Improving student learning in a connected world - Education Matters Magazine
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Improving student learning in a connected world

Data science researchers from the University of Technology Sydney have teamed up with Acer in a project that utilises artificial intelligence and data science to advance learning outcomes.

The University of Technology Sydney (UTS), in partnership with computer technology companies Acer and Intel, has launched a new pilot program that trials new methods of monitoring student attentiveness and learning in the classroom.

An industry first-of-its-kind, the UTS x Acer Learner Attention Analytics Pilot Program is currently being piloted with 200 data science students in the faculty of Engineering and IT at UTS, with several high schools expected to participate in the future.

The program employs the latest technology in attention analysis, with the aim of establishing a fuller understanding of student behaviour in a classroom setting. The desired outcome is the development of a proof-of-concept platform that could help enhance student learning experiences and outcomes.

The pilot program was announced in early April 2019 at the UTS campus in Ultimo by Acer’s Oceania Managing Director, Darren Simmons, and the university’s Executive Director of Data Science, Professor Fang Chen.

Mr Simmons spoke of the necessity of harnessing developments in computer technology to aid the process of learning in a classroom setting in an age of ever greater connectivity.

He says that with Acer selling 70,000 to 80,000 laptops in the education space annually, he was regularly confronted with questions regarding the efficacy of computer usage in student learning outcomes.

“Do students really learn better? Do they really interact better with the environment? Is the process individualised? Is it actually having an impact? These are questions that are directed to me all the time. It’s a challenge, and one that this project is working on,” adds Mr Simmons.

He says the program, developed in partnership with UTS, would revolutionise personal learning in and out of schools, enable live learning and information sharing, and help develop hardware and software that will enhance the learning experience and promote the wellbeing of students.

“Acer is thrilled to support the UTS Data Science team led by Professor Chen and to be part of a pilot program that will transform the education sector and be crucial in preparing students for the future,” Mr Simmons says. “In addition to education, it will also assist technology providers, such as Acer, to develop new computers and software applications and behaviour-aware computer technology to better facilitate the changing needs of the education sector.”

The project involves the collection of learner data using hand gesture and eye-tracking technology combined with a graphical user interface (GUI) to record mouse movements, keyboard and digital pen usage and eye movements. The data will then be analysed using artificial intelligence (AI) and machine learning algorithms to determine behaviour patterns and the linkage to learning outcomes.

According to Professor Chen, students in a highly-connected, digitalised world now face more distractions than ever before. Combined with the old issue of different students learning at different ways at varying paces, the presence of devices, and their potential to disrupt student attention, is putting traditional teaching approaches that rely on a standardised curriculum to the test. It also creates a greater need for educators to better understand how to capture the attention of different students.

“There’s so much for a student to learn. How can they use the limited time and limited space to learn quickly and in the way that is best for their learning outcomes? The current system is that you read results or your report card after semester or after you finish the course, and you get the score,” says Professor Chen. “How can you know in between how you are performing and how you’re dealing with the content – whether you feel the content is suitable for you or not, and how the pace of the learning is for you?”

Professor Chen explains the purpose of the project was to facilitate the development of learning experience that was more personalised and more responsive to the needs of individual students.

“The aim of the UTS x Acer Learner Attention Analytics Pilot Program is to create an education industry blueprint that can generate tailored personalised learning programs according to learners’ behaviour patterns,” she says. “Using learners’ behaviour as a fundamental indicator of attention and analysing this with AI and machine learning technologies will enable the education sector to optimise the pace and learning materials for the needs of different learners.”

During the launch, the technology was demonstrated to those assembled. When a learner sits in front of a computer, a camera will capture what the learner’s eyes are attending to, while software will register whether, and in what manner, the learner is touching and using the mouse and keyboard. According to Professor Chen, these forms of student monitoring will help determine if the individual learner is focussing on the content they should be and to what extent the student is preoccupied with other distractions.

“Basically, that’s the concept: to capture all the behaviours of the learner in front of a digital device. This is tracking where you’re looking at, mouse movement, and how you’re interacting with the device.”

In this manner, Professor Chen says, data would be collected and analysed to establish what insights can be established on the basis of particular patterns of recorded behaviour. “It is in data analysis that we can try to find out what different behaviours mean. Do they mean that the student is more engaged? Does the engagement link to a better learning outcome or not?”

The pilot project is in the initial data collection stage of the program, involving two classes within the School of Computer Science UTS Data Arena, including an undergraduate class of 150 students studying software engineering, and a postgraduate class of 50 students using machine learning.

According to the UTS research team, the project will also have the potential to be further extended to detect learner frustration and hesitation, the determination of which, they claimed, was essential in developing customised teaching and learning, and integral in improving student experience and wellbeing.

“The purpose of the project is to make the learning experience better and more positive, and to help students learn not only in school, but in life beyond school,” Professor Chen says. “We hope that this is a step towards providing a more engaging and personalised learning experience for every student.”

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