A Compositional Adaptation-based Approach for Recommending Learning Resources in Software Development

Document Type : Original Article

Authors

1 Data Analysis & Processing Research Group, IT Research Faculty, ICT Research Institute, Iran

2 E-Content & E-Services Research Group, IT Research Faculty, ICT Research Institute, Iran

3 Computer Engineering Group, Science & Culture University, Iran

4 Electrical Engineering Group, Islamic Azad University South Tehran Branch, Iran

Abstract

In this paper, we discussed the application of a compositional adaptation approach to recommend learning resources to users in the area of software development.  This approach makes use of a domain-specific ontology in this area to find those words, which are used in the technical description of the stored cases. A point peculiar with representing cases in the proposed approach is to take into account the characteristics of included learning resources, which justify the way they support the essential operations in the case of solution. In this way, only those components that comply with user’s request would be considered in the final solution. In the paper, the performance of the proposed approach for recommending learning resources together with the status of user experience in his/ her interaction with the resulted recommending system, have been evaluated. Results demonstrate the fact that the learning resources through this approach are sufficiently beneficial for the users. Although the proposed approach has been applied for recommending learning resources in the area of software development, it can be equally applied to any technological area through developing domain-specific ontology for that area. This is mainly because any technological area has its own specific objects/ entities holding their own semantic similarities that finally lead to forming a domain-specific ontology for that area.

Keywords

Main Subjects

References

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International Journal of Engineering
Volume 35, Issue 7
TRANSACTIONS A: Basics
July 2022
Pages 1317-1329

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