Richard Cox, Shaaron Ainsworth

Education is far less tied to traditional classrooms and now happens at home, with friends and online. With technology playing an increasingly key role in its liberation, it becomes important to understand how
people learn with it.

Without that understanding, the evidence informed design of technology-enhanced learning systems is hampered, limiting our ability to provide rich and effective educational experiences. Luckily, the same
technologies that enhance learning also enable us to gain insights into the nature of learning. This is because the devices that students use can also serve as microscopes, revealing in close-up the details of their learning.

Researchers in education now ‘data mine’ the records of thousands of students’ interactions with technology-enhanced learning systems. Data-mining is revealing which curriculum components pull their weight in terms of learning outcomes, very difficult information to collect in traditional ways. It can also be used to study how students use online socialnetworks, experiment with new forms of information presentation and feedback, discover the extent to which students differ, and encourage collaborative learning. And it means that learning technology systems can be continually improved on the basis of real-world evidence.

Such evidence has told us in the past that learning can benefit from a cyclical approach and from a social one. A learning cycle might consist of traditional teaching followed by a phase in which students explore material on their own or in small groups. By observing others, they learn what works and what not
to bother trying themselves. Importantly for their academic self-esteem, they also come to understand that they are not unique in their misconceptions and misunderstandings.

Technology-enhanced systems enable new and large-scale forms of social learning that provide powerful experiences for students and masses of data for researchers. A good example is social network behaviour, understanding how networks of people come together and move apart, how they access and create information, and how they construct individual and group knowledge.

Such social learning underlines the complementary relationship between technology and education – what technology discovers about learning being used to shape how technology promotes learning and vice versa.

Take the example of gaming. Educational games are increasingly seen as a compelling way of engaging students. Difficult concepts can be accessed in ways that are interactive and concrete, and players motivated to explore them because the games are fun. But the key challenge for designers is ensuring
that educational games have a positive influence on learning rather than one that is negative or distracting. One answer is to log students’ interactions with games and use the data to determine how well they are learning, whether their performance is influenced by issues such as gender and how the game itself can be improved.

Zombie Division is a 3D adventure game which
helps eight to 11-year-olds with maths. Matrices, the hero, explores a labyrinth populated by skeletons of warriors with numbers on their chests. To complete the maze, Matrices must engage some of these warriors in combat and defeat them; others he must avoid as he cannot overcome them. The key characteristic of the design is that the mathematical ideas – identifying number patterns, multiples, primes, factors and squares – are embedded in the game, not just added ‘chocolate-covered broccoli’.
This embedding is achieved by providing Matrices with three weapons to divide opponents. If he chooses appropriately, he defeats the warrior skeleton, but if he makes a mistake the skeleton will attack him instead.

Zombie Division doesn’t just help children improve their maths skills. It also logs their performance in order to provide teachers with valuable information. They can see what division problems a particular child finds difficult or easy. They can discover if factors such as gender, amount of game experience or
mathematical knowledge influence their ability to play and learn.

Data can also be scrutinised to see if children can apply what they’ve learnt in the game to other contexts. For example, their performance when dividing a number on a skeleton can be compared to how they get on doing the same calculation in a typical maths lesson.

In trials children performed better on the ‘skeletons’ than they did on the numbers; but still did better on the numbers if they had first practised on the skeletons. Such data can be used by parents and teachers to ensure children practise appropriate tasks, help designers find features that make games
effective and help researchers understand why we learn more when having fun.

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