Formative assessment brief: portfolio plan and reproducible data science code

1 Assessment in brief

1.1 Module code and title

TRAN5340M - Transport Data Science

1.2 Assessment title

Formative Coursework: Portfolio Plan and Reproducible Code

1.3 Assessment type

Portfolio Plan and Reproducible Code Submission

1.4 Learning outcomes

  1. To develop a clear plan for applying data science techniques to a transport problem.
  2. To demonstrate the ability to work with datasets and produce reproducible code.
  3. To critically engage with academic literature and formulate research questions.

Note: this is formative and not formally assessed, but feedback will be provided.

1.5 Deadline

Non-assessed submission deadline: 28th February 2025, 13:59.

1.6 Feedback

Feedback will be provided within 15 working days of submission. Written feedback will be provided alongside guidance on how to proceed with the final coursework.

1.7 Contact

Professor Robin Lovelace
r dot lovelace [at] leeds.ac.uk

1.8 Assessment summary

This formative coursework is designed to help you plan and receive feedback on your final coursework. You will submit a .zip file containing a PDF document and reproducible code. The document should outline your topic, datasets, research questions, and analysis plan. Feedback will be provided to guide your final submission.

The purpose of this formative assessment is:

  • To allow you to ask questions to the course team (e.g., “Does this sound like a reasonable input dataset and topic?”).
  • To describe progress on reading input datasets and the analysis plan.
  • To receive feedback on your approach before the final submission.

2 Use of GenAI

Generative AI category: AMBER

Under this category, AI tools can be used in an assistive role.

In this assessment, AI tools can be utilised in an assistive role to:

  • act as a support tutor supporting your research on the topic. e.g. to ask clarifying questions or to suggest areas to investigate.
  • testing and debugging of any code you produce yourself as part of the assignment.
  • provide ideas or inspiration to help you overcome a creative block.
  • give feedback on content or provide proof reading of content that you have generated yourself.

In this assessment, AI tools cannot be utilised to:

  • produce the entirety of, or sections of, a piece of work that you submit for assessment beyond that which is outlined above.

The use of Generative AI must be acknowledged in an ‘Acknowledgements’ section of any piece of academic work where it has been used as a functional tool to assist in the process of creating academic work.

The minimum requirement to include in acknowledgement:

  • Name and version of the generative AI system used e.g. ChatGPT-4.0
  • Publisher (company that made the AI system) e.g. OpenAI
  • URL of the AI system
  • Brief description (single sentence) of context in which the tool was used.

For example: “I acknowledge the use of ChatGPT-3.5 (Open AI, https://chat.openai.com/) to summarise my initial notes and to proofread my final draft. Best practice is to include a link to the exact prompt used to generate the content, e.g. https://g.co/gemini/share/4933efa27596

The standard Academic Misconduct procedure applies for students believed to have ignored this categorisation.

For detailed guidance see https://generative-ai.leeds.ac.uk/ai-and

General guidance

Skills@library hosts useful guidance on academic skills including specific guidance on academic writing

3 Submission requirements

You will submit a .zip file containing:

  1. A concise PDF document (recommended length: 2 pages, absolute maximum: 5 pages) outlining:
    • A draft title of your topic.
    • The main dataset you will use and other potential datasets.
    • Ideas on a research question.
    • Questions you would like to ask about the topic (e.g., “Is this a suitable dataset?”).
    • At least 2 references to academic literature related to the topic.
    • Minimal code and/or a description of where you accessed the data and how you imported it.
    • Any preliminary analysis you have done.
    • A suggested structure for the document:
      • Topics considered.
      • Input datasets.
      • Analysis plan (e.g., a workflow diagram as shown here).
      • Motivation for choosing this topic.
      • Questions and options.
  2. Reproducible code in a .qmd file.

3.1 Rendering

  • If you cannot render to PDF directly, render to HTML and convert to PDF by printing to PDF from your browser.

3.2 Presentation

You must appropriately cite all supporting evidence using appropriate references and a consistent, professional style.

See the authoring tutorial with RStudio at quarto.org for guidance on how to add citations to your document in RStudio’s Visual Editor mode.

4 Assessment criteria

The assessment is formative so is not assessed, but you will be provided with feedback on the following criteria:

  • Clarity and feasibility of the proposed topic and research question.
  • Appropriateness of the selected datasets.
  • Engagement with academic literature.
  • Quality of the analysis plan and workflow diagram.
  • Quality of and reproducibility of the code and documentation.

See the marking criteria for more details.

5 Academic misconduct and plagiarism

The university expects that all the work you do, which includes all forms of assessments submitted and examinations taken, meet the university’s standard for Academic Integrity. All forms of Academic Integrity are investigated through the Academic Misconduct Procedure. This applies to all taught elements of your study, including undergraduate programmes, taught postgraduate study, and taught elements of research degrees. Breaching academic integrity standards can lead to serious penalties.

Guidance on Academic Integrity and Academic Misconduct can be found on the For Students website pages. Full definitions of offences under the Academic Misconduct Procedure can be found in the Academic Misconduct Procedure.

6 Academic integrity

All work must meet university standards for Academic Integrity. See the For Students website for guidance.

7 Support resources