Ontology Validation & Utilisation For Personalised Feedback In Education

Demaidi, Mona Nabil (2018) Ontology Validation & Utilisation For Personalised Feedback In Education. Doctoral thesis, Birmingham City University.

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Abstract

Virtual Learning Environments provide teachers with a web-based platform to create different types of feedback which vary in the level of details given in the feedback content. Types of feedback can range from a simple correct or vice-versa to a detailed explanation about the reason why the correct answer is correct and the incorrect answer is incorrect. However, these environments usually follow the ‘one size fits all’ approach and provide all students with the same type of feedback regardless of students’ individual characteristics and the assessment question’s individual characteristics. This approach is likely to negatively affect students’ performance and learning gain. 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 have three drawbacks: firstly, creating the different types of feedback is a time consuming process, as the types of feedback are either hard-coded or auto-generated from a restricted set of solutions created by the teacher or a domain expert; secondly, they are domain dependent and cannot be used to auto-generate feedback across different educational domains; thirdly, they have not attempted any integration which takes into consideration both the characteristics of the assessment questions and the student’s characteristics. This thesis 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 has three aims: firstly, it uses any pre-existing domain ontology which is a knowledge representation of the educational domain to auto-generate assessment questions with different characteristics, in particular, questions aimed to assess students at different levels in Bloom’s taxonomy1; secondly, it associates each auto-generated question with specialised domain independent types of feedback; thirdly, it provides students with personalised feedback which adapts the types of feedback based on the student and the assessment question characteristics. OntoPeFeGe allowed the integration of student’s characteristics, the assessment question’s characteristics, and the personalised feedback, for the first time. The experimental results applying OntoPeFeGe in a real educational environment revealed that the personalised feedback particularly improved the performance of students with initial low background knowledge. Moreover, the personalised feedback improved students’ learning gain significantly at questions designed to assess the students at high levels in Bloom’s taxonomy. In addition, OntoPeFeGe is the first prototype to quantitatively analyse the quality of auto-generated questions and tests, and to provide question design guidance for developers and researchers working in the field of question generators. OntoPeFeGe could be applied to any educational field captured in an ontology. However, assessing how suitable the ontology is for generating questions and feedback, as well as how it represents the subject domain of interest, is a necessary requirement to using the ontology in OntoPeFeGe. Therefore, this thesis also presents a novel method termed Terminological ONtology Evaluator (TONE) which uses the educational corpus (e.g., textbooks and lecture slides) to evaluate the domain ontologies. TONE has been evaluated experimentally showing its potential as an evaluation method for educational ontologies.

Item Type: Thesis (Doctoral)
Additional Information: I am grateful to all the people who helped and inspired me during the preparation of this thesis, especially my supervisor Prof. Mohamed Medhat Gaber, who gave up his valuable time to help me extend the research work, analyse the experimental results, read my thesis draft, and offered comments that had led to many improvements. Thanks for Birmingham City University staff for providing me with help, support and facilities to carry out my research. I would also like to thank Dr. Nick Filer who supervised me at the beginning of my PhD until his plans for retirement, and the University of Manchester staff who facilitated the experimental studies. Words fail me to express my appreciation to my family who provided me with their support, understanding and endless love, through my PhD years. Father Nabil and mother Amneh you are my role model and have always inspired me to become the person I am today. Thank you for believing in me and giving me the chance to accomplish my dreams. Sisters Lana and Hala, brothers Iyad and Murad, thank you for being always there for me and standing by my side. I am lucky to have an amazing family like you. Finally, I would like to thank my dear friends.
Uncontrolled Keywords: Ontology, formative feedback, Bloom's taxonomy, question generation, feedback generation, personalisation
Subjects: G400 Computer Science
X900 Others in Education
Divisions: REF UoA Output Collections > Doctoral Theses Collection
Depositing User: Kip Darling
Date Deposited: 25 Jan 2019 16:28
Last Modified: 25 Jan 2019 16:28
URI: http://www.open-access.bcu.ac.uk/id/eprint/6928

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