Funding

Self-funded

Project code

COMP7610423

Department

School of Computing

Start dates

October, February and April

Application deadline

Applications accepted all year round

Applications are invited for a self-funded, 3-year full-time or 6-year part time PhD project.

The PhD will be based in the School of Computing and will be supervised by Dr Farzad Arabikhan.

The work on this project could involve:

  • Extensive literature review, collecting data and developing a framework to identify the main factors in student鈥檚 drop out 
  • Develop Machine Learning/Data Mining models for the early prediction of students with low, medium and high withdrawal risk
  • Understand the differences between BME and White student performances and drop out

Student progression and non-continuation have been major challenges for universities. Disengagement mostly happens in year one and some departments such as Computing are suffering the most.

The identification and detection of at-risk students is tackled in some large measure through the personal tutoring, meetings with students and also by looking at students鈥 performance data - which is usually received too late. As data clearly shows, predicting potential dropouts given the highly contingent and subjective nature of personal tutoring has not been successful. Analysis of students鈥 performance in lower levels, background information, qualification on entry, attendance and many more factors could impact the student鈥檚 retention at the university level. Also, statistics show that more withdrawals occur around December and January time which highlights the necessity of using a centralised data-oriented system which can flag up potential at risk students at very early stages.

The project aim is to develop a framework to identify the required data and information and use Machine-Learning (ML)/Data-Mining techniques for the early prediction of students with low, medium and high withdrawal risks. The predictive ML model will be developed using primary and historical data available at the 小蓝视频. The model output will help to identify potential 鈥渁t risk鈥 students from day 1 which will help academics to provide appropriate interventions from a very early stage. 

This project will provide an opportunity to use novel techniques in machine learning to develop appropriate predictive model and the research output can be disseminated in journal and conference papers. The 小蓝视频 will also benefit from it to improve the retention rate. 

The supervision team has experience in supervising PhD students in Machine Learning/Soft Computing with several publications and knowledge expertise in the research field. They have also been involved in a number of research and KTP projects.

Entry requirements

You'll need a good first degree from an internationally recognised university or a Master鈥檚 degree in an appropriate subject. In exceptional cases, we may consider equivalent professional experience and/or qualifications. English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.

Data science and programming knowledge is required.

How to apply

We encourage you to contact Dr Farzad Arabikhan (Farzad.arabikhan@port.ac.uk) to discuss your interest before you apply, quoting the project code below.

When you are ready to apply, please follow the 'Apply now' link on the Computing PhD subject area page and select the link for the relevant intake. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV. Our 鈥How to Apply鈥 page offers further guidance on the PhD application process. 

When applying please quote project code:COMP7610423