- 1.0 Table of Contents
- 1.1 Learning Objectives
- 1.2 Summary
- 1.3 Academic Strengths and Developmental Areas
- 1.4 Areas of Interest
- 1.5 Fields of Study
- MLO1 - Identify interests, strengths, development areas and previous experience
- MLO1 - Critically reflect on interests, strengths, development areas and previous experience
- MLO1, MLO3 - Select a unit of analysis or object of research
Academic research projects require viability, critical assessment, and a degree of self-reflection. Topics should be interesting and creative, and clustering techniques can help make broad subjects viable by exploring subtopics to potentially focus on.
As Computer Science covers various fields and disciplines, it is able to take interdisciplinary approaches to addressing societal challenges. It is important to consider whether the skills held match the skills demanded by any chosen approach.
Research can be classified according to field, approach, and nature. However, good research skills, such as exhibiting open-mindedness and critical analysis, transcend these categories.
- Required: none
- Extension: none
Research projects are not solely concerned with the specific interests of the researcher: they need to be viable in terms of:
- Existing researcher knowledge
- Existing researcher skills
- Potential to raise these to the required level
- Time available
- Resources available
Realistic proposals stem from being critical but also embracing confidence in particular areas.
Self reflection on existing skills using limited, acceptable, competent, and excellent labels:
- Academic reading - competent
- Academic writing - competent
- Academic referencing - excellent
- Using reference tools - excellent
- Searching for literature - competent
- Making research notes - competent
- Planning and meeting deadlines - excellent
- Using feedback to improve - competent
- Communicating - excellent
Research methodologies need to fit both the problem being investigated and the researcher's skill set.
Quantitative methods skills recap:
- Hypothesis testing
- Survey design
- Data analysis
- Data visualisation
- Time series analysis
- Machine learning approaches
- Experimental design
Qualitative methods skills recap:
- Interviews
- Content analysis
- Ethnography
- Grounded theory
- Case study analysis
- Narrative analysis
- Participant observation
Self reflection on the above skills:
- “My strengths are mainly in quantitative methods” - Agree
- “My strengths lie mainly in qualitative methods” - Disagree
- “I am strong in both qualitative and quantitative research methods” - Somewhat agree
- “I need to develop my skills in both qualitative and quantitative research methods” - Somewhat disagree
- Required: Chapters 1, 3, Projects In Computing And Information Systems: A Student's Guide, Dawson
- Extension: none
Computer Science includes a wide variety of fields and disciplines:
- Academic fields relate to the area covered, such as cyber security
- Academic disciplines relate to the way of developing knowledge
Research in Computer Science draws upon a range of techniques from parent academic disciplines, such as mathematics, engineering, psychology, and philosophy.
Inspiration can be drawn from existing work completed by academic researchers at the University of York. This includes, but is not limited to:
- 3D image analysis - Towards a Complete 3d morphable model of the human head
- Human computer interaction - Video game loot boxes are linked to problem gambling
- Artificial intelligence - Automatically improving constraint models in Savile Row
- Robotic systems - Modelling and verification of the functional behaviour of robotic applications
- Cryptography - High-rate measurement device independent quantum cryptography
- Web navigation - Effects of menu organisation and visibility on web navigation for people with dyslexia
- Pattern recognition - Graph embedding using frequency filtering
- Cyber security - A smart contract for boardroom voting with maximum voter privacy
- Computing theory - When does a physical system compute
An example in the human interaction field, indicating academic discipline and methodology, is shown below:
Many of these topics combine disciplinary fields. This is referred to as interdisciplinarity and is increasingly common.
- This is partly due to the wide variety of disciplines that make up Computer Science
- However, it is also due to the need to combine approaches in order to address modern societal challenges
Considering the range of potential projects that match both interests and skills is one of the most difficult stages of a study.
Programming may be involved in computing projects, but it is important to recognise not all computing projects require it.
Unlike research degree projects, those for taught degrees do not need to make significant breakthroughs or be necessarily be publishable.
The different types of Computer Science projects have broadly been suggested to be:
- Research based
- Development
- Evaluation
- Industry based
- Problem solving
These different types of project may involve work in the following areas:
- Algorithms and data structures
- Applied computer science
- Artificial intelligence
- Computer architectures and hardware
- Databases
- Formal methods
- Graphics and visualisation
- Human computer interaction
- Image processing, vision, pattern recognition
- Information systems
- Networking
- Security and cryptography
- Software engineering
- Theoretical computer science
Identifying areas of interest and basing projects around them can help maintain motivation throughout the research process.
Identifying existing skills and knowledge, as done previously, can help determine how reasonable a research project is.
Investigating previous university projects and conducting wider reading can provide inspiration or ideas for building upon existing work.
- Brainstorming can also be used to generate ideas quickly. It involves listing ideas quickly and without judgement, evaluating and assessing them after
A university brainstorming activity offered the following potential topics:
- Developing a time-series model pipeline for accurate price validation of products
- Developing a model to identify fake news based on features and content
- Developing a multi-agent reinforcement learning model of the stock markets
- Predicting cybersecurity threats with time-series analysis
- Perceptions on privacy concerns in relation to smart homes devices
- Developing a model that can identify plagarism involving AI generated text
Asking "so what?"* can help add depth and ensure a project is meaningful or at least interesting.
- Required: Chapters 2, 3, Projects In Computing And Information Systems: A Student's Guide, Dawson
- Required: Chapter 2, Research Design, Qualitative, Research Design, Qualitative, Quantitative and Mixed Methods Approaches, Creswell and Creswell
- Extension: Chapters 7, 8, Dissertations and Project Reports: A Step by Step Guide, Cottrell
Research can be classified from different perspectives: field, approach, and nature.
Research field is a label used to group research and researchers with similar interests, such as artifical intellignce or information systems.
Approach relates to the research methods employed in the research process, such as surveys or experiments.
The nature of research describes its overall contribution to knowledge and can be broken down further:
- Purely theoretical
- Evaluating theory for its practical application
- Applying research with a practical outcome
Please note that purpose is categorised under nature in this classification, as the purpose of research is knowledge.
Regardless of the classification, good research tends to involve similar characteristics:
- Open mindedness - questioning conventional wisdom
- Critical analysis - considering alternative interpretations and sources
- Generalisations - making generalisations and specifying limits on them
Some social science texts may use the term research object where Computer Science would use topic or phenomenon.
Generally, the topic should be of interest, creative, and not repetitive.
Some useful techniques to get started include:
- Describing an interesting area in a sentence
- Creating an initial working title
- Phrasing the topic as a question
Initally the topic may be broad, but needs to refined and narrowed to ensure the project has a viable scope. This can be aided by asking the following questions:
- What phenomena are of interest?
- What are the main problems?
- What are the boundaries?
Deliberately practicing topic identification, adding context, and narrowing breadth, can be useful.
The unit of analysis is the what and who being studied: individuals, groups, artefacts, theories etc.
As there should be discussion about it at the end of a study, any issues should be identified prior to beginning. These could include accessibility and ethical issues.
Clustering is a technique that can help refine broad topics by exploring aspects that can be focused on.
This generally involves listing keywords related to areas of interest and logically grouping them together
One such technique is using Research Territory Maps (RTM). These can help explore links between subtopics:
Alternatively, relevance trees can be used. These take high level thoughts about a field and make them more specific:
Finally, traditional spider diagrams are an option. These involve elements of both previous techniques:
Obviously these are not exclusive, and multiple diagrams can be used.