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Individualising online education through machine learning

 

Our project focuses on applying machine learning – the ability of computers to learn from data – to the design of the next generation of online education systems. Our goal is to automatically adapt such systems to the background, skills and learning style of students to improve the delivery of knowledge to them.

Portrait / project description (ongoing research project)

This project will rethink the design of the next generation of online learning systems to enable the technology to deal with Big Data-sized data sets, such as MOOCs. The project will also validate the quality of our learning results using statistical theory. Based on this new machine learning framework, we will focus on implementing an individualised online learning system to enhance students’ learning experience by automatically exploring their learning style and skills. Moreover, we will study the decision problems in the design of such individualised systems and develop an appropriate (Bayesian) optimisation framework for their automation.

Background

The Internet has significantly reduced the cost of access to information, giving rise to so-called massive online open courses (MOOCs). The social benefits of MOOCs are clear: they provide students borderless, free access to lectures from the best universities in the world. Moreover, MOOCs themselves generate huge amounts of data that can help understand student behaviour to better tailor offerings to their needs.

Aim

Traditional machine-learning technologies used to analyse MOOCs data, such as neural networks, are behind the times. Consequently, our project will develop modern machine learning techniques for learning efficiently from data sets in MOOCs.  

Relevance/application

As online learning systems are not based on static textbooks, they can keep up with today’s fast-paced changes. By developing automatic ways of individualising these systems to the needs of students, we will ensure that knowledge transfer in education is future-proof.

Original title

Theory and methods for accurate and scalable learning machines

Project leader

  • Professeur Volkan Cevher, Laboratoire de systèmes d'information et d'inférence, EPFL

 

 

Further information on this content

 Contact

Professeur Volkan Cevher Laboratoire de systèmes d'information et d'inférence
EPFL - STI - IEL – LIONS
Bâtiment ELE 233
Station 11 1015 Lausanne volkan.cevher@epfl.ch

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