Machine Learning Assisted Feedback for Engineering Drawing Assessments

Assessment of engineering drawings can be a complex and lengthy task which causes delay in getting feedback to students. The provision of constructive, relevant and timely feedback is essential to student learning. The emergence of digital tools such as large language models and deep learning models are enabling feedback to be generated automatically to aid in the assessment of students' work. However, how these digital tools can be utilised for the assessment of engineering drawings is yet to be explored.

This project will explore the opportunities and challenges associated with the use of machine learning for providing feedback on engineering drawing assignments. Elements of the research will involve the development of a marking assistance tool to support the marking team by identifying common issues in the submissions, providing quality feedback and suggesting a bracket of scores. It is expected that this will involve the use of machine learning tools but it is ultimately up to the PhD researcher to decide on the best digital tools to address the challenge. The initial pilot will be for the Year 2 Mechanical Design A module with the potential of being rolled out to other modules within the Department.

Candidate Requirements:

Essential:

- Have, or expected to attain an undergraduate degree (2:1 or higher) and preferably a Master's degree in engineering, computer science, statistics , education or a related field.

-Familiarity with at least one programming language.

- Strong written and verbal communication skills.

Desirable:

- Equivalent experience using artificial intelligence (e.g. machine learning, computer vision, natural language processing, etc.).

- Experience providing feedback and assessment in education.

Application Details:

Eligible applicants should first make an informal enquiry to Dr. Khadijat Olorunlambe ([Email Address Removed]) with the subject line 'Prospective PhD Student', including the following:

- A brief statement explaining your motivation for pursuing a PhD and why you would like to undertake this research (1-page A4 maximum).

- CV containing academic record and relevant experience.

- Contact details for two referees