic Web characteristics that are either discussed as stand-alone factors or tend to revolve around the complexities of the Semantic Web technology and its implementation, often disregarding users' needs. Conversely, a comprehensive analysis of e-learning models for higher education in the literature revealed several Critical Success Factors (CSFs) that are relevant to the Semantic Web but often overlooked in 3.0 e-learning models. Consequently, this chapter provides an overview of the CSFs of e-learning relevant to 3.0 e-learning systems as well as an overview of the main Semantic Web characteristics for e-learning to define a new and combined set of 3.0 e-learning characteristics that will holistically represent 3.0 e-learning systems capturing the needs and expectations of users. The new initial 3.0 e-learning model proposed is evaluated within the higher education sector in Mauritius.

E-Learning and the Semantic Web

2016 ◽  
pp. 11-40
Author(s):  
Raadila Bibi Mahmud Hajee Ahmud-Boodoo
Keyword(s):  
Higher Education ◽  
Semantic Web ◽  
Success Factors ◽  
Comprehensive Analysis ◽  
Learning Systems ◽  
Education Sector ◽  
Learning Models ◽  
Semantic Web Technology ◽  
Critical Success ◽  
E Learning

A number of 3.0 e-learning systems have been proposed in the literature to capture the numerous benefits that the Semantic Web has to offer to the higher education sector. These 3.0 e-learning systems identify some essential Semantic Web characteristics that are either discussed as stand-alone factors or tend to revolve around the complexities of the Semantic Web technology and its implementation, often disregarding users' needs. Conversely, a comprehensive analysis of e-learning models for higher education in the literature revealed several Critical Success Factors (CSFs) that are relevant to the Semantic Web but often overlooked in 3.0 e-learning models. Consequently, this chapter provides an overview of the CSFs of e-learning relevant to 3.0 e-learning systems as well as an overview of the main Semantic Web characteristics for e-learning to define a new and combined set of 3.0 e-learning characteristics that will holistically represent 3.0 e-learning systems capturing the needs and expectations of users. The new initial 3.0 e-learning model proposed is evaluated within the higher education sector in Mauritius.

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.

Sign in / Sign up

Export Citation Format

Share Document