Modeling Dyslexic Students’ Motivation for Enhanced Learning in E-learning Systems

This chapter emphasizes the variety of today’s e-learning systems. They have both positive and negative characteristics. Several useful tools are common for these systems. The main part of this chapter contains a detailed description of e-learning systems and their tools. If a system is appropriate for the needs of the learner then it has more intelligent behavior and its tools are more specialized. Some systems have separate tools that act as standalone applications. Others contain built in tools. In this chapter, the e-learning tools are grouped by their functions. Owing to standardization efforts, the differences between the e-learning tools become their advantages, and the e-learning systems become interoperable. The intelligent learning management systems (ILMS) become a new way to integrate the benefits of the different e-learning systems. At the end of the chapter there is a short description of an ILMS named Multitutor. This represents a possible way of future e-learning systems development.

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Ontologies and Contracts in the Automation of Learning Object Management Systems

Web-Based Intelligent E-Learning Systems ◽

10.4018/978-1-59140-729-4.ch011 ◽

2006 ◽

pp. 216-234

Author(s):

Salvador Sanchez-Alonso ◽

Miguel-Ángel Sicilia ◽

Elena Garcia-Barriocanal

Keyword(s):

Learning Objects ◽

Learning Systems ◽

Learning Object ◽

Management Systems ◽

Shared Knowledge ◽

Knowledge Representations ◽

Learning Object Metadata ◽

E Learning ◽

Contractual Approach ◽

Enhanced Learning

Current standardized e-learning systems are centred on the concept of learning object. Unfortunately, specifications and standards in the field do not provide details about the use of well-known knowledge representations for the sake of automating some processes, like selection and composition of learning objects, or adaptation to the user or platform. Precise usage specifications for ontologies in e-learning would foster automation in learning systems, but this requires concrete, machine-oriented interpretations for metadata elements. This chapter focuses on ontologies as shared knowledge representations that can be used to obtain enhanced learning object metadata records in order to enable automated or semi-automated consistent processes inside Learning Management Systems. In particular, two efforts towards enhancing automation are presented: a contractual approach based on pre- and post-conditions, and the so-called process semantic conformance profiles.

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University Enhancement System using a Social Networking Approach: Extending E-learning

10.28945/3318 ◽

2009 ◽

Author(s):

Oludele Awodele ◽

Sunday Idowu ◽

Omotola Anjorin ◽

Adebunmi Adedire ◽

Victoria Akpore

Keyword(s):

Learning Environment ◽

Social Networking ◽

Learning Process ◽

Learning Systems ◽

Learning System ◽

The Internet ◽

Open Collaboration ◽

E Learning ◽

Learning Institutions ◽

Enhanced Learning

The proliferation of e-leaming systems in both learning institutions and companies has contributed a lot to the acquisition and application of new skills. With the growth in technology, especially the internet, e-learning systems are only getting better and having more impact on the users. This paper suggests an approach to e-learning that emphasizes active and open collaboration, and also the integration of other services that aid or contribute to the learning process. This approach aims at having an extended and enhanced learning environment that is tied or connected to other systems within the immediate environment or otherwise. We illustrate the possibility and usability of such system in a university, such that other important administrative systems are integrated into the e-learning system, and collaboration is open to both academic and non-academic personnel’s.

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

OLYMPIADS IN INFORMATICS ◽

10.15388/ioi.2018.07 ◽

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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