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  Exploit Learning Related Emotion to Improve students Engagement and Motivation in Higher Education

   School of Computing

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  Dr Alaa Mohasseb  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

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 Alaa Mohasseb.

The work on this project could involve:

  • Explore and investigate different NLP and Machine learning methods to improve students’ engagement and motivation in higher education
  • Exploit sentiment analysis to track students’ learning behaviours.
  • Data collection, pre-processing, and analysis.
  • Develop an approach that captures and identifies students’ behaviours towards learning.

Project description

Natural language processing (NLP) methods are used for giving insight into many problems in our life and with the increasing amount of data being generated every day, these methods became more important to make sense of the data. NLP techniques have been applied to many different real-world problems, including education, in which it has successfully been used in many educational settings. 

This PhD project aims to explore and investigate different NLP and machine learning based methods and their application to teaching and learning in higher education to improve and increase students’ engagement and motivation. Different sentiment analysis models will be explored to track emotions in students’ learning. In addition, the research will focus on exploring and designing an approach to develop a way to capture and track students’ learning-related emotions and behaviours using NLP methods and machine learning techniques.

This approach will help learning institutions in higher education to better understand students’ learning patterns and categorise their learning behaviours to help increase their engagement and motivation. This in turn will help improve learning outcomes for universities students and even influence how to assess and support students.

The successful candidate will be supervised by Dr Alaa Mohasseb who has an extensive research experience in the field of Text Mining, Natural Language Processing and Machine learning and have been involved in a number of research and projects.

General admissions criteria

You'll need a good first degree from an internationally recognised university or a Master’s 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.

Specific candidate requirements

You should have an experience of the fundamentals of Natural Language Processing, Data Analytics and Machine Learning techniques, preferably good technical skills in text and speech processing. Competent in applying NLP toolkits, such as NLTK or Spacy, or ML toolkits such as Scikit-Learn or Tensorflow. 

Good programming skills in Python and analytical skills, knowledge of foundations of computer science are also required. You should be able to think independently, including the formulation of research problems and have strong oral and written communication skills and good time management.

How to Apply

We encourage you to contact Dr Alaa Mohasseb ([Email Address Removed]) 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 ‘How to Apply’ page offers further guidance on the PhD application process. 

When applying please quote project code:COMP7560423

Computer Science (8)

Funding Notes

Self-funded PhD students only.
PhD full-time and part-time courses are eligible for the UK Government Doctoral Loan (UK students only).
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