Big Data will make Feedback more focussed on effective teaching rather than student progress, it will make learning more individualised, and it will enable us to make probabilistic predictions about what programmes are best for different students.
This is according to Big Data enthusiasts Meyer-Schonberger and Cukier in their (2017) reprint of their 2013 original ‘Big Data: The Essential Guide to Work, Life and Learning in the Age of Insight…
This post is a summary of the section at the back of this book, which focuses on big data and education (introduction to this section is here).
An excellent counter point to the outrageous, almost entirely speculative and sweepingly general claims made in this book is Neil Selwyn’s ‘Is Technology Good for Education?‘ – the later is based on stacks of peer-reviewed evidence, the former on speculation only.
How will big data change feedback in education?
In the small data age, data collection in schools was largely limited to test scores and attendance, focussing on collecting standardised data on student performance, with feedback being almost exclusively in one direction – from the teachers to the schools to the kids and their parents – what is not measured is how well we teach our kids, or how effective different teaching techniques are in facilitating student progress.
Big data changes this by datafying the learning process – for example, e-books allow us to track how students read books, what they take notes one, at what point the give up reading, what sections they go back and check – thus we can measure how effective different books are, or different passages within books are, at helping students to understand knowledge, which can be used as a basis for immediate and differentiated intervention by teachers.
We could also use e-books in conjunction with testing to measure the relationship between different textual materials and the ‘decay curve’ – the rate at which students forget knowledge, which might be useful in improving test scores.
Companies such as Pearsons and Kaplan are very involved in producing e-books, but at time of writing (2017) even in America only 5% of school text books are digital.
In schools, the education which we are exposed to is standardised into a one size fits all package, tailored to a mythical average student. Learning has barely evolved from the industrial era – the materials students are given are identical, and the learning process still works essentially like an assembly line, with all students being paced through a syllabus at the same rate and learning benchmarked against a series of standardised tests.
All of this is tailored towards the needs of the teachers and the system, not the needs of the students.
However, in the Big Data age, following the American economist Tyler Cowen, ‘average is over’, and following Khan Academy founder Sal Khan ‘one size fits few’. The problem with the current, industrial era education system is that very few people actually benefit from it – the bright student is bored, while the weaker understands nothing. What we need is a means of flexibly adapting the pace and content of teaching to better fit the needs of individual students.
Tailoring education to each student has long been the aim of adaptive-learning software – an example of this is Carnegie Learning’s ‘Cognitive Tutor’ for school mathematics which decides which math questions to ask based on how students answered previous questions. This way it can identify problem areas and drill them, rather than try to cover everything but miss holes in their knowledge, as happens with the traditional system.
Another example is New York City’s ‘School of One’, a math programme in which students get their own personalised ‘playlist’ determined by an algorithm, each day, with maths problems for them suited to their needs.
Such individualised learning systems are dynamic — the learning materials change and adapt as more data is collected, analysed and transformed into feedback. More advanced material is only provided once students have mastered the fundamentals.
All of this is based on the idea of the ‘student as consumer/ client’ – one argument is that ‘if we can rip our favourite music and burn it into our own playlist’, why can’t we do this with education? A second argument is that in any other field of business, consumers provide feedback on products and the manufacturers improve (and increasingly personalise) the products to meet the demands of diverse consumers…. Adaptive learning should transform education into something which is more responsive to the needs of students/ consumers, rather than it being led by unresponsive systems and teachers.
Supporting evidence for adaptive learning:
In a trial of 400 high school freshmen in Oklahoma, the Cognitive Tutor system helped them achieve the same level of math proficiency in 12% less time than students learning math in the traditional way.
According to Bill Gates, talking in 2013, students on remedial education courses using adaptive software outperformed students in conventional courses and colleges benefitted from a 28% reduction in the cost per student.
Big data will provide us with insights into how people in aggregate learn, but more importantly, into how each of us individually acquires knowledge. These insights are not perfect – they do not give us cause and effect relationships – Big data insights are probabilistic:
For example, we may spot that teaching materials of a certain sort will improve a particular person’s tests scores by 95%, but if we make a recommendation based on this, it will not work in 5% of cases.
This is something we are going to have to learn to live with, and parents and students are going to have to bear the risk – for example, all Big Data can do is to tell ‘clients’ that if they study this particular course, then there is a 70-80% chance they’ll see ‘x’ amount of improvement.
However, some probabilities will be more certain than others, and so for at least some specific recommendations, we can act with reasonable certainty.
We are going to have to get over seeing through the world through the lens of cause and effect…
Criticisms of Mayer-Schonberger and Cukier’s views on how Big Data will transform education
Personally, as a teacher myself I’m sceptical when non-experts start making sweeping predictions about the future of education based on speculation, especially when one of the claims for the Big Data is that it provides empirical insights, such speculation is hypocritical, precisely because it’s not based on any actual data!
The idea that transnational technology companies are going to help everyone in education is nonsense – they are profit driven, the fact that profit comes first, and that this will be a limiting factor in how data is used in the future is not even mentioned.
They see ‘teachers as the enemy’ – as a barrier to Big data, this is highly dismissive of a group of people who have gone into a job to benefit children, where I doubt that people for tech companies do not have this as their primary motive – also see below, for an alternative explanation of their criticism to ‘teachers as a barrier’ to ed tech companies playing more of a role in education.
The ‘one size fits all model’ might be dominant in education because with a teacher student ration of 1-100 (in colleges) teachers literally cannot meet the individual needs of individual students. There simply isn’t time for this, along with the need for teachers to keep on top of the knowledge themselves, and keep up to date with technological changes, institutional-legal requirements, and do all of the (still necessary) marking of students work.
Related to the above point, making teachers analogous to other professionals with clients, I don’t believe there’s any other field of work where professionals are expected to deal with 100 clients at a time and personally interact with each of them every single day in a meaningful way… dealing with diverse and complex knowledge (rather than specialising in one particular thing, i.e. a haircut, or a financial advice for example) – while it might be fair to expect teachers to respond to ‘clients’ demands, 1 teacher cannot do this with 100 students. The ratio needs altering (1-10 maybe?).
The authors cit very few examples of peer-reviewed evidence to back up their claims.