• 11:00 – Intro and Welcome – Paul Baxter

• 11:05 – MSc Projects in the School of Maths: Reflection and Next Steps - Luisa Cutillo

• 11:15 – MSc Projects and Current Steps in the School of Geography - Vikki Houlden

• 11:25 – Flexibility in Reflective Dissertations: Implications Regarding AI - Richard de Blacquiere-Clarkson

• 11:35 – Group Discussion: The future and value of dissertations, student and staff workload, and new assessment strategies (All)

• 11:55 – Summary and Next Actions - Paul Baxter

• 12:00 – Close

MSc Projects in the School of Maths: Reflection and Next Steps

Luisa Cutillo, School of Mathematics, University of Leeds

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

Last Year Assessment Supervision Structure

• Dissertation work starts in early June, after your last exam.
• Students contact your supervisor before June to arrange an informal meeting (meeting 0)
• Students and your supervisor(s) arrange up to 6 hours of meetings (modalities to be agreed) - do this as soon as possible.
• Consecutive meetings not more than 2-3 weeks apart.
• Supervisor monitors student's progress.

Last Year Assessment Components

Viva

(15 minutes, 2+13)

Manuscript

(50 pages)

Understandig

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.

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Discussed Components

Project Portfolio

Viva (Oral Examination)

Alternative Outputs

Manuscript

OR Exam-Style Assessment

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

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.

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?