Lec1

Aims

• To provide a deep and systematic understanding of the nature and conduct of Computer Science research
• To enable students to undertake independent research
• To enhance existing transferable key skills
• To develop high-order transferable key skills
• To remind students of the Legal, Social, Ethical and Professional (LSEP) issues applicable to the computer industry

Learning Outcomes

• Have an understanding of how established techniques of research and enquiry are used to extend, create and interpret knowledge in Computer Science

• Have a conceptual understanding sufficient to:

  • evaluate critically current research and advanced scholarship in Computer Science, and
  • propose possible alternative directions for further work

• Be able to:

  • deal with complex issues at the forefront of the academic discipline of Computer Science in a manner, based on sound judgments, that is both systematic and creative,
  • demonstrate self-direction and originality in tackling and solving problems within the domain of Computer Science,
  • act autonomously in planning and implementing solutions in a professional manner, and
  • define, plan, and/or carry out a project related to research and to communicate conclusions clearly to both specialists and non-specialists

• Make use of the qualities and transferable skills necessary for employment requiring:

  • the exercise of initiative and personal responsibility,
  • decision making in complex and unpredictable situations,
  • scientific risk identification, assessment and control, and
  • the independent learning ability required for continuing professional development

• Understand and participate within the professional, legal, social and ethical framework within which they would be expected to operate as professionals within the IT industry

• Have the skills set to be able to continue to advance their knowledge and understanding, and to develop new skills to a high level, with respect to continuing professional development as a “self-directed life-long learner” across the discipline of Computer Science

In short, you should learn to

• understand research and research methods in Computer Science
• be able to plan, and conduct your own research, taking into account ethical, legal, and professional limitations
• be able to communicate its results

British Computer Society

• BCS (British Computer Society), The Chartered Institute for Information Technology (IT)
• Recognised as a professional engineering institute for the registration of information systems and software engineers
• Chartered Scientist is a professional qualification in the UK
• the required standard for Chartered Scientist registration is MSc qualification (or equivalent) with four years of postgraduate work experience

What to Do

• Pick a topic that you like
• Pick a “top” paper on that topic
• Use that paper as a starting point to explore the area around it

Deliverables

• A review article (10 A4 pages max including everything, ≥ 11pt font, ≥ 1 inch margins) introducing the reader to the area

• An accompanying report

  • short document discussing the whole development.
    • how the group worked together
    • challenges faced
    • solutions to any obstacles
    • professional and ethical issues
    • conclusions and other interesting remarks
  • It may also be used as an appendix for anything that the group wants to report but is not suitable to be included in the paper draft

Lec2

What Is ‘Research’

• an active, diligent, and systematic process of inquiry in order to discover, interpret or revise facts, events, behaviours, or theories, or to make practical applications with the help of such facts, laws, or theories.
• a collection of information about a particular subject.
• derives from the Middle French and the literal meaning is “investigate thoroughly”.

Knowledge: A Hierarchy

Knowledge is a particular level in a hierarchy:

• Data
• Information
• Knowledge
• Wisdom

Knowledge: Data and Information

Data on its own has no meaning, only when interpreted by some kind of data processing system does it take on meaning and becomes information

Knowledge: Alternative Definitions

Knowledge (Dawson 2005)

• higher level understanding of things

• represents our understanding of the ‘why’ instead of the mere ‘what’

• interpretation of information in the form of rules, patterns, decisions, models, ideas, etc.

• In natural sciences, understanding ‘why’ is too ambitious most of time; understanding ‘how’ is usually what we aim for

• In other areas, understanding ‘why’ is trivial, understanding ‘how’ is challenging

Knowledge and Theories: Criteria

Theory (http://en.wikipedia.org/wiki/Theories) A body of (descriptions of) knowledge is usually only called a theory once it has a firm empirical basis, that is, it

• is consistent with pre-existing theory to the extent that the pre-existing theory was experimentally verified, though it will often show pre-existing theory to be wrong in an exact sense

• is supported by many strands of evidence rather than a single foundation, ensuring that it’s probably a good approximation if not totally correct

• makes (testable) predictions that might someday be used to disprove the theory, and

• has survived many critical real world tests that could have proven it false,

• is a/the best known explanation, in the sense of Occam’s Razor, of the infinite variety of alternative explanations for the same data.

Knowledge and Theories: Facts versus Theories

• Fact
  1. a truth (statement conforming to reality) or
  2. data supported by a scientific experiment
• Status of a ‘truth’ is by and large unachievable
• A theory is formulated, developed, and evaluated according to the scientific method
• Given enough experimental support a theory can be (a scientific) fact

Investigation

• Scientists use observations and reasoning to propose explanations for natural phenomena in the form of hypotheses
• Predictions from these hypotheses are tested by experiment and further technologies developed
• Any hypothesis which is cogent enough to make predictions can then be tested reproducibly in this way
• Once established that a hypothesis is sound, it becomes a theory
• Sometimes scientific development takes place differently with a theory first being developed on the basis of its logic and principles

Research and Originality

• Originality: Doing something that has not been done before

• Dawson (2005): There is no point in repeating the work of others and discovering or producing what is already known

• Only true for what is truly known (i.e. very little)

  • Theories make predictions, which need to be tested
  • Those performing the tests are neither infallible nor trustworthy
  • Tests need to be repeated and results replicated

Fallibility

Cold fusion (http://en.wikipedia.org/wiki/Cold_fusion)

• Cold fusion: Nuclear fusion reaction that occurs well below the temperature required for thermonuclear reactions, that is, near ambient temperature instead of millions of degrees Celsius
• First reported to have been achieved by Pons (University of Utah) and Fleischmann (University of Southampton) in 1989
• Scientists tried to replicate results shortly after initial announcement
• Teams at Texas A&M University and the Georgia Institute of Technology first confirmed the results, but then withdraw those claims due to lack of evidence
• Vast majority of experiments failed

Trustworthiness

• Researcher at Bell Labs working in the field of condensed matter physics and nanotechnology
• In 2001, he was listed as an author on an average of one research paper every eight days
• Claimed to have produced a transistor on the molecular scale
• Published (and peer reviewed) papers were suspected to contain duplicated and anomalous data
• Dismissed after an investigation found 24 cases of misconduct Science withdrew 8 and Nature 7 papers co-authored by Sch ̈on

Research and Originality

• Areas of originality (Cryer 1996)
• Exploring the unknown
  • Investigate a field that no one has - investigated before
• Exploring the unanticipated
  • Obtaining unexpected results and investigating new directions in an already existing field
• The use of data
  • Interpret data in new ways
• Tools, techniques, procedures, and methods
  • Apply new tools/techniques to alternative problems Try procedures/methods in new contexts

Choosing a Project

• The project needs to be within your capabilities The project needs to have sufficient scope
• The project needs to interest you
• The project needs to have a serious purpose - The project needs to have a clear outcome
• The project needs to be related to your degree programme The resources required are available or can be obtained

Conclusion

• Choosing the right project is an important stage crucial to your success
• There are a number of techniques that can assist you
• In a project proposal or project
  • specification stick to the required structure and
  • address all the guiding questions as precisely as possible