- Feb 2022
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twitter.com twitter.com
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Claudia Sahm. (2022, January 5). “We, as experts, have a responsibility to policymakers and everyday people to match the strength of our recommendations to the strength of our data. When I read Oster, I see a tone and conviction that far exceeds the many limitations of her data.” https://t.co/NqWwj0hi28 [Tweet]. @Claudia_Sahm. https://twitter.com/Claudia_Sahm/status/1478532000441151488
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- Jan 2022
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Fischer, O., Jeitziner, L., & Wulff, D. U. (2021). Affect in science communication: A data-driven analysis of TED talks. PsyArXiv. https://doi.org/10.31234/osf.io/28yc5
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- Feb 2021
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www.nature.com www.nature.com
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Chande, A., Lee, S., Harris, M., Nguyen, Q., Beckett, S. J., Hilley, T., Andris, C., & Weitz, J. S. (2020). Real-time, interactive website for US-county-level COVID-19 event risk assessment. Nature Human Behaviour, 4(12), 1313–1319. https://doi.org/10.1038/s41562-020-01000-9
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trailblazer.to trailblazer.to
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What this means is: I better refrain from writing a new book and we rather focus on more and better docs.
I'm glad. I didn't like that the book (which is essentially a form of documentation/tutorial) was proprietary.
I think it's better to make documentation and tutorials be community-driven free content
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- Jul 2020
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Fitzgerald, R. M. (2020). WAKING TO NORMAL: Examining Archival Appraisal in Data-driven Society [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/2befk
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- Sep 2016
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www.chronicle.com www.chronicle.com
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the use of data in scholarly research about student learning; the use of data in systems like the admissions process or predictive-analytics programs that colleges use to spot students who should be referred to an academic counselor; and the ways colleges should treat nontraditional transcript data, alternative credentials, and other forms of documentation about students’ activities, such as badges, that recognize them for nonacademic skills.
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.
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The importance of models may need to be underscored in this age of “big data” and “data mining”. Data, no matter how big, can only tell you what happened in the past. Unless you’re a historian, you actually care about the future — what will happen, what could happen, what would happen if you did this or that. Exploring these questions will always require models. Let’s get over “big data” — it’s time for “big modeling”.
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- Dec 2015
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mfeldstein.com mfeldstein.com
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increased investment in professional development and teaching-friendly tenure and promotion practices
Even those who adopt a taylorist model to education may understand that “it takes money to save money”.
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