OntoPeFeGe: Ontology-based Personalised Feedback Generator

Demaidi, Mona Nabil and Gaber, Mohamed Medhat and Filer, Nick (2018) OntoPeFeGe: Ontology-based Personalised Feedback Generator. IEEE Access. pp. 1-20. ISSN 2169-3536

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Abstract

Virtual Learning Environments provide teachers with a web-based platform to create different types of feedback. These environments usually follow the 'one size fits all' approach and provide students with the same feedback. Several personalised feedback frameworks have been proposed which adapt the different types of feedback based on the student characteristics and/or the assessment question characteristics. The frameworks are intradisciplinary, neglect the characteristics of the assessment question, and either hard-code or auto-generate the types of feedback from a restricted set of solutions created by a domain expert. This paper contributes to research carried out on personalised feedback frameworks by proposing a generic novel system which is called the Ontology-based Personalised Feedback Generator (OntoPeFeGe). OntoPeFeGe addressed the aforementioned drawbacks using an ontology which is a knowledge representation of the educational domain. It integrated several generation strategies and templates to traverse the ontology and auto-generate the questions and feedback. The questions have different characteristics, in particular, they aim to assess students at different levels in Bloom's taxonomy. Each question is associated with different types of feedback that range from verifying student's answer towards giving the student more details related to the answer. The feedback auto-generated in OntoPeFeGe is personalised using a rule-based algorithm which takes into account the student characteristics and the assessment question characteristics. The personalised feedback in OntoPeFeGe was quantitatively evaluated on 88 undergraduate students. The results revealed that the personalised feedback significantly improved the performance of students with low background knowledge. In addition, the feedback was evaluated qualitatively using questionnaires provided to teachers and students. The results showed that teachers and students were satisfied about the feedback.

Item Type: Article
Subjects: G400 Computer Science
G700 Artificial Intelligence
Divisions: Faculty of Computing, Engineering and the Built Environment
Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology > Enterprise Systems
UoA Collections > REF2021 UoA11: Computer Science and Informatics
Depositing User: Mohamed Gaber
Date Deposited: 14 Jun 2018 13:45
Last Modified: 21 Jun 2018 09:10
URI: http://www.open-access.bcu.ac.uk/id/eprint/6044

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