Online Research Tools and Techniques. (2020, September 16). https://www.youtube.com/watch?v=wGWqBtDkOFs

Perrott, D. (2020, May 26). Is Applied Behavioural Science reaching a Local Maximum? Medium. https://medium.com/@DavePerrott/is-applied-behavioural-science-reaching-a-local-maximum-538b536f7e7d

Data Intersections 2020 | Heather Krause. (2020, February 21). How not to use data like a racist. https://www.youtube.com/watch?v=EGO7yevPHDk

Why Most Advertisers Can’t Afford to Boycott Facebook. (2020, July 8). Pro Market. https://promarket.org/2020/07/08/why-most-advertisers-cant-afford-to-boycott-facebook/

Carrying out qualitative research under lockdown – Practical and ethical considerations. (2020, April 20). Impact of Social Sciences. https://blogs.lse.ac.uk/impactofsocialsciences/2020/04/20/carrying-out-qualitative-research-under-lockdown-practical-and-ethical-considerations/

The Ethics of Online Research. (2018, August 30). Leeds University Library Blog. https://leedsunilibrary.wordpress.com/2018/08/30/the-ethics-of-online-research/

Like the way unethical people use statistics in general? This should be a concern, especially as government data, long considered the gold standard of data, undergoes attacks that would skew the data toward political ends. (see the census 2020)

Primary problems to resolve regarding the use of "big data" in humanitarian contexts: dataset size/sample, predictive analytics are contrary to human behavior, and ethical abuses of PII.

We have hardly begun to engineer data ethincs approriate for our extraordinary information carnival.

Kudos for listing the identifier. I googled that, which brought me to http://researchsupport.admin.ox.ac.uk/governance/ethics/committees/oxtrec , and under "Approved studies 2016", I found the entry

593-16 Elizabeth Pisani What makes data sharing work? WWARN case study Minimal Risk N/A 16/5/16

which is way more transparent than most ethics statements in published papers.

Also kudos to the Central University Research Ethics Committee (CUREC) for the decision highlighted under the "Approved studies 2016" headline:

It was agreed by CUREC in early 2016 that, in the interest of transparency, OxTREC should make publicly available a list of studies that it has approved. The list below sets out those studies that have been approved by OxTREC since January 2016.

Very important to keep in mind. Not only do we realise that re-identification is a risk, but this risk is exacerbated by the increase in “triangulation”. Hence some discussions about Differential Privacy.

Useful reminder. Sounds a bit like “now that we have easier access to data, we have to be particularly careful”. Probably not the first reflex of most researchers before they start sending forms to their IRBs. Important for this to be explicitly designated as a concern, in IRBs.

Again, this is probably a more felicitous wording than “privacy protection”. Sure, it takes as a given that some use of data is desirable. And the preceding section makes it sound like Learning Analytics advocates mostly need ammun… arguments to push their agenda. Still, the notion that we want to advocate for responsible use is more likely to find common ground than this notion that there’s a “data faucet” that should be switched on or off depending on certain stakeholders’ needs. After all, there exists a set of data use practices which are either uncontroversial or, at least, accepted as “par for the course” (no pun intended). For instance, we probably all assume that a registrar should receive the grade data needed to grant degrees and we understand that such data would come from other sources (say, a learning management system or a student information system).

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.