Detection and utilisation of learning-related emotion using machine learning within an education setting
PhDs and postgraduate research
Funded PhD Project (UK and EU students only)
School of Computing
4 May 2021 (12pm GMT)
Candidates applying for this project may be eligible to compete for one of a small number of bursaries available; these cover tuition fees at the UK rate for three years and a stipend in line with the UKRI rate (£15,609 for 2021/22). Bursary recipients will also receive a £1,500 p.a. for project costs/consumables.
The work on this project will:
- Explore and investigate different NLP and Machine learning methods to Improve teaching and learning
- Exploit sentiment analysis to track emotions in students’ learning
- Develop an approach that captures and identifies students’ emotions and behaviours
This PhD project aims to explore and investigate different NLP and machine learning based methods and their application to teaching and learning. Different sentiment analysis models will be explored to track emotions in students’ learning. In addition, the research will focus on exploring and designing a novel approach to develop a way to capture and track students’ learning-related emotions and behaviours using NLP methods and machine learning techniques.
The project will be in collaboration with Bohunt Education Trust; a pioneering multi-academy trust, responsible for an expanding collection of schools in England. A number of their schools (located in Horsham, Worthing, Wokingham and Petersfield) have 1-2-1 schemes with iPads.
This approach will help learning institutions, especially teachers, to better understand students’ learning patterns and categorise their learning behaviours. This in turn will help improve learning outcomes for young people and even influence how we assess and support students. The outcome of this 3-year project will be a platform that integrates Natural Language Processing methods, sentiment analysis and machine learning techniques.
The supervisory team consists of Dr Alaa Mohasseb who has research experience in the field of Text Mining, Natural Language Processing and Machine learning and Dr Ella Haig who has over 15 years of research experience, including in the areas of modelling user behaviour/characteristics (including emotions), text mining and machine learning.
The successful candidate will have the chance to work on a cutting-edge research project and work with staff and students from the trust and visit/work with different schools, which will be excellent opportunities for skills and career development.
You'll need a good first degree from an internationally recognised university (minimum upper second class or equivalent, depending on your chosen course) 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.
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’d encourage you to contact Dr Alaa Mohasseb (firstname.lastname@example.org) to discuss your interest before you apply, quoting the project code.
When you are ready to apply, you can use our online application form. 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.If you want to be considered for this funded PhD opportunity you must quote project code COMP5820521 when applying.