Mr Baumgarten

Computer Science teacher and self confessed geek

Extended essay

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" and be framed as question rather than just a topic.

The question 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).

The general formula for a successful EE question is: "To what extent does the efficiency of algorithm ABC compare to algorithm XYZ when performing task PQR".

Poor topics

Computer Science is a technical course. Social issues related to ICT belong in the ITGS course.

Examples:

  • Computer viruses: Why are they effective in their attack? Ensure this is technical rather than social.
  • Are hackers malicious people? – 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.
  • 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

Experiment structure

While not a requirement, and should be customised to the needs of your EE, I recommend students to be guided by the traditional structure for a science experiment:

  • Aim
  • Hypothesis
  • 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.
  • Method
  • Results
  • Conclusion

Timeline class of 2021

Meeting 1 (March 6, 2020)

  • Discuss proposed focus, question, programming/experiment elment.
  • Topic and proposed programming element to be approved/locked in this meeting (scope is appropriate?, proposal is feasible?)

Meeting 2 (April 3, 2020)

  • "1000 words" deadline
  • First reflection (fill out on RPPF)
  • Background research finished - except where new research is required to explain the experiment results (ie: the primary research)

Meeting 3 (April 29, 2020)

  • "2000 words" deadline
  • Programming for the experiment finished, though data collection could be ongoing dependant on project
  • Start documenting results of the experiment/primary research/program

Meeting 4 (June 19, 2020)

  • "4000 words" deadline
  • Full fraft EE with citation and referencing
  • Written feedback
  • Second reflection (RPPF #2)
  • Action plan for the summer to be agreed upon

Meeting 5 (August 21, 2020)

  • Identify/resolve final issues prior to EE submission

Submit final (9 September 2020)

  • Submit final EE

Meeting 6 (9 October 2020)

  • Viva Voce final reflection (RPPF #3)