Dissertation Meeting

26/01/2026

Luisa Cutillo, School of Mathematics, University of Leeds

• 13:00 – Intro and Welcome

• 13:05 – EAP Lecture  - Deak Kirkham

• 13:15 – Assessment Components & Supervision

• 13:25 – Academic Integrity & Good Practice

• 13:35 – Projects Selection

• 13:45 – Q&A 

• 14:00 – Close

• MATH5871 (MSc Statistics/ Statistics with applications to Finance)
• MATH5872 (MSc Data Science and Analytics)

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Project Portfolio

Viva (Oral Examination)

ManuscriptAssessment

Process-Based Assessment

 Evaluating students' engagement, understanding, and progress throughout the duration of their project, rather than focusing solely on the final product.

Assessment Components

Project Portfolio

3 Milestones

Viva (Oral Examination)

30 Minutes

Manuscript

 Assessment

Supervision and milestones

Project Portfolio

The portfolio will include regular updates, reflections, and evidence of engagement and progress.

Phase 1: Preparation & Skills Building

(Late February – March, expert led)

Phase 2: Dissertation Work (April/May–August, 6  meetings)

Lecture 1  (late February): critically read and summarise academic work

Supervision meeting 0: once dissertations are assigned, informal meeting with supervisors

Lecture 2 (last teaching week before Easter): how to prepare a background study and identify reliable sources (post meting 0)

Project Portfolio

The portfolio will include regular updates, reflections, and evidence of engagement and progress.

Phase 1: Preparation & Skills Building

(Late February – March, expert led)

Lecture 1: critically read and summarise academic work

Lecture 2: how to prepare a background study and identify reliable sources (post meting 0)

Supervision and milestones

Project Portfolio

The portfolio will include regular updates, reflections, and evidence of engagement and progress.

Phase 1: Preparation & Skills Building

(Late February – March, expert led)

Phase 2: Dissertation Work (April/May–August, 6  meetings)

Last Year 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 initial overview 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.

Example of Supervision Structure

Academic Integrity and Use of Generative AI

Good Practice

Projects Selection

Last Year 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 initial overview 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.

Engagement

Thanks!

Time for questions and suggestions!

Let's have a Discussion on

(~ 20 min)

What is the value of the dissertations in the genAI era?

What is feasible in terms of workload for staff and students?

Which strategies are implemented in other MSc programs in Leeds and beyond?