Monthly Archives: December 2019

Blