Online maths company has partnered with scientists to identify what makes lessons successful – and to see if AI can be used to improve teaching
Ambar presses her hand to her forehead, nose crinkled in concentration as she considers the question on her screen: how many sevens in 91? The ten-year-old has been grappling with it for about a minute when she smiles: “13!”.
Her tutor responds by posting a large smiley cat picture on her screen – the virtual equivalent of a pat on the back. He is sitting on the other side of the world in an online tutoring centre in India.
Ambar, who attends Pakeman primary school in north London, is one of nearly 4,000 primary school children in Britain signed up for weekly one-to-one maths sessions with tutors based in India and Sri Lanka. The lessons, provided by a company called Third Space Learning, are targeted at pupils struggling with maths – particularly those from disadvantaged backgrounds.
From next year, the platform will become one of the first examples of artificial intelligence (AI) software being used to monitor, and ideally improve, teaching.
Together with scientists at University College London (UCL), the company has analysed around 100,000 hours of audio and written data from its tutorials, with the goal of identifying what makes a good teacher and a successful lesson.
Tom Hooper, the company’s CEO, said: “We’re looking to optimise lessons based on the knowledge we gain. We’ve recorded every lesson that we’ve ever done. By using the data, we’ve been trying to introduce AI to augment the teaching”.
Initially, the company’s 300 tutors will receive real-time, automated interventions from the teaching software when it detects that a lesson may be veering off-course.
Pupils on the programme have a 45-minute session with the same tutor each week. They communicate through a headset and a shared “whiteboard” (they can’t see each other). The lessons at Pakeman school are tailored to the individual, including visual rewards linked to the child’s interests. Premier League strikers for nearby Arsenal, cute animals and pink, iced doughnuts flash up on the screens of Ambar’s classmates.
In addition to the raw audio data, each lesson has various success metrics attached: how many problems completed, how useful the pupil found the session, how the tutor rated it. Using machine learning algorithms to sift through the dataset, the UCL team has started to look for patterns.
An early analysis found, perhaps unsurprisingly, that when tutors speak too quickly, the pupil is more likely to lose interest. Leaving sufficient time for the child to respond or pose their own questions was also found to be a factor in the lesson’s success, according to Hooper. These observations are likely to form the basis of the initial prompts that the tutors will receive, probably in the form of messages flashing up on their screen.
“We’re going to be drip-feeding it in in relatively simple ways to start with,” said Hooper.