Precision education represents a shift from the collection of assessment-type data about educational outcomes, to the generation of data about the intimate interior details of students’ genetic make-up, their psychological characteristics, and their neural functioning.
If we want a full and comprehensive debate about the role of data in our lives, we need to first appreciate that the analysis and use of our data is not restricted to the types of figures that we have been reading about in these recent stories – it is deeply embedded in the structures in which we live.
The activist and internet entrepreneur Maciej Ceglowski once described big data as “a bunch of radioactive, toxic sludge that we don’t know how to handle.” Maybe we should think about Google and Facebook as the new polluters. Their imperative is to grow! They create jobs! They pay taxes, sort of! In the meantime, they’re dumping trillions of units of toxic brain poison into our public-thinking reservoir. Then they mop it up with Wikipedia or send out a message that reads, “We take your privacy seriously.”
It is becoming evident that Big Data alone won’t be able to fix education systems. Decision-makers need to gain a better understanding of what good teaching is and how it leads to better learning in schools. This is where information about details, relationships and narratives in schools become important. These are what Martin Lindstrom calls Small Data: small clues that uncover huge trends. In education, these small clues are often hidden in the invisible fabric of schools. Understanding this fabric must become a priority for improving education.
Seemingly insignificant behavioral observations containing very specific attributes pointing towards an unmet customer need. Small data is the foundation for break through ideas or completely new ways to turnaround brands.
Sahlberg takes this concept and applies it to education. Some ‘small data’ practices he suggests include:
- Focus on formative assessment over standardised testing
- Develop collective autonomy and teamwork in schools
- Involve students in assessing and reflecting their own learning and then incorporating that information into collective human judgment about teaching and learning
This move away from standardisation is something championed by people like Greg Whitby.
Rather than thinking of AI as “artificial intelligence,” Eubanks effectively builds the case for how we should think that AI often means “automating inequality” in practice.