MATH5871/72 Dissertation Supervision and Assessment

Luisa Cutillo

Supervision Info shared with the students

• - Dissertation work starts in early June, after your last exam.
• - Contact your supervisor before June to arrange an informal meeting.
• - You and your supervisor(s) should arrange up to 6 hours of meetings (modalities to be agreed) - do this as soon as possible.
• - Consecutive meetings should not be more than 2-3 weeks apart.
• - Your supervisor will monitor your progress.
• - Students are expected to be on campus during their dissertation work.

Good Practice

• - Share a short report with your supervisor(s) ~2 days ahead of every meeting.
• - Practice on using latex with the provided template on Minerva. A very good online tool is overleaf.
• - Create a project on GitHub to share with your supervisor(s). (ABC for a Data Scientist!).
• - Learn how to use GitHub. Very good tutorials here: Atalassian and Software Carpentries.

Timeline & details

MATH5871 ASSESSMENT

MATH5872 ASSESSMENT

General Guidance

• Internal assessors list will be shared shortly

• Assessors’ deadline to return overall marks: Monday, 6 October 2025.

• Read the dissertation ahead of time and prepare questions on the main parts you want to ask about to check on understanding and integrity

• Arrange interview schedule accommodating delays/unforeseen circumstances

• Schedule the interviews with your students and internal assessors as soon as possible

• Assessment forms will be shared with you shorthly

Assessment Guidelines

• Understanding (30%) Based on the report, and on presentation and answers to questions in the oral examination, does the student understand the methods described and the work done? Are suitable analyses carried out and/or examples used to illustrate the theory? Are sound conclusions drawn from any analyses, simulations or examples?

• Achievement (20%) Is the work done of the quantity and level that could reasonably be expected of a competent student in the time available? Is there a derivation of an original result, substantial analysis of a dataset or great effort spent programming?

• Initiative (20%) Did the student exercise initiative; for example, by influencing the direction of the project or by locating their own sources of data and/or information?

• Report (20%) Is the report laid out well, with good structure and use of figures, tables etc? Is there clarity in the exposition? Are results (theoretical, or of data analysis and simulations) precise and unambiguous? Are there few typographical errors and are any mathematical expressions clearly formatted?

• Presentation Skills (10%) The quality of the student’s presentation will be assessed not only in terms of the initialoverview but also through their ability to communicate effectively during the question and answer session. Well-structured answers to questions and validity of their answer are taken into consideration in the Understanding mark.

Thanks!