Qualifications
- BSc (University of Hull)
- PhD / DPhil (University of Hull)
Summary
Neil Gordon is a reader in Computer Science. He has research interests at the interface of mathematics with computer science, particularly in the areas of finite geometry and its applications and in formal approaches.
He is also an advocate for the effective development and use of technology for teaching, especially in higher education, and has worked on a number of projects with the AdvanceHE.
After a joint degree in Mathematics and Computer Science, he went on to complete a PhD in Applied Mathematics (Finite Geometry and Computer Algebra, with Applications).
This was followed by work as a Research Assistant, initially on geometry and group theory, and later on solving differential equations and their applications in mathematical physics.
He worked for some time as an Educational Technology Advisor, exploring and supporting the use of computer technology in teaching mathematics.
In 2000, he began working as a lecturer in Computer Science.
Neil is a principal fellow of the Higher Education Academy. His teaching interests include the teaching of Computer Science, flexible learning, Technology Enhanced Learning and Gamification applied to learning.
He teaches on modules across the range of levels, from level 4 (first year) through to taught Masters. Modules include:
Level 4 "Computational Thinking", and also "Professional Develpoment";
Level 6 "Commercial Game Development", "Communicating and Teaching Computing" and "Advanced Software Engineering",
and "Level 7 Component Based Architectures".
Neil also supervises undergraduate and postgraduate project students.
Journal Article
Fairness, Bias, and Ethics in AI: Exploring the Factors Affecting Student Performance
Omughelli, D., Gordon, N., & Al Jaber, T. (2024). Fairness, Bias, and Ethics in AI: Exploring the Factors Affecting Student Performance. Journal of Intelligent Communication, 4(1), 100-110. https://doi.org/10.54963/jic.v4i1.306
Presentation / Conference Contribution
Improving Rice Yield Prediction Accuracy Using Regression Models with Climate Data
Mohamad Mohsin, M. F., Umana, M. K., Hassan, M. G., Sharif, K. I. M., Ismail, M. A., Salleh, K., Zahari, S. M., Sarmani, M. A., & Gordon, N. Improving Rice Yield Prediction Accuracy Using Regression Models with Climate Data. Presented at International Conference on Computing and Informatics 2023, Kuala Lumpur, Malaysia
Research interests
Finite geometry and applications, including error correcting codes
HCI, gamification and computer-based instruction
Computer algebra
Safety and reliability analysis
Telehealth
Technology enhanced learning and information systems
Co-investigator
Project
Funder
Grant
Started
Status
Project
Use of Foundational AI in teaching and assessment – A practical case study in software engineering education
Funder
Council of Professors and Heads of Computing (CPHC)
Grant
£4,980.00
Started
1 February 2024
Status
Ongoing
Postgraduate supervision
Finite Geometry and Computer Algebra
Mathematical Modelling
Computer Gamification and Instruction
Human Computer Interaction
Computing Education