scholarly journals Learner Profile Design for Personalized E-Learning Systems

2009 ◽  
Author(s):  
Xu Wei ◽  
Jun Yan
Keyword(s):  
Learning Systems ◽  
E Learning ◽  
Learner Profile ◽  
Profile Design

scholarly journals Toward a Personalized Learning Path through a Services-Oriented Approach

2019 ◽  
Vol 14 (15) ◽  
pp. 52
Author(s):  
Mohamed Bendahmane ◽  
Brahim El Falaki ◽  
Mohammed Benattou
Keyword(s):  
Information Technologies ◽  
Service Oriented Architecture ◽  
Learning Systems ◽  
Individualized Learning ◽  
Learning Path ◽  
New Information ◽  
Service Oriented ◽  
E Learning ◽  
Learner Profile ◽  
Competency Based

In most existing E-learning systems, activities' content and order are presented in a static manner without taking into consideration the learners characteristics, profiles or competencies. The challenge is to adapt and regulate learning processes according to the learner profile by applying learning models that use new information technologies. There are several adaptation approaches of E-learning environments, such as, adaptive hypermedia system, semantic web, etc. In our proposed system, we adopted a Competency Based Approach to offer each learner an individualized learning path for the acquisition of the competence targeted on the basis of the collaborative filtering. Concerning the technological aspect, the system is implemented as a web services while adhering to a service-oriented architecture. This allows interoperability with heterogeneous learning systems

Improving Personalization In E-Learning Systems

2014 ◽  
Vol 4 (2) ◽  
pp. 75-84
Author(s):  
Assma Bezza ◽  
Amar Balla ◽  
Farhi Marir
Keyword(s):  
Knowledge Management ◽  
Ad Hoc ◽  
Research Work ◽  
User Profile ◽  
Learning Systems ◽  
Learning Content ◽  
E Learning ◽  
Learner Profile ◽  
Knowledge Content ◽  
User Intervention

Individual learners have different requirements and characteristics, and as a result learning content should be able to be personalized and adaptable to the e-learner' profile. Little research work undertaken to tackle this issue, and it has been limited to ad-hoc work on personalizing, and adapting learning content in e-Learning. This paper presents two methods for modeling user profile and for personalizing and adapting a given content to match that profile: inductive (without user intervention) and deductive (with user intervention). These methods will be used as a base to review and classify research work undertaken on personalizing content in the domain of knowledge management and e-learning systems. Based on these reviews, especially those undertaken in personalizing knowledge content in knowledge management systems, the paper proposes a comprehensive approach for personalizing learning content.

scholarly journals Creating an Advanced Web Based Environment using Semantic Web

2017 ◽  
pp. 38-44 ◽  
Author(s):  
Panchajanyeswari M Achar
Keyword(s):  
Learning Environment ◽  
Learning Objects ◽  
Learning Systems ◽  
Personalized Recommendation ◽  
Learning Material ◽  
Web Based ◽  
Learning Content ◽  
User Query ◽  
E Learning ◽  
Learner Profile

E-learning systems are of no help to the users if there are no powerful search engines and browsing tools to assist them. Most of the current web-based learning systems are closed systems where the courses and the learning material are fixed. The only thing that is dynamic is that the organization of the learning content is adapted to allow individualized learning environment. The learners of web-based e-learning systems belong to different categories based on their skills, background, preferences and learning styles. This paper focuses on personalized semantic search and recommending learning content that are appropriate to the learning environment. The semantic and personalized search of the learning content is based on comparison of the learner profile. The learner profile depends on re individual learning style of the user and learning objects’ metadata. This concept needs to be represented both in the learner profile as well as learning object description as certain data structures. Personalized recommendation of learning objects uses an approach to determine a more suitable relationship between learning objects and learning profiles. Thus, it may advise a learner with most suitable learning objects. Semantic learning objects search is based on the query expansion of the user query and by using the semantic similarity to retrieve semantic matched learning objects.

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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