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Machine Learning for Education - Automatic teaching

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  • Full or part time
    Dr Tom Fincham Haines
  • Application Deadline
    No more applications being accepted
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

The goal of this project is to develop machine learning algorithms that can teach humans.

Consider a model of the concepts taught during a lesson, including dependencies. This model can then drive a formative test, to identify weaknesses in a student’s understanding by asking questions of them. The results will indicate what, if anything, a student needs further help with.

Applying this modelling/questioning/revision structure iteratively is, in a limited sense, teaching. This
project is about automating its steps:
● Extracting lesson models from a variety of data sources.
● Diagnosing student misunderstandings with the fewest questions.
● Automatic question generation.
● Revision material generation.
In all cases the aim is to use machine learning to give each student an experience tailored to their
strengths. Teacher involvement is expected at all steps, though the choice of complete automation is a long term goal.

This belongs to the area of machine teaching [1] (this term has been overloaded, and has multiple meanings), which is closely related to active learning. Optimal teaching of classification problems has been explored [2], and there is also work on how to optimise teacher-student interactions [3], often built on Bayesian graphical models (which is called curriculum learning if both the student and the teacher are computers).

Candidates should normally have a good first degree or a Master’s degree in computer science, maths or a related discipline. A strong mathematical background is essential; good programming skill and previous machine learning experience highly desirable.

Informal enquiries are welcome and should be directed to Dr. Tom SF Haines ([Email Address Removed]).

Formal applications should be made via the University of Bath’s online application form for a PhD in Computer Science:
https://www.bath.ac.uk/samis/urd/sits.urd/run/siw_ipp_lgn.login?process=siw_ipp_app&code1=RDUCM-FP01&code2=0012

For general information on studying for a PhD in computer science at Bath, see:
http://www.bath.ac.uk/science/postgraduate-study/research-programmes/phd-computer-science/

Anticipated start date: 1 October 2018.
Applications may close earlier than the advertised deadline if a suitable candidate is found; therefore, early application is recommended.

Funding Notes

UK and EU students applying for this project may be considered for a University Research Studentship which will cover Home/EU tuition fees, a training support fee of £1,000 per annum and a tax-free maintenance allowance at the RCUK Doctoral Stipend rate (£14,553 in 2017-18) for a period of 3.5 years.

Note: ONLY UK and EU applicants are eligible for this studentship; unfortunately, applicants who are classed as Overseas for fee paying purposes are NOT eligible for funding.

References

[1] “Becoming the Expert - Interactive Multi-Class Machine Teaching”, by Johns, Mac Aodha and Brostow, CVPR 2015.

[2] “Optimal Teaching for Limited-Capacity Human Learners”, by Patil, Zhu, Kopec and Love, NIPS 2014.

[3] “A rational account of pedagogical reasoning: Teaching by, and learning from, examples”, by Shafto, Goodman and Griffiths, Cognitive Psychology 2014.


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