The United States education system, despite the country’s massive wealth, has never really fulfilled its potential, lagging behind many other developed nations. Indeed, research on basic education levels throughout the world by the Social Progress Imperative ranked the USA 14th in the world in 2015/2016, with east Asian countries dominating the leaderboard - South Korea tops the rankings, followed by Japan in second place.
The reasons for this are complex. Lack of funding, poor teaching, poor teacher training, even women abandoning the teaching profession because of more equal corporate opportunities, have all been blamed for the problem. One oft-cited reason is the failure to keep pace with the needs of our hyper-connected society and advancing technology, although this is increasingly being rectified as schools embrace analytics and machine learning, bringing powerful algorithms into classrooms to help refine teaching methods.
We have always used data in the classroom to a degree. In Charles Dickens’ novel Hard Times, school board Superintendent Mr Gradgrind’s insisted that ‘Now, what I want is Facts. Teach these boys and girls nothing but Facts. Facts alone are wanted in life. Plant nothing else, and root out everything else. You can only form the minds of reasoning animals upon Facts; nothing else will ever be of any service to them.’ This is an outlook that has largely stood true for measuring performance since the industrial revolution, with various metrics, grades, and averages in the classroom providing students with an idea of what ‘good’ is and a route to achieving it. On a macro level, districts analyze large data sets for patterns around students' academic performance and attendance to show which schools and teachers are failing.
What has been recognized since Gradgrind prowled the corridors is that all pupils learn differently. They have different needs, come from different backgrounds, and have different interests. As such, they learn at different speeds, some based on what they read, some what they see, and some through experience. Data in all industries has long been used to tailoring marketing materials, but schools have lagged behind when it comes to using data for tailoring educational materials in the same way. This is changing, driven by how content in the classroom is consumed, with a move towards learning software and digital games generating massive amounts data that can be mined for evidence of student learning.
Teachers can use data from learning software to see how well certain ideas or topics are being grasped by students and adapt their lesson plans accordingly. Many of these programs even use machine learning to understand for themselves where a pupil needs work, the style of learning that most suits them, and what can be done to help them progress their knowledge, essentially removing the teacher from the equation and allowing them to focus on children for whom virtual learning may not be the best option.
And it does not stop there. In the future, classrooms could be filled with even more data points to help better understand students. Cameras could capture each child's every facial expression to gauge when they are engaged, helping to improve their teacher’s performance. They could be given wearable devices such as Fitbits, the data from which could like devices that track everything from their heart rates to their time between meals to improve physical condition and how it correlates to their performance and behaviour in the classroom.
There are numerous issues around the collection of data in schools, with many parents likely to view it as infringing on their child’s privacy. There is also the issue of funding, which means that many areas that really need it are likely to go without. However, as the technology improves and gets cheaper, there is a clear need, and moving forward schools should look to incorporate it as much as possible.