37 Matching Annotations
  1. Aug 2022
  2. Aug 2021
    1. Rogers, J. P., Watson, C. J., Badenoch, J., Cross, B., Butler, M., Song, J., Hafeez, D., Morrin, H., Rengasamy, E. R., Thomas, L., Ralovska, S., Smakowski, A., Sundaram, R. D., Hunt, C. K., Lim, M. F., Aniwattanapong, D., Singh, V., Hussain, Z., Chakraborty, S., … Rooney, A. G. (2021). Neurology and neuropsychiatry of COVID-19: A systematic review and meta-analysis of the early literature reveals frequent CNS manifestations and key emerging narratives. Journal of Neurology, Neurosurgery & Psychiatry, jnnp-2021-326405. https://doi.org/10.1136/jnnp-2021-326405

  3. Jul 2021
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  7. Feb 2021
  8. Jan 2021
    1. Weingarten. E., Chen. Q., McAdams., Yi. J., (2016). From Primed Concepts to Action: A Meta-Analysis of the BehavioralEffects of Incidentally Presented Words. Psychological Bulletin 2016 (142) pp 472-497.

  9. Nov 2020
  10. Oct 2020
  11. Sep 2020
    1. Siemieniuk, R. A., Bartoszko, J. J., Ge, L., Zeraatkar, D., Izcovich, A., Kum, E., Pardo-Hernandez, H., Rochwerg, B., Lamontagne, F., Han, M. A., Liu, Q., Agarwal, A., Agoritsas, T., Chu, D. K., Couban, R., Darzi, A., Devji, T., Fang, B., Fang, C., … Brignardello-Petersen, R. (2020). Drug treatments for covid-19: Living systematic review and network meta-analysis. BMJ, 370. https://doi.org/10.1136/bmj.m2980

  12. Aug 2020
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  16. Aug 2019
    1. Moreover, annotation is the agreed upon means of starting and sustaining that conversation.

      With this text appearing on bookbook.pubpub.org being an excellent example of just this. #meta

      I'm sort of hoping for some discussion of Kathleen Fitzpatrick's process behind her book Planned Obsolescence which was released in draft form for open peer review in fall 2009, much like Annotations. It's the first example I can think of a scholar doing something like this digitally in public, though there may have been other earlier examples.

  17. Sep 2016
    1. Research: Student data are used to conduct empirical studies designed primarily to advance knowledge in the field, though with the potential to influence institutional practices and interventions. Application: Student data are used to inform changes in institutional practices, programs, or policies, in order to improve student learning and support. Representation: Student data are used to report on the educational experiences and achievements of students to internal and external audiences, in ways that are more extensive and nuanced than the traditional transcript.

      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.