Students completing the IB Diploma are required to completd the Extended Essay as part of the Diploma core. You may elect to do your Extended Essay for Computer Science if you wish (subject to supervision capabilities).
Recommendations for topic selection
- Extended essays should begin with “to what extent”. Should be a question rather than just a topic. Topic needs to be specific and narrowly defined.
- The best Computer Science EE’s are those that specifically compare related technologies/protocols/algorithms against each other in a measured evaluation (this could be search, sort, encryption, compression, path finding, machine learning, … whatever). For example: “To what extent does the efficiency of algorithm ABC compare to algorithm XYZ when performing task PQR”.
Examples of bad topics
- The virus VBS/Love Letter. Why was it so effective in it’s attacks? – Could be ok but needs refinement so emphasis is technical not social.
- Are hackers really malicious individuals? – Too broad, opinion based, sociological rather than technical.
- To what extent will quantum computing mean an end to network security? - Too broad, unable to conduct meaningful primary research
- Which operating system out of Windows or Linux will give a better and more efficient performance? – Define criteria for better and efficient? Under what context? Don’t just take statistics off a website. Conduct primary research as well (install on two identical computers?)
- Artificial Intelligence topics are quite common. Quite a few are done very poorly (lack of genuine technical expertise being displayed). That said there are some very good ones so it can be done – just be cautious (see notes about machine learning that follow).
- Getting historical is a mistake. A comparison analysis of algorithms etc is really essential.
- Stay away from the ethics / advantages / disadvantages of social networks etc - this is ITGS not CS
- Moores law – they get 100s of these each year. Stay away from HISTORY
- WEBSITES, USERS, HISTORY – predominately ITGS topics not CS.
- The effects of the technology – these EE belong under ITGS (where as studying the technology itself – this belongs under CS)
Machine learning based topics
Certainly the flavour of the month. Be aware that means the markers will receive lots of them, so yours needs to be good to stand out from the crowd. What could you do? Some aspects of ML you could use to narrow your EE:
- Learning methods: Supervised, unsupervised, reinforcement learning
- Decide on efficacy measure: Spead of decision making, adaptability, innovation, insight
- Branches of ML: Computational learning theory, Adversarial Machine Learning, Quantum Machine Learning, Predictive Analysis, Robot Learning, Grammar Induction, Meta-Learning
- Available tools: ai-one, Protege, IBM Watson, TensorFlow, Amazon Web Services, OpenNN, Apache Spark, Caffe, Veles
- Topics: Machine Learning Algorithms, Computer Vision, Supervised Machine Learning, Unsupervised Machine Learning, Deep Learning, Neural Networks, Reinforcement Learning, Predictive Learning, Bayesian Network, Data Mining
These are the completion expectations I apply to students who wish to do their EE in Computer Science. Computer Science expectations for Extended Essays
Pre-meeting in early March
- Proposed focus, question, programming elment.
- Agreement on what the student needs to do in order to refine the question & proposed program by the next meeting.
Late March meeting
- Topic and proposed programming element to be approved/locked in this meeting (scope, feasible)
- First formal reflection to be written following this meeting
Deadline: 28 March 2018
- Background research finished
- except where new research is required to explain the programming results (ie: the primary research)
- Program finished
- though data collection could be ongoing - project dependant and requires pre-approval
- Need to be able to start writing up results of the primary research (program) and secondary research after this date.
Submit your draft EE and notes as they currently stand.
Deadline: 16 May 2018
- Action plan for the summer to be agreed upon.
- Second formal meeting to be written as a reflection
Deadline: 20 June 2018
- Full draft which includes results of the programming portion.
- Formal feedback on full draft to be provided pre-Biarritz
Deadline: 24 August 2018
- Final version submitted
Deadline: 1 October 2018
Post-Biarritz viva voce
- All done, let’s celebrate!
Deadline: 17 October 2018
Be guided by the traditional structure for a science experiment:
- Independent variables: The variables you are changing in order to conduct the experiment (the inputs)
- Dependent variables: The variables that change as a result of the experiment (the outputs)
- Control variables: The variables that could affect the outcome if they changed, so have been made constant so they do not cause errornous results.