COMP2121 Data Mining

School of Computing: Assessment brief
Module title Data Mining
Module code COMP2121
Assignment title Assessment for COMP2121 Data Mining, Semester 2 2024/25
Assignment type and 
description
Assessment 1: Test submitted as Minerva MCQ Test, 30% weight,
1 hour to complete, week18 (week 5 of semester 2) 
release 14:00 Wednesday 26.2.25, deadline 14:00 Thursday 27.2.25
Use of Gen AI (Generative Artificial Intelligence) instructions:
There is a three-tier traffic light categorisation for using Gen AI in 
assessments. This assessment is red category. AI tools cannot be 
used.
 
Assessment 2: Report, PDF submitted via Minerva,
70% weight, approx 10 hours per student to complete,
release week 14 (aka week 1 semester 2) Wednesday 29.1.25
deadline week 23 (aka week 10 semester 2) 14:00 Thursday 1.5.25
Use of Gen AI (Generative Artificial Intelligence) instructions:
There is a three-tier traffic light categorisation for using Gen AI in 
assessments. This assessment is green category. AI has an integral 
role and should be used as part of this assessment. Under this 
category, you can use Gen AI as a primary tool throughout the 
assessment process.
Rationale Summative assessment of student knowledge and understanding of the 
module syllabus.
Word limit and 
guidance
Test: 30 questions. Report: up to 12 pages
Weighting Test: 30% Report: 70%
Submission deadline Test: 14:00 Thursday 27.2.25
Report: 14:00 Thursday 1.5.25
Submission method Test submitted as Minerva Test
Report submitted as Minerva Assignment
Feedback provision Test代写COMP2121 title Data Mining answers will be discussed in a subsequent lecture 
Report feedback via Minerva
Learning outcomes 
assessed
These exercises will enable you to: learn theory, methods and 
terminology used in data mining and text analytics; investigate how to 
apply AI methods, resources and techniques for implementing and 
evaluating data mining and text analytics in a practical applied research 
project; summarize and present your knowledge and ideas to a peer
audience, in a research proposal report.
Module lead Prof Eric Atwell
Other Staff contact Dr Noorhan Abbas

