52 Matching Annotations
  1. May 2021
  2. Apr 2021
  3. Mar 2021
  4. Dec 2020
    1. ReconfigBehSci @SciBeh (2020) For those who might think this issue isn't settled yet, the piece include below has further graphs indicating just how much "protecting the economy" is associated with "keeping the virus under control" Twitter. Retrieved from: https://twitter.com/i/web/status/1306216113722871808

  5. Sep 2020
  6. Aug 2020
    1. Bartik, A. W., Cullen, Z. B., Glaeser, E. L., Luca, M., Stanton, C. T., & Sunderam, A. (2020). The Targeting and Impact of Paycheck Protection Program Loans to Small Businesses (Working Paper No. 27623; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27623

  7. Jul 2020
  8. Jun 2020
  9. May 2020
  10. Apr 2020
  11. Mar 2017
    1. 沒有看到許委員的『數位經濟基本法』原始草案全文。在討論過程中有一些問題,例如數位經濟的基本定義;對資料產業沒有處理,以至於詹先生對國家保存資料的想像可能低估技術、行政程序,導致不恰當地反映在資料保存相關條文等處,不曉得是否有修正與否。

  12. Sep 2016
    1. the automatic collection of students’ data through interactions with educational technologies as a part of their established and expected learning experiences raises new questions about the timing and content of student consent that were not relevant when such data collection required special procedures that extended beyond students’ regular educational experiences of students

      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.

    1. 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.

  13. Dec 2015
    1. Users publish coursework, build portfolios or tinker with personal projects, for example.

      Useful examples. Could imagine something like Wikity, FedWiki, or other forms of content federation to work through this in a much-needed upgrade from the “Personal Home Pages” of the early Web. Do see some connections to Sandstorm and the new WordPress interface (which, despite being targeted at WordPress.com users, also works on self-hosted WordPress installs). Some of it could also be about the longstanding dream of “keeping our content” in social media. Yes, as in the reverse from Facebook. Multiple solutions exist to do exports and backups. But it can be so much more than that and it’s so much more important in educational contexts.

  14. Nov 2015