Name
Title | ||
|---|---|---|
An adaptable and personalised E-learning system applied to computer science Programmes design |
Abstract | ||
|---|---|---|
With the rapid advances in E-learning systems, personalisation and adaptability have now become important features in the education technology. In this paper, we describe the development of an architecture for A Personalised and Adaptable E-Learning System (APELS) that attempts to contribute to advancements in this field. APELS aims to provide a personalised and adaptable learning environment to users from the freely available resources on the Web. An ontology was employed to model a specific learning subject and to extract the relevant learning resources from the Web based on a learner’s model (the learners background, needs and learning styles). The APELS system uses natural language processing techniques to evaluate the content extracted from relevant resources against a set of learning outcomes as defined by standard curricula to enable the appropriate learning of the subject. An application in the computer science field is used to illustrate the working mechanisms of the APELS system and its evaluation based on the ACM/IEEE computing curriculum. An experimental evaluation was conducted with domain experts to evaluate whether APELS can produce the right learning material that suits the learning needs of a learner. The results show that the produced content by APELS is of a good quality and satisfies the learning outcomes for teaching purposes. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s10639-018-9836-x | Education and Information Technologies |
Keywords | Field | DocType |
E-learning,Personalised E-learning,Learning styles,ACM/IEEE computing curriculum,Ontology,Natural language processing,Keyword extraction,Key phrases,Dependency relation,Parse tree,Linguistic methods | Educational technology,Dependency relation,Ontology,Learning styles,Computer science,Knowledge management,Curriculum,Learning environment,Multimedia,Cognitive style,Personalization | Journal |
Volume | Issue | ISSN |
24.0 | 2.0 | 1573-7608 |
Citations | PageRank | References |
1 | 0.34 | 13 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eiman Aeiad | 1 | 1 | 0.34 |
| Farid Meziane | 2 | 308 | 37.98 |