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A user centred approach to online information systems support

School of Computing

About the Project

In situations where users are faced with online tasks that require to follow a linear process, such as visa applications or medical health checks, current online facilities offer sufficient support. However, more work can be done for users that fail to find suitable information they need or fully interact with such facilities.
Furthermore, in cases of non-linear processes, where outcomes are more subjective and often dependent on a user’s experience and capabilities in identifying when enough information has been obtained or a task has been resolved, there remains opportunities to support users from a systematic, educational and skills based perspective.
A PhD candidate will explore these opportunities to design and develop a user focused solution in a suitable context, to be determined with support from the supervisory team. For example, a student could use a wide range of methods and techniques to identify, design, develop and evaluate a solution to support users in their information interactions.

Academic qualifications
A first degree (at least a 2.1) ideally in Computer Science or Library and Information Systems with a good fundamental knowledge of systems design and user centred information systems.

English language requirement
IELTS score must be at least 6.5 (with not less than 6.0 in each of the four components). Other, equivalent qualifications will be accepted.

Essential attributes:
• Experience of fundamental Information Science.
• Competent in mixed methods data collection and analysis.
• Knowledge of user focused system design.
• Good written and oral communication skills
• Strong motivation, with evidence of independent research skills relevant to the project
• Good time management

Desirable attributes:
A strong interest in information seeking behaviours.
Experience of prototype design and development skills.
Knowledge of, or a willingness to gain a working knowledge of, Machine Learning.

Funding Notes

This is an unfunded position.


Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of machine Learning research, 3(Jan), 993-1022.
Brazier, D., & Harvey, M. (2018, March). A Comparative Study of Native and Non-Native Information Seeking Behaviours. In European Conference on Information Retrieval (pp. 237-248). Springer, Cham.

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