Improving the Online Teaching of STEM Educators in Higher Education through Knowledge Mining from Pedagogy Literature

Eligibility: UK/International (including EU) graduates with the required entry requirements

Duration: Full-Time – between three and three and a half years fixed term

Application deadline: This opportunity will only remain open until a suitable candidate is identified and appointed - early application is therefore highly recommended

Interview date: Will be confirmed to shortlisted candidates

Start date: September 2021 or as soon as possibe thereafter

Please apply at the earliest opportunity as this project will be recruited to as soon as a suitable candidate is identified.

For further details contact: Dr Xiaorui Jiang 

Introduction

Coventry University is inviting applications from suitably-qualified graduates for a self-funded full-time PhD studentship.

The successful candidate will join the project “Improving the Online Teaching of STEM Educators in Higher Education through Knowledge Mining from Pedagogy Literature” led by Assistant Professor (Senior Lecturer) Dr Xiaorui Jiang (Natural Language Processing, Scientometrics), Dr Matthew England (Machine Learning, CS Education), and Dr Farzana Aslam (STEM Teaching, Learning and Outreach) at Coventry University.

The successful candidate will have the opportunity to work across the Research Centre for Data Science (CDS) and Research Centre for Global Learning (GLEA), under the supervision of an interdisciplinary team specialized in Natural Language Processing, Machine Learning, Deep Learning, Scientific Text Mining, STEM (Science, Technology, Engineering and Mathematics) Education and AI for Education in the UK.

About the project

By breaking geographical barriers, online education is integral to the UN’s Sustainable Development Goal for Education and its targets of accessibility and equality. However, moving education online requires innovation in teaching and assessment methods. 

The global pandemic has intensified the movement towards online education, with many changes likely to last beyond the pandemic. But, emerging evidence suggests this move has exposed and deepened inequalities.  It has also accelerated the development of new concepts, theories and practices as more educators document the effects of their pedagogical changes. All frontline educators should engage with this pedagogical development, but many lack the time and expertise to identify, and analyse new pedagogical findings relevant to their practice. 

The project will address this using AI technologies, specifically Deep Text Mining (a combination of machine learning and natural language processing).  We will build tools that move beyond the existing technology of literature recommendation systems; instead extracting conclusive, evidenced, and actionable knowledge claims. 

We will focus on STEM subjects in Higher Education where there are some common challenges (e.g. reliance on labs and closed book examinations).   Our aim is to help frontline educators to efficiently choose the most promising teaching methods for the post-COVID world.

Funding 

This project is an opportunity for those who have secured their own funding.  Students with scholarships or funding from their home country are particularly welcomed to apply.

Postgraduate Loans (Masters by Research and Doctoral)

You may be able to get a UK loan from Student Finance England or Student Finance Wales for a Postgraduate degree.

Benefits

The successful candidate will receive comprehensive research training including technical, personal and professional skills. All researchers at Coventry University (from PhD to Professor) are part of the Doctoral College and Centre for Research Capability and Development, which provides support with high-quality training and career development activities. 

Staff from CDS largely run the MSc Data Science and Computational Intelligence programme. Should the PhD candidate require training on artificial intelligence, natural language processing, machine learning and dee learning then modules from this programme may be undertaken in the first year. 

In addition, the centre runs activities year round for the development of their PhD students including: the CDS 5-minute thesis competition, thesis writing bootcamps, and round table discussions on the following topics:

  • Giving seminars.
  • Writing papers.
  • Peer reviewing an article for a journal.
  • What is a research question anyway?
  • How should I read an article – there are so many to read!
  • The importance of a good abstract.

There is of course also the main research seminar program of the centre.

This project is co-supervised with the Research Centre for Global Learning (GLEA) which has a multidisciplinary team of researchers and faculty associates. GLEA also delivers a structured PhD programme in Global Education

The PhD candidate can access modules from this programme as required including on research methodology and research design for pedagogy.

Year-round activities in GLEA for research students include:

  • Research workshops aimed at supporting PGR writing.
  • Seminars around methodology and developing research questions.
  • Support sessions for discussing challenges.

Entry requirements

  • A minimum of a 2:1 first degree in a relevant discipline/subject area with a minimum 60% mark in the project element or equivalent with a minimum 60% overall module average. 
    PLUS 
    the potential to engage in innovative research and to complete the PhD within a 3.5 years
  • a minimum of English language proficiency (IELTS overall minimum score of 7.0 with a minimum of 6.5 in each component)

In addition

  • A Masters degree in a relevant subject area will be considered as equivalent and welcomed. The Masters must have been attained with overall marks at 60%. In addition, the dissertation or equivalent element in the Masters must also have been attained with a mark at least 60%. (Note that marks are in UK standard; Marks from overseas institutions will be converted into British scoring bands).
  • Strong, demonstrable programming skills and good mathematical grounding are essential.
  • Experience with machine learning techniques in the context of image processing or other applications is desirable.

How to apply

All applications require full supporting documentation, and a covering letter, plus

  • an up-to 2000 word supporting statement is required showing how the applicant’s expertise and interests are relevant to the project
  • a copy of the degree or Masters thesis in electronic format
  • copies of relevant publications if any, or a copy of coursework report in a related area in machine learning, deep learning, natural language processing, and artificial intelligence etc., in electronic format
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