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Future education: Content Tailored to the Student

Research Fellow at the HSE’s Center of Education Quality Monitoring Dmitry Abbakumov, author of the ‘Theory and practice of computerized testing’ lecture course and developer of the ‘Adaptivity Guide’ software which makes it possible to adapt e-courses to students’ abilities. He recently gave a presentation about his research at The 2014 Computerized Adaptive Testing Summit at Princeton and at Edcrunch in Moscow. In his interview, he talks about why adaptive education is the future and how it is developing at HSE.

Online education and adaptive technologies

Although adaptive technologies first appeared in education over 50 years ago, they are only just becoming popular. The main explanation for this is, I feel, the availability of massive open online courses (MOOC) and the broader development of IT in education.

Research shows that, in the coming years, online education will continue to grow. For example, the audience for the Coursera platform reached 3 million students in one year after it launched, and today, two years on, has exceeded 8 million students. The number of courses offered by leading universities via Coursera is nearing 1,000.

It is now, when, there are tens and even hundreds of thousands of people enrolled in online courses of study, that it questions of personalisation are even more relevant: which content will be relevant for a particular student at a particular time, which tasks are most suited to them in terms of difficulty? The answers to these questions can be found through adaptive education technologies. We're talking about services, apps, programmes and platforms for adaptive education, in which the digital study materials are tailored to students' needs: delivered at the apposite moment, in a convenient format and most appropriate sequence.

General principles

Education in more complex than adaptive testing. Whatever data transfer technologies we have, however advanced the graphics, however able the lecturers are, the problem of student diversity remains. People are different, and they take different approaches to studying: some find it easier to absorb information in text format, others prefer graphics and diagrams, while others prefer aural courses. Students have different starting levels of knowledge and different abilities, which is why one piece of material can be very difficult for one student, and very easy for others. Then there are all the differences in our memories, attention, motivation, and many other features that differ student to student.

About 40 percent of those who do not succeed in online study courses on Coursera are dissatisfied with the difficulty of the content offered.

 Therefore, adaptive education algorithms need to incorporate numerous parameters on the student side (preparedness, ability, perception, memory, motivation) and on the content side (difficulty, sequence, structure, content).

Adaptive education tends to be built in one way. First data is collected about how people study (what an individual finds difficult, where they make mistakes, how quickly they read, which pictures catch their eye), and then mathematical models and algorithms are used to analyse this information, and then finally – the third phase involves selecting content for study in accordance with the analysis received and sending it to the student.

Adaptive technologies psychometrics

There are not many adaptive learning programmes – the total number worldwide can be counted on the fingers of one hand. And, what's most interesting, the current projects are not currently competing with one another, as they each use different approaches.

For example, one (www.mathsgarden.com) selects the best mathematical problems in difficulty terms for young schoolchildren, another (www.cerego.com) analyses content for memorability and offers students a tailored plan for learning it. Then there is the powerful analytical platform Knewton (www.knewton.com), which collects data on how thousands of students are studying and selects individually tailored tasks for them based on this, also offering suggestions and recommendations regarding study partners.

About 40 percent of those who do not succeed in online study courses on Coursera are dissatisfied with the difficulty of the content offered.

I am interested in tailoring the difficulty levels, sequence, and content of study materials to students’ psychometric profiles. Currently, with support from the HSE Innovation and Enterprise Office in developing the Adaptivity Guide programme. It helps course authors input content changes to improve student learning (in part this content development involves generating new content).

From the start of the academic year we have been working together with the HSE’s Centre for Educational Resource Development to test several models and algorithms I have developed as part of the courses offered on Coursera. I hope that, in December, we will be able to present the first results of this project, and propose our recommendations on the adoption of adaptive elements into these courses.

Liudmila Mezentseva, HSE News Service

Photo: Mikhail Dmitriev

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