23 Matching Annotations
  1. Sep 2024
    1. I emphatically disagree with BlueFish about observers being difficult to properly unit test. This is precisely the biggest point that distinguishes them from lifecycle callbacks: you can test observers in isolation, and doing so discourages you from falling into many of the state- and order-heavy design pitfalls BlueFish refers to (which again I think is more often true of lifecycle callbacks).
  2. Aug 2023
  3. Aug 2022
  4. Nov 2021
    1. In effect, the $ syntax we've seen above will actually setup a subscription to the store. And the subscription will be cancelled when the component is destroyed. If the store is subscribed by multiple components, the store will be disposed only when the last component unsubscribes (it will be reinitialized, if a new subscription is made). This is very handy to manage the lifecycle of disposable things in your code.
  5. Mar 2021
  6. Jan 2021
  7. Oct 2020
  8. Sep 2020
  9. Aug 2020
  10. Nov 2018
    1. Towards teaching as design: Exploring the interplay between full-lifecycle learning design tooling and Teacher Professional Development.

      This article explores the theory of training teachers as learning designers to promote innovate and creativity. Included in the article are studies of designers with little teaching experience compared with those that are full-cycle teachers and the effect of TPD and LD upon training.

      RATING: 5/5 (rating based upon a score system 1 to 5, 1= lowest 5=highest in terms of content, veracity, easiness of use etc.)

  11. Jan 2014
    1. The Data Life Cycle: An Overview The data life cycle has eight components: Plan : description of the data that will be compiled, and how the data will be managed and made accessible throughout its lifetime Collect : observations are made either by hand or with sensors or other instruments and the data are placed a into digital form Assure : the quality of the data are assured through checks and inspections Describe : data are accurately and thoroughly described using the appropriate metadata standards Preserve : data are submitted to an appropriate long-term archive (i.e. data center ) Discover : potentially useful data are located and obtained, along with the relevant information about the data ( metadata ) Integrate : data from disparate sources are combined to form one homogeneous set of data that can be readily analyzed Analyze : data are analyzed

      The lifecycle according to who? This 8-component description is from the point of view of only the people who obsessively think about this "problem".

      Ask a researcher and I think you'll hear that lifecycle means something like:

      collect -> analyze -> publish
      

      or a more complex data management plan might be:

      ask someone -> receive data in email -> analyze -> cite -> publish -> tenure
      

      To most people lifecycle means "while I am using the data" and archiving means "my storage guy makes backups occasionally".

      Asking people to be aware of the whole cycle outlined here is a non-starter, but I think there is another approach to achieve what we want... dramatic pause [to be continued]

      What parts of this cycle should the individual be responsible for vs which parts are places where help is needed from the institution?