Learning Analytics, Surveillance and Conversation

In the noisy discourse that surrounds learning analytics, there are some basic points which are worth stating clearly:

1. Learning Analytics, like any “data analysis” is basically counting: complex equations which promise profound insights are in the end doing nothing other than counting. 
2. Human beings determine what is to be counted and what isn’t, and within what boundaries one thing said to be the same (and counted as the same) as another thing. 
3. Learning analytics takes a log of records – usually records of user transactions – and re-represents it in different ways.
4. The computer automates the process of producing multiple representations of the same thing: these can be visual (graphs) or tabular 
5. Decisions are facilitated when one or many of the representations automatically generated by the computer coincides with some human’s expectation. 
6. If this doesn’t happen, then doubt is cast over the quality of the analysis or the data.
7. Learning analytic services typically examine logs for multiple users from a position of privilege not available to any individual user. 
8. Human expectations of the behaviour of these users is based on bias surrounding those aspects of individual experience that a person in privilege will have: typically this will be knowledge of the staff ("the students have had a miserable experience because teacher x is crap")
9. Often such high-level services exist on a server into which data from all users is aggregated with little understanding by users as to what might be gleaned from it. 
10. The essential relationship in learning analytics is between automatically generated descriptions and human understanding.  
11. Data analytic tools like Tableau, R, Python, etc all provide functionality for programmatically manipulating data in rows and columns and performing functions on those rows and columns. Behind the complexity of the code, this is basically spreadsheet manipulation. It is the principal means whereby different descriptions are created. 

So the real question about learning analytics is a question about automatically-generated multiple descriptions of the data, and how those multiple descriptions influence decision-making. 

Of course, decisions made from good data will not necessarily be good decisions, nor are decisions made with bad data necessarily bad. What matters is the relationship between the expectations of the human being and the variety of description they are presented with. 

In teaching, communication, art, biology or poetry, multiple descriptions of things contribute to the making of meaning. Poets assemble various descriptions to convey ideas which don't have concrete words. Composers create counterpoint in sound. When we discuss things, we express different understandings of the same thing. And teaching is the art of expressing a concept in many different ways. What if some of these ways are generated by machines?

AI tools like automatic translaters or adaptive web pages are rich and powerful objects for humans to talk about. As such tools adapt in response to user input, people talking about those tools understand more about each other. Each transformation reveals something new about the people having a discussion. 

This is important when we consider analytic tools. The richness of the ability to generate multiple descriptions means that there is variety in the different descriptions that might be created by different people. The value of such tools lies in the conversations that might be had around them. 

With the emphasis on conversation, there is no reason why analytic tools should be cloud-based. There is no reason why surveillance is necessary. They could be personal tools, locally-installed instead. Their simple job is to process log files relating to one user or another. Through using them in conversation, individuals can understand each other's understanding better. They should be used intersubjectively.

Recently I've been doing some experiments with personally-oriented analytical tools which transform spreadsheet logs of activity into different forms. The value in the exercise is the conversation. 

Whatever we do with technology, it is always the conversation that counts!