A multi-agent approach to adaptive learning using a structured ontology classification system

EHIMWENMA, Kennedy Efosa (2017). A multi-agent approach to adaptive learning using a structured ontology classification system. Doctoral, Sheffield Hallam University.

KEhimwenma_2017_PhD_Multiagent approach.pdf - Accepted Version
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Link to published version:: https://doi.org/10.7190/shu-thesis-00007


Diagnostic assessment is an important part of human learning. Tutors in face-to-face classroom environment evaluate students’ prior knowledge before the start of a relatively new learning. In that perspective, this thesis investigates the development of an-agent based Pre-assessment System in the identification of knowledge gaps in students’ learning between a student’s desired concept and some prerequisites concepts. The aim is to test a student's prior skill before the start of the student’s higher and desired concept of learning. This thesis thus presents the use of Prometheus agent based software engineering methodology for the Pre-assessment System requirement specification and design. Knowledge representation using a description logic TBox and ABox for defining a domain of learning. As well as the formal modelling of classification rules using rule-based approach as a reasoning process for accurate categorisation of students’ skills and appropriate recommendation of learning materials. On implementation, an agent oriented programming language whose facts and rule structure are prolog-like was employed in the development of agents’ actions and behaviour. Evaluation results showed that students have skill gaps in their learning while they desire to study a higher-level concept at a given time.

Item Type: Thesis (Doctoral)
Thesis advisor - Crowther, Paul
Thesis advisor - Beer, Martin [0000-0001-5368-6550]
Additional Information: Director of Studies: Dr Paul Crowther. Supervisor: Dr Martin Beer
Research Institute, Centre or Group - Does NOT include content added after October 2018: Sheffield Hallam Doctoral Theses
Identification Number: https://doi.org/10.7190/shu-thesis-00007
Depositing User: Hilary Ridgway
Date Deposited: 21 Feb 2018 16:25
Last Modified: 03 May 2023 02:00
URI: https://shura.shu.ac.uk/id/eprint/18747

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