The ‘viva’ is the word used in the UK for the oral thesis defense. The decision to award you a PhD results from a successful completetion of the viva, along with your written work (of course). So, its helpful to know about it. Some people may be under the impression that all you have to do is know your thesis well. Well, from reading advice and interacting with people who have gone through the experience, here are some thoughts.
This advice comes from Andrew Broad, a professor who teaches a course on thesis writing and management. Broad first mentions that there is a general reasoning behind the viva and the kind of questions asked. In general
These are the points being examined (according to Alex Gray from the University of Cardiff):
- Understanding: that you’re ready to become an independent researcher.
- Relationship to other work: that you have a command of your subject-area. Similarity to the work of others doesn’t detract from novelty!
- Novelty – is your work publishable? If you have already published a couple of papers, that should be proof of sufficient originality. Don’t panic about recent publications that are very similar to your work – the important thing is to be aware of them, and to know the differences between your work and theirs.
- What you have achieved, and that you are aware of its implications. What will it make a difference to?
- Demonstration of hypothesis (what you set out to achieve). How have you evaluated/tested your hypothesis? Always be prepared to reconsider your hypothesis if you end up demonstrating something else – it’s vitally important that your results match your hypothesis, and that you have a convincing argument for this.
- Why did you do it the way you did? Not just your practical work, but everything. For example, your literature review should be focused towards your hypothesis.
Broad also lists excellent ideas of what kinds of questions might be asked. Regardless of discipline, these general kinds of questions are common. I suggest, as I will also try to do, pre-answering these questions both as you write your thesis, and again when you are finished or close to finished.
- What is the area in which you wish to be examined? (particularly difficult and important if your thesis fits into several areas, or has several aspects, or seems to fit into an area of its own as mine does).
- In one sentence, what is your thesis? (Resist the temptation to run from the room!)
- What have you done that merits a PhD?
- Summarise your key findings.
- What are you most proud of, and why? This may be asked (again) towards the end of the viva.
- What’s original about your work? Where is the novelty? Don’t leave it to the examiners to make up their own minds – they may get it wrong!
- What are the contributions (to knowledge) of your thesis?
- Which topics overlap with your area?
For topic X:
- How does your work relate to X?
- What do you know about the history of X?
- What is the current state of the art in X? (capabilities and limitations of existing systems)
What techniques are commonly used?
Where do current technologies fail such that you (could) make a contribution?
- How does/could your work enhance the state of the art in X?
- Who are the main `players’ in X? (Hint: you should cluster together papers written by the same people)
Who are your closest competitors?
- What do you do better than them? What do you do worse?
- Which are the three most important papers in X?
- What are the recent major developments in X?
- How do you expect X to progress over the next five years? How long-term is your contribution, given the anticipated future developments in X?
- What did you do for your MPhil, and how does your PhD extend it? Did you make any changes to the system you implemented for your MPhil?
- What are the strongest/weakest parts of your work?
- Where did you go wrong?
- Why have you done it this way? You need to justify your approach – don’t assume the examiners share your views.
- What are the alternatives to your approach?
What do you gain by your approach?
What would you gain by approach X?
- Why didn’t you do it this way (the way everyone else does it)? This requires having done extensive reading. Be honest if you never thought of the alternative they’re suggesting, or if you just didn’t get around to it. If you try to bluff your way out, they’ll trap you in your own words.
- Looking back, what might you have done differently? This requires a thoughtful answer, whilst defending what you did at the time.
- How do scientists/philosophers carry out experiments?
- How have you evaluated your work?
- intrinsic evaluation: how have you demonstrated that it works, and how well it performs?
- extrinsic evaluation: how have you demonstrated its usefulness for a specific application context?
- What do your results mean?
- How would your system cope with bigger examples? Does it scale up? This is especially important if you have only run your system on `toy’ examples, and they think it has `learned its test-data’.
- How do you know that your algorithm/rules are correct?
- How could you improve your work?
- What are the motivations for your research? Why is the problem you have tackled worth tackling?
- What is the relevance of your contributions?
- to other researchers?
- to industry?
- What is the implication of your work in your area? What does it change?
- How do/would you cope with known problems in your field? (e.g. combinatorial explosion)
- Have you solved the field’s problem that you claim to have solved? For example, if something is too slow, and you can make it go faster – how much increase in speed is needed for the applications you claim to support?
- Is your field going in the right direction? For example, if everyone’s been concentrating on speed, but the real issue is space (if the issue is time, you can just wait it out (unless it’s combinatorially explosive), but if the issue is space, the system could fall over). This is kind of justifying why you have gone into the field you’re working in.
- Who are your envisioned users? What use would your work be in situation X?
- How do your contributions generalise?
To what extent would they generalise to systems other than the one you’ve worked on?
Under what circumstances would your approach be useable? (Again, does it scale up?)
- Where will you publish your work? Think about which journals and conferences your research would best suit. Just as popular musicians promote their latest albums by releasing singles and going on tour, you should promote your thesis by publishing papers in journals and presenting them at conferences. This takes your work to a much wider audience; this is how academics establish themselves.
- Which aspects of your thesis could be published?
- What have you learned from the process of doing your PhD? Remember that the aim of the PhD process is to train you to be a fully professional researcher – passing your PhD means that you know the state of the art in your area and the directions in which it could be extended, and that you have proved you are capable of making such extensions.
- Where did your research-project come from? How did your research-questions emerge? You can’t just say “my supervisor told me to do it” – if this is the case, you need to talk it over with your supervisor before the viva. Think out a succinct answer (2 to 5 minutes).
- Has your view of your research topic changed during the course of the research?
- You discuss future work in your conclusion chapter. How long would it take to implement X, and what are the likely problems you envisage? Do not underestimate the time and the difficulties – you might be talking about your own resubmission-order!
Most of the viva will probably consist of questions about specific sections of your thesis, and the examiner should give a page-reference for each question. According to Alex Gray, these questions fall into six categories:
- Clarification. The examiners ask you to explain a particular statement in the thesis. In some cases, their lack of understanding may be due to a typo, e.g. “Why did you connect the client to the sewer?” Also, “not” is a small word which makes a big difference!
- Alternatives considered. Be honest if you didn’t consider alternatives, otherwise you’ll be digging a hole for yourself.
- Awareness of other work.
- Distinction from similar work. Especially recent publications where others are working in the same area – what are the similarities and differences between your work and theirs?
- Correction of errors (typos, technical errors, misleading statements, and so on).
Thanks to Dr. Broad; here is his website: http://www.geocities.com/andrewbroad/cs/cs710/viva.html.
I will try and post more as I find helpful info.