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Exploring the relationship between Tinto’s Personal Attributes and retention in Computer Science degree programmes (Advert Reference: SF18/CIS/PRICKETT)

Project Description

The aim of this project is to explore the relationship between Computer Science students’ personal attributes and their retention at University. This project builds upon the work carried out by Tinto, who produced a model of student attrition which suggested that student retention is influenced by student attributes and experience combined with institutional factors. The student attributes are said to include previous educational input, family history and the individual’s own abilities whereas the institutional factors focus on achievement while at university and faculty interactions. This model proposes that the student attributes and institutional factors combine to influence integration (social and academic) which is key to success. Personal factors have been explored less than institutional factors.

In particular this project intends to focus upon positive psychology or related other personal attribute and the relationship with retention. This would build upon a similar approach to previous work but focusing on individual student attributes. It is intended the research would employ an existing measure(s), validate their use in this context and then develop appropriate prediction models using advanced information science, data mining, machine learning and artificial intelligence approaches. For instance, the most relevant attributes regarding retention can be discovered by employing feature selection or clustering approaches, and the prediction model can be developed using multi-criteria decision-making systems.

This project is a collaboration between three of the departments’ research groups (Digital Learning Lab, Computational Intelligence and Visual Computing and Information Management and Data Analytics) and will be co-supervised by academics from the three areas. It will be cross-disciplinary research and would suit a computer / information scientist excited about education or someone with an educational background who is excited by computer / information science.

Dr Tom Prickett is the Faculty Director for Portfolio Development, Review and Approval, a Senior Fellow of the Higher Education Academy and member of the Digital Learning Lab.

Dr. Longzhi Yang is the Director of Teaching and Learning at the Department and an Expert in Artificial Intelligence and member of the Computational Intelligence and Visual Computing research group. His research mainly focuses on decision-making systems using AI approaches, especially under uncertain environment.

Dr Morgan Harvey is the programme leader for the BSc/Comp Computer Science framework and member of the Information Management and Data Analytics research group. His research work involves allowing people to effectively model and utilise data sources they interact with every day

Please note eligibility requirement:

* Academic excellence of the proposed student i.e. 2:1 (or equivalent GPA from non-UK universities [preference for 1st class honours]) in Computer Science, Information Science or Education; or a Masters (preference for Merit or above); or APEL evidence of substantial practitioner achievement.
* Appropriate IELTS score, if required

This project is well suited to motivated and hard-working candidates with a keen interest in Computer / Information Science or Education. The applicant should have excellent communication skills including proven ability to write in English.

For further details of how to apply, entry requirements and the application form, see

Please note: Applications that do not include a research proposal of approximately 1,000 words (not a copy of the advert), or that do not include the advert reference (e.g. SF18/CIS/PRICKETT) will not be considered.

Start Date: 1 March 2019 or 1 June 2019 or 1 October 2019

Northumbria University takes pride in, and values, the quality and diversity of our staff. We welcome applications from all members of the community. The University hold an Athena SWAN Bronze award in recognition of our commitment to improving employment practices for the advancement of gender equality and is a member of the Euraxess network, which delivers information and support to professional researchers.

Funding Notes

This is an unfunded research project.


D Brazier, M Harvey (2017) Strangers in a Strange Land: A Study of Second Language Speakers Searching for e-Services, Proceedings of the 2017 ACM SIGIR Conference on Human Information Interaction & Retrieval (CHIIR)

M Harvey, M Pointon (2018) Noisy Signals: Understanding the Impact of Auditory Distraction on Web Search Tasks, Proceedings of the 2018 Conference on Human Information Interaction & Retrieval, ACM, p 241-244

J. Li, L. Yang, Y. Qu, and G. Sexton (2018), An extended Takagi-Gugeno-Kang inference system (TSK+) with fuzzy interpolation and its rule base generation, Soft Computing, 22(10), 3155-3170.

L. Yang, F. Chao, and Q. Shen (2017), Generalised adaptive fuzzy rule interpolation, IEEE Transaction on Fuzzy Systems, 25(4), 839-853.

Y. Liu, L. Yang, I. Han, J. Lu, P Yuen, Y. Zhao, and R. Song (2017), Engaging students for learning and assessment of the advanced computer graphics module using the latest technologies, Proceedings of International Conference on Education and New Developments, pp. 238-242.

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