Evidence, Evaluation & Policy, part of the MSc Public Policy Programme, explores how research evidence is created, synthesised, and used within policymaking. Students develop practical skills in evidence appraisal, policy design, and evaluation, working through an applied portfolio that mirrors the real-world policy cycle, from defining a problem to designing and assessing solutions. The course combines critical theory with hands on policy analysis and reflection on ethical and digital dimensions of evidence use, including the critical engagement with generative AI tools.

The course will be delivered via:

■ 60 minute lecture

■ 90 minute 'policy labs' 

in semester 2, once per week, over 10 consecutive weeks including 1 reading week.

None

None

Assessment Type: Integrated Policy Portfolio (100%)
Length: Equivalent to 4,000-5,000 words
Submission: Multiple electronic submissions with deadlines via Moodle

Marking: One marking session once all components are submitted
Feedback: Formative feedback on one or two early components (i.e. policy briefing or evidence appraisal) mid-semester, and summative feedback on the final submission following the reflective discussions.

The Policy Portfolio replaces the previous structure of two essays and a multiple-choice exam. It assesses students' ability to apply evidence evaluation and policy analysis skills in a realistic, practice-based context scaffolded alongside the workshops. The portfolio mirrors the policy cycle, allowing students to demonstrate synthesis, design, evaluation, and reflection.

Purpose:
This authentic, process-oriented assessment allows students to demonstrate applied understanding of the evidence-policy-evaluation nexus. It develops transferable skills in policy communication, evidence synthesis, and ethical reflection while aligning with University guidance on assessment in the generative AI era.

Support for students:

There will be an example assessment with blank templates to help students understand the format of each component of the portfolio. There will also be an opportunity for students to submit one or two components (e.g., briefing or evidence synthesis) in an alternative format such as a policy podcast, infographic, poster, dashboard or other to diversify communication skills. All documents and literature will be available on Moodle in advance of lectures and workshops. Lectures will be recorded via Echo360.

Evidence, Evaluation & Policy examines how research evidence is produced, synthesised, and used in the formulation and assessment of public policy. The course moves beyond theory to simulate how policy professionals work with evidence in practice, from defining policy problems and appraising data to designing interventions and planning evaluations. Students engage with contemporary debates about the politics of evidence, methodological rigour, and the ethics of policymaking in complex social contexts. Through an applied policy portfolio, students develop advanced skills in evidence synthesis, policy design, and evaluation planning, while critically reflecting on how knowledge, power, and technology intersect in shaping decisions. The course also encourages the responsible and transparent use of generative AI tools as part of developing professional competence in evidence-informed policymaking.

By the end of this course, students will be able to:

1. Synthesise and critically appraise diverse forms of social research evidence to inform real world policy challenges.

2. Design evidence informed policy interventions that demonstrate a clear theory of change and awareness of contextual, ethical, and political constraints.

3. Develop and justify appropriate evaluation frameworks, including indicators, data sources, and methodological approaches for assessing policy effectiveness.

4. Communicate policy analysis and recommendations clearly and persuasively to varied audiences, using professional and accessible formats.

5. Reflect critically on the ethical, methodological, and epistemic dimensions of evidence use in policymaking, including the transparent and responsible engagement with generative AI tools.