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Research Methods in Computer Science - COM00180M

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  • Department: Computer Science
  • Module co-ordinator: Prof. William Smith
  • Credit value: 20 credits
  • Credit level: M
  • Academic year of delivery: 2024-25
    • See module specification for other years: 2023-24

Module summary

A core module that equips postgraduate taught students with core theorectical and practical research skills.

Module will run

Occurrence Teaching period
A Semester 1 2024-25

Module aims

Research is about both generating new knowledge and evaluating confidence in knowledge. There are three skills associated with the conduct of good research:

  1. The ability to accurately identify from existing literature and systems a meaningful or important gap in knowledge, and therefore what constitutes new knowledge in the domain of Computer Science

  2. The ability to competently intervene in the world (e.g. through developing systems, implementing data collection procedures, conducting experiments…) in order to generate knowledge that causes positive change.

  3. The ability to evaluate the quality of evidence stemming from an intervention using sound analysis, and communicate that analysis to the scientific community.

As the ways of intervening in the world depend strongly on your disciplinary area, the aim of this module is to provide an introduction to the first and third pillars, namely the conduct of literature reviews to identify a research gap; and the methods for analysing research data, interpreting said analysis, and accurately presenting research outcomes.

The module will also cover general principles related to the second pillar that apply across disciplinary areas in the development of novel methods and conduct of experiments.

Module learning outcomes

By the end of this module, students will be able to…

  1. Utilise principles of responsible research and innovation to articulate and justify a meaningful gap in the evidence base.

  2. Accurately present a gap in the evidence base within the structure of a formal academic literature review.

  3. Critically evaluate the validity of methods used in extant or novel research.

  4. Analyse a variety of quantitative research data using an array of appropriate inferential statistical tests.

  5. Accurately interpret quantitative data with reference to a stated research goal.

  6. Present the outcomes of a research project.

Indicative assessment

Task Length % of module mark
Individual Open Assessment
N/A 50
Group Open Assessment
N/A 50

Special assessment rules


Indicative reassessment

Task Length % of module mark
Individual Open Asessment - Reassessment
N/A 50
Reflection on Group Assessment - Reassessment
N/A 50

Module feedback

Feedback is provided throughout the sessions, and after the assessment as per normal University guidelines.

Indicative reading

  • Howell DC. Fundamental Statistics for the Behavioral Sciences . 9th edition, student edition. Cengage Learning; 2017.
  • Goldbort R. Writing for Science . Yale University Press; 2006.

The information on this page is indicative of the module that is currently on offer. The University constantly explores ways to enhance and improve its degree programmes and therefore reserves the right to make variations to the content and method of delivery of modules, and to discontinue modules, if such action is reasonably considered to be necessary. In some instances it may be appropriate for the University to notify and consult with affected students about module changes in accordance with the University's policy on the Approval of Modifications to Existing Taught Programmes of Study.