I would think that this would be one of the easiest things to measure and also would provide significant and useful data. We should check in with Brian (?) to see what data is currently being tracked.

3.1 Guest Lecture: Lauren Klein » Q&A on "What is Feminist Data Science?"<br /> https://www.complexityexplorer.org/courses/162-foundations-applications-of-humanities-analytics/segments/15631

https://www.youtube.com/watch?v=c7HmG5b87B8

Theories of Power

Patricia Hill Collins' matrix of domination - no hierarchy, thus the matrix format

What are other broad theories of power? are there schools?

Relationship to Mary Parker Follett's work?

Bright, Liam Kofi, Daniel Malinsky, and Morgan Thompson. “Causally Interpreting Intersectionality Theory.” Philosophy of Science 83, no. 1 (January 2016): 60–81. https://doi.org/10.1086/684173.

about Bayesian modeling for intersectionality

Where is Foucault in all this? Klein may have references, as I've not got the context.

How do words index action? —Laura Klein

The power to shape discourse and choose words - relationship to soft power - linguistic memes

Color Conventions Project

20:15 Word embeddings as a method within her research

General result (outside of the proximal research) discussed: women are more likely to change language... references for this?

[[academic research skills]]: It's important to be aware of the current discussions within one's field. (LK)

36:36 quantitative imperialism is not the goal of humanities analytics, lived experiences are incredibly important as well. (DK)

Szabelska, A., Pollet, T. V., Dujols, O., Klein, R. A., & IJzerman, H. (2021). A Tutorial for Exploratory Research: An Eight-Step Approach. PsyArXiv. https://doi.org/10.31234/osf.io/cy9mz

Rahman, M., Ali, G. G. M. N., Li, X. J., Paul, K. C., & Chong, P. H. J. (2020). Twitter and Census Data Analytics to Explore Socioeconomic Factors for Post-COVID-19 Reopening Sentiment [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/fz4ry

Making the case for the need for digitally native research methodologies.

In the current universe, Twitter also makes a number of predictions about users that could be used as proxy variables for economic and cultural characteristics. It can display things like your audience's net worth as well as indicators commonly linked to political orientation. Triangulating some of this data could allow for other forms of intended or unintended discrimination.

I've already been able to view a wide range (possibly spurious) information about my own reading audience through these analytics. On September 9th, 2019, I started a Twitter account for my 19th Century Open Pedagogy project and began serializing installments of critical edition, The Woman in White: Grangerized. The @OPP19c Twitter account has 62 followers as of September 17th.

Having followers means I have access to an audience analytics toolbar. Some of the account's followers are nineteenth-century studies or pedagogy organizations rather than individuals. Twitter tracks each account as an individual, however, and I was surprised to see some of the demographics Twitter broke them down into. (If you're one of these followers: thank you and sorry. I find this data a bit uncomfortable.)

Within this dashboard, I have a "Consumer Buying Styles" display that identifies categories such as "quick and easy" "ethnic explorers" "value conscious" and "weight conscious." These categories strike me as equal parts confusing and problematic: (Link to image expansion)

I have a "Marital Status" toolbar alleging that 52% of my audience is married and 49% single.

I also have a "Home Ownership" chart. (I'm presuming that the Elizabeth Gaskell House Museum's Twitter is counted as an owner...)

....and more

Wow. Not that we need such an extreme case to shed light on the perverse incentives at stake in Learning Analytics, but this surely made readers react. On the other hand, there’s a lot more to be said about retention policies. People often act as though they were essential to learning. Retention is important to the institution but are we treating drop-outs as escapees? One learner in my class (whose major is criminology) was describing the similarities between schools and prisons. It can be hard to dissipate this notion when leaving an institution is perceived as a big failure of that institution. (Plus, Learning Analytics can really feel like the Panopticon.) Some comments about drop-outs make it sound like they got no learning done. Meanwhile, some entrepreneurs are encouraging students to leave institutions or to not enroll in the first place. Going back to that important question by @sarahfr: why do people go to university?

this is just so awful.

Have yet to notice much discussion of the relationships between SIS (Student Information Systems), CRM (Customer Relationship Management), ERP (Enterprise Resource Planning), and LMS (Learning Management Systems).

In other words, Google does use everyone’s information (Data as New Oil) and can use such things to target ads in Higher Education.

Surprising mention of “open-source platforms”, here. Doesn’t sound like these issues are absent from proprietary platforms. Maybe they mean non-institutional platforms (say, social media), where these issues are really pressing. But the wording is quite strange if that is the case.

Really? These aren’t uncontroversial, to say the least. For instance, discussion board interactions often call for careful, mixed-method work with an eye to preventing instructor effect and confirmation bias. “Time on task” is almost a codeword for distinctions between models of learning. Research in cognitive science gives very nuanced value to “time spent on task” while the Malcolm Gladwells of the world usurp some research results. A major insight behind Competency-Based Education is that it can allow for some variance in terms of “time on task”. So it’s kind of surprising that this summary puts those two things to the fore.

Ha! The Chronicle’s summary framed these categories somewhat differently. Interesting. To me, the “application” part is really about student retention. But maybe that’s a bit of a cynical reading, based on an over-emphasis in the Learning Analytics sphere towards teleological, linear, and insular models of learning. Then, the “representation” part sounds closer to UDL than to learner-driven microcredentials. Both approaches are really interesting and chances are that the report brings them together. Finally, the Chronicle made it sound as though the research implied here were less directed. The mention that it has “the potential to influence institutional practices and interventions” may be strategic, as applied research meant to influence “decision-makers” is more likely to sway them than the type of exploratory research we so badly need.

Useful breakdown. Research, predictive models, and recognition are quite distinct from one another and the approaches to data that they imply are quite different. In a way, the “personalized learning” model at the core of the second topic is close to the Big Data attitude (collect all the things and sense will come through eventually) with corresponding ethical problems. Through projects vary greatly, research has a much more solid base in both ethics and epistemology than the kind of Big Data approach used by technocentric outlets. The part about recognition, though, opens the most interesting door. Microcredentials and badges are a part of a broader picture. The data shared in those cases need not be so comprehensive and learners have a lot of agency in the matter. In fact, when then-Ashoka Charles Tsai interviewed Mozilla executive director Mark Surman about badges, the message was quite clear: badges are a way to rethink education as a learner-driven “create your own path” adventure. The contrast between the three models reveals a lot. From the abstract world of research, to the top-down models of Minority Report-style predictive educating, all the way to a form of heutagogy. Lots to chew on.

Interestingly enough, a lot of teachers either don’t know that such data might be available or perceive very little value in monitoring learners in such a way. But a lot of this can be negotiated with learners themselves.

Neat use of TAGS Explorer. Did do so, to interesting effects. Regular livetweet events may be more interesting. And there’s something to be said about NodeXL and COSMOS.

And we need an ever-larger data set, right?

As usual, @AudreyWatters puts things in proper perspective.

Even those who adopt a taylorist model to education may understand that “it takes money to save money”.