Studying at York
Methods & Data - COM00161M
Module summary
The Methods and Data Module provides the interdisciplinary skills students need to be successful and responsible researchers working with games.
Module will run
| Occurrence | Teaching period |
|---|---|
| A | Autumn Term 2021-22 to Spring Term 2021-22 |
Module aims
The Methods and Data Module provides the interdisciplinary skills students need to be successful and responsible researchers working with games.
Module learning outcomes
Subject Content
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Specify and justify a research question
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Collect qualitative and quantitative data, including game generated data, to address a research question
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Explore, analyse and visualise data appropriately
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Write up a study in the expected format for an academic publication
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Identify and consider ethical and societal ramifications of research, and apply responsible innovation frameworks to anticipate, reflect, engage with, and act on ramifications
Academic and Graduate Skills
- Conduct a literature search and critique papers in terms of validity and rigour
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Prepare and deliver a substantial oral presentation on their work aimed at game designers or other industry stakeholders
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Identify and plan for future research training needs
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Understand principles and good practices of open science and reproducible research and apply them to your respective research
Module content
This module has two components: Research Methods and Game Analytics and Responsible Innovation, each of which is assessed separately.
The Research Methods component trains students in qualitative and quantitative games research methods, including reproducibility, open science, and the use of York’s extensive facilities for eye-tracking, brain imaging, and physiological data capture. Joint critique of papers outside the students’ home disciplines develops a shared appreciation for differing epistemologies across disciplines. Students are assessed on a research paper reporting game data collected and analysed by them.
In the Game Analytics and Responsible Innovation component, students work with large games industry data sets, for example Dota 2, to learn principles of data wrangling, exploration, prediction, machine learning, and data visualisation in week 1. Using innovative methods like design fiction, students will then extrapolate potential ethical and societal ramifications of such games research work (e.g. security, privacy), and apply responsible innovation frameworks like AREA to unpack how they can anticipate, reflect, and engage with these ramifications in their work. Students are assessed on a data collection and responsible innovation plan for their PhD project.
Assessment
None
Special assessment rules
None
Additional assessment information
Reassessment for each part of the assessment failed.
Reassessment
None
Module feedback
Students should receive feedback within twenty working days. Working days exclude University closure days (‘customary leave’ days between Christmas and New Year and public holidays/statutory holidays.’)
Indicative reading
- Cairns, P. and Cox, A.(eds), Research Methods in HCI, CUP, 2008
- Cairns, P., Doing Better Statistics in Human Computer Interaction, CUP, 2019.
- Seif El-Nasr et al. (eds.): Game Analytics – Maximizing the Value of Player Data. Springer, 2013.
- Drachen, Mirza-Babaei, Nacke (eds), Games User Research, OUP, 2018