The rise of data-driven education

The Oxford English Dictionary definition of the word ‘knowledge’ is ‘facts, information, and skills acquired through experience or education; the theoretical or practical understanding of a subject’. Thus, one could define learning as ‘the acquisition of data’, and as students absorb this data, a vast amount of data is created at the same time. In fact, recent developments mean that more data is being created than ever before. Concrete statistics are hard to find, but estimates reveal that at least 50% of classes will be delivered online by 2019. Online learning (e-learning), mobile learning (m-learning), MOOCs and blended learning are current trends that create more data than traditional teaching methods.

Success and dropout rates can also see the benefits of data science, as shown in the case of Arizona State University. At this American university, learning is digital and customized to the students. Staff can then help students with advice about their strengths and areas they need to work on. ASU has seen student success rates rise 13% and dropout rates have fallen by 54%, a benefit of the extra support that students receive.

Figure 2 : Using data can help reduce dropout rates in universities.

However, this case study highlights the first limitation of the current technologies. In mathematical and scientific courses, Big Data techniques can easily be applied to the learning process, where exams are assessing quantitative, not quantitative, information. Whether such analysis can work as comprehensively for essay-based subjects remains to be seen. Another significant question is whether the learning process can simply be reduced to a series of numbers. Those weary of Big Data will argue that the relationship between teacher and student is never the same, and that a one-size-fits-all policy based on data could never work.

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