Freedom to run software that I’ve paid for on any device I want without hardware dongles or persistent online verification schemes. Freedom from the prying eyes of government and corporations. Freedom to move my data from one application to another. Freedom to move an application from one hosting provider to another. Freedom from contracts that lock me in to expensive monthly or annual plans. Freedom from terms and conditions that offer a binary “my way or the highway” decision.
Audrey Watters writes down a series of questions to consider when thinking about data:
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:
The real kicker 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)
“Being a PYP teacher… a good PYP teacher, demands that you put in the thought, that you deliberate over purpose and meaning – either alone or with your colleagues – and that you continuously reflect on what you and your students are doing" https://timespaceeducation.wordpress.com/2017/06/27/semantics-is-not-a-bad-word/
A description of Gramsci's notion of the organic intellectual from David Sessions:
Gramsci’s conception of the organic intellectual was not merely meant to describe the prophets of the European bourgeoisie and its industrial capitalism. The organic intellectual was above all a concept for the left: a name for those who, emerging from working-class conditions, had the inclination and ability to express their vision of society and organize it into action. He envisioned not a savior swooping down from the elite, but thinkers sharing an experience of economic privation, translated into both an intellectual and social struggle. (Source)
Unlike the public intellectual, whose position is built over time, the thought leader breaks through and disrupts with a single minded focus. As David Sessions explains:
The true role of the thought leader is to serve as the organic intellectual of the one percent—the figure who, as Gramsci put it, gives the emerging class “an awareness of its own function” in society. The purpose of the thought leader is to mirror, systematize, and popularize the delusions of the superrich: that they have earned their fortunes on merit, that social protections need to be further eviscerated to make everyone more flexible for “the future,” and that local attachments and alternative ways of living should be replaced by an aspirational consumerism. The thought leader aggregates these fundamental convictions into a great humanitarian mission. Every problem, he prophesies, can be solved with technology and rich people’s money, if we will only get our traditions, communities, and democratic norms out of the way. (Source)
We talk about digital citizenship, but taking this a step further, is the idea of CivicTech.
Grodeska uses the term “CivicTech” and I think there is a fair amount of overlap between “Civic Imagination” (the idea of imagining a better future and then taking steps to make it happen) and “CivicTech” (which is the idea of making sure we use digital tools wisely and with agency to affect change in the world.) Source
It is often stated that code is the new literacy of the 21st century. One of the problems with this is that code and is not the same as literature. As Peter Seibel describes:
Code is not literature. We don’t read code, we decode it. We examine it. A piece of code is not literature; it is a specimenSource
It is interesting to considering this in lieu of Doug Belshaw's eight elements of digital literacies.
There is often power and purpose in the words that we use to describe the world we inhabit. One as area of importance is the description of classrooms and learning spaces:
To call a space a ‘learning space’ is not just aspirational, it is a descriptive and ontological claim – hard to evidence, hard to know who is learning what, in what ways. A ‘teaching space’ is also descriptive (and more dull, less engaging, old-fashioned perhaps) but it is more modest, more honest, more accurate and more verifiable. It is also narrower: if everywhere is in some way a learning space, then calling a particualar space a ‘learning space’ adds nothing.https://architectureandeducation.org/2016/09/12/the-changing-vocabulary-of-education-and-its-spaces/
Personalised learning is one of those terms that everyone seems to know and agree with, but when we dig down, realise there are many differing interpretations of it. Here then is an except from Audrey Watters which captures some of the nuances:
“Personalized learning” can mean that students “move at their own pace” through lessons and assignments, for example, unlike those classrooms where everyone is expected to move through material together. (In an invented history of education, this has been the instructional arrangement for all of history.) Or “personalized learning” can mean that students have a say in what they learn – students determine topics they study and activities they undertake. “Personalized learning,” according to some definitions, is driven by students’ own interests and inquiry rather than by the demands or standards imposed by the instructor, the school, the state. “Personalized learning,” according to other definitions, is driven by students’ varied abilities or needs; it’s a way of navigating the requirements of school bureaucracies and requesting appropriate accommodations – “individualized education plans” and the like. Or “personalized learning” is the latest and greatest – some new endeavor that will be achieved, not through human attention or agency or through paperwork or policy but through computing technologies. That is, through monitoring and feedback, through automated assessment, and through the programmatic presentation of new or next materials to study. (Source)
One of the biggest changes to education in the last few years has been the emphasis on feedback. The challenge then becomes how to be equitable with time and energy. The solution, algorithms.
"DreamBox Learning tracks a student’s every click, correct answer, hesitation and error — collecting about 50,000 data points per student per hour — and uses those details to adjust the math lessons it shows. And it uses data to help teachers pinpoint which math concepts students may be struggling with.
Mr. Hastings described DreamBox as a tool teachers could use to gain greater insights into their students, much the way that physicians use medical scans to treat individual patients. “A doctor without an X-ray machine is not as good a doctor,” Mr. Hastings said."Source
Using various applications to provide automated feedback forces the question as to what education should involve and be about.
Biesta and the Learnification