1. Assignment guidance
   You are advised to attempt both assessments: Test, Report. There is no May/June exam.
2. Assessment tasks
   For Test, each student will take an individual online test in Minerva. The tests will include Multiple 
   Answer Questions: each question has several suggested answers, and for each possible answer 
   you must select if it is correct or not. If you require special arrangements and/or extra time due to a 
   disability or other special circumstances, please make sure you notify lecturer Eric Atwell AND 
   student support well in advance so they can prepare accordingly.
   For the Report, you will develop a 6-month research project proposal, using data mining and text 
   analytics theory, methods and technologies for a practical application of your choice. Lectures and 
   online learning resources will include examples of data mining and text analytics methods, 
   techniques, resources, and applications. You can also include other tools and techniques in your 
   research proposal, as appropriate.
   When writing an applied data mining and text analytics research proposal, you can learn from 
   advice provided by the Engineering and Physical Sciences Research Council. EPSRC is a major UK 
   supporter and funder for research projects in Engineering and Physical Sciences, including AI. The 
   EPSRC website gives guidance on writing research project proposals, see
   https://www.ukri.org/councils/epsrc/guidance-for-applicants/w... support/
   A widely cited guide to planning computer science student projects is:
   Dawson, C.W., 2005. Projects in computing and information systems: a student's guide (third 
   edition). Pearson Education. https://scholar.google.com/scholar?cites=15713354297912485216
   A standard research project proposal includes several forms and tables, but the core document is 
   “Proposed research and its context” to include:
   Research hypothesis & objectives,
   Background,
   Importance and contribution to knowledge, 
   Pilot study,
   Programme and methodology,
   And Workplan diagram, eg Gantt Chart 
   You should use these headings to structure your report, and write a section for each of these: 2
   pages each for Pilot study and Programme and methodology, 1 page each for other sections, plus 1 
   page for References; maximum 9 pages in total. 
   In addition, as an Appendix of up to 3 pages, describe your use of data mining and text analytics 
   tools in developing your Report. This should include: 
    (i) tools used in the small pilot study to trial the methods proposed; 
    (ii) use of tools (eg Google Scholar, ChatGPT) in searching for background information; 
    (iii) use of tools (eg Word, ChatGPT) to draft the report and improve grammar and style. 
   You must include appropriate examples of prompts/queries and results for each of these three 
   tasks: the pilot study, background research, and drafting the report.
3. General guidance and study support
   See Minerva Learning Resources for the module for guidance and study support on the sections of
   the Proposed Research and Context:
   Research hypothesis and objectives: Set out your research idea or hypothesis: the task or 
   challenge you plan to address and solve. Explain how the proposed project is challenging and 
   novel, emphasising the scientific ambition. Identify the overall aims of the project and the 
   measurable objectives against which the outcomes and impacts of the work will be assessed. 
   Background: Introduce the background to the proposal and explain its context. Explain how this 
   work relates to and builds on past and current research at Leeds University, in the UK and abroad. 
   Include appropriate citation of journal and/or conference papers to support your explanation.
   Importance and contribution to knowledge: Explain how the project may contribute to current or 
   future economic success; to future development of key emerging industries; and/or addresses key 
   societal challenges. Describe how your project would benefit national and international research, 
   and engage with research in other disciplines to broaden the reach of the new knowledge. 
   Pilot study: You must implement a small-scale pilot study to demonstrate feasibility of your 
   proposed solution. You must select a simple case study challenge or task, select and acquire an 
   appropriate sample of data, develop a prototype solution, and evaluate your prototype solution.
   Programme and methodology: Describe the work programme including methods, tools, 
   experiments, and user evaluation. Identify the contribution of users and/or stakeholders. State
   milestones and deliverables that you will use to monitor progress, and explain how the project will 
   be managed. The research work programme should make use of CRISP-DM or another appropriate 
   methodology for AI projects; and should include use of at least two data mining and/or text analytics 
   methods, tools or techniques introduced in the module (eg SketchEngine, Weka, ChatGPT, LLMs)
   1-page workplan diagram must match the written description of the research work programme, 
   showing start, end and duration of each phase or work-package, and timing of deliverables.
4. Assessment criteria and marking process
   The mark scheme for your report will reflect the EPSRC grading scheme with marks in the range 0-6 
   for each criterion, EXCEPT the sections on Pilot study, Programme and methodology, and 
   Appendix: these are most important, and get higher weight:
   Background (0-6 marks)
   Importance and contribution to knowledge (0-6 marks)
   Research hypothesis and objectives (0-6 marks)
   Pilot study (0-12 marks)
   Programme and methodology, with corresponding workplan diagram (0-12 marks)
   Appendix: use of data mining and text analytics tools in developing your Report (0-18 marks)
   TOTAL: up to 60 marks
   Return of marks and feedback will be via Minerva grade center, approximately 3 weeks after 
   submission.
5. Presentation and referencing
   Page limits are strict: up to 12 pages in total. Text must be single-spaced spaced Arial 11 with 2cm 
   margins; you cannot include more text by using smaller fonts or narrower margins. You must 
   present your ideas for all specified sections, and references must be included within the page limits.
   You must submit via Minerva one copy of the report as a PDF document. 
   References and citations must be in a consistent format. I recommend Kilgarriff referencing style 
   for Artificial Intelligence papers, see https://blog.kilgarriff.co.uk/?p=71 but you are free to use 
   another format, as long as you apply it consistently to all your references. 
   The quality of written English will be assessed in this work. As a minimum, you must ensure:
   • Paragraphs are used
   • There are links between and within paragraphs although these may be ineffective at times
   • There are (at least) attempts at referencing
   • Word choice and grammar do not seriously undermine the meaning and comprehensibility of 
   the argument
   • Word choice and grammar are generally appropriate to an academic text
   These are pass/ fail criteria. So irrespective of marks awarded elsewhere, if you do not meet these
   criteria you will fail overall.
6. Submission requirements
   You must submit one copy of the report as a PDF document by the due date and time using the 
   "Submit My Work" link on Minerva. The filename should be your name e.g. EricAtwell.pdf
7. Academic misconduct and plagiarism
   Academic integrity means engaging in good academic practice. This involves essential academic 
   skills, such as keeping track of where you find ideas and information and referencing these 
   accurately in your work.
   By submitting this assignment you are confirming that the work is a true expression of your own 
   work and ideas and that you have given credit to others where their work has contributed to yours.
8. Assessment/ marking criteria grid
   The mark scheme for your report will reflect the EPSRC grading scheme with marks in the range 0-6 
   for each criterion, EXCEPT the sections on Pilot study, Programme and methodology, and 
   Appendix: these are most important, and get higher weight:
   Background (0-6 marks)
   Importance and contribution to knowledge (0-6 marks)
   Research hypothesis and objectives (0-6 marks)
   Pilot study (0-12 marks)
   Programme and methodology, with corresponding workplan diagram (0-12 marks)
   Appendix: use of data mining and text analytics tools in developing your Report (0-18 marks)
   TOTAL: up to 60 marks
   WX：codinghelp