Is this meaningful data? Are “test scores” or “grades” meaningful units of measurement, for example? What can we truly know based on this data? Are our measurements accurate? Is our analysis, based on the data that we’ve collected, accurate? What sorts of assumptions are we making when we collect and analyze this data? Assumptions about bodies, for example. Assumptions about what to count. Assumptions and value judgments about “learning”. How much is science, and how much is marketing? Whose data is this? Who owns it? Who controls it? Who gets to see it? Is this data shared or sold? Is there informed consent? Are people being compelled to surrender their data? Are people being profiled based on this data? Are decisions being made about them based on this data? Are those decisions transparent? Are they done via algorithms – predictive modeling, for example, that tries to determine some future behavior based on past signals? Who designs the algorithms? What sorts of biases do these algorithms encode? How does the collection and analysis of data shape behavior? Does it incentivize certain activities and discourage others? Who decides what behaviors constitute “a good student” or “a good teacher” or “a good education”? source
Continuing this conversation, Jim Groom suggests that the key question is:
How do we get anyone to not only acknowledge this process of extraction and monetization (because I think folks have), but to actually feel empowered enough to even care source
Speaking about assemblages, Ian Guest posits that:
When data is viewed in different ways, with different machines, different knowledge may be produced. source
Benjamin Doxtdater makes the link between power and data:
The operation of power continues to evolve when Fitbits and Facebook track our data points, much like a schoolmaster tracks our attendance and grades.source
Kin Lane provides the cautionary tale of privacy and security violations via APIs, in which he suggests:
Make sure we are asking the hard questions about the security and privacy of data and content we are running through machine learning APIs. Make sure we are thinking deeply about what data and content sets we are running through the machine learning APIs, and reducing any unnecessary exposure of personal data, content, and media.source
Emily Talmage questions the intent behind platform economy and the desire for correlations that detach values from the human face:
For whatever reason – maybe because they are too far away from actual children – investors and their policy-makers don’t seem to see the wickedness of reducing a human child in all his wonder and complexity to a matrix of skills, each rated 1, 2, 3 or 4. [source}(https://emilytalmage.com/2017/07/31/how-data-is-destroying-our-schools/)
Yael Grauer documents how researches at Yale Privacy Lab and French nonprofit Exodus Privacy have uncovered the proliferation of tracking software on smartphones, finding that weather, flashlight, ride-sharing, and dating apps, among others, are infested with dozens of different types of trackers collecting vast amounts of information to better target advertising.
“The real question for the companies is, what is their motivation for having multiple trackers?” asked O’Brien.source
Ben Williamson collects together a number of critical questions when addressing big data in education:
How is ‘big data’ being conceptualized in relation to education?
What theories of learning underpin big data-driven educational technologies?
How are machine learning systems used in education being ‘trained’ and ‘taught’?
Who ‘owns’ educational big data?
Who can ‘afford’ educational big data?
Can educational big data provide a real-time alternative to temporally discrete assessment techniques and bureaucratic policymaking?
Is there algorithmic accountability to educational analytics?
Is student data replacing student voice?
Do teachers need ‘data literacy’?
What ethical frameworks are required for educational big data analysis and data science studies?source
Discussing personal data, Kim Jaxon asked her students to consider the platforms they frequent:
I invited our class to look closely at Google, Facebook, Snapchat, Blackboard Learn, TurnItIn, and many other platforms they frequent or are asked to use, and to think critically about the collection and control of their data. Borrowing from Morris and Stommel’s work, we are asking: Who collects data? Who owns it? What do they do with it? Who profits or benefits? What is left out of the results: what is hidden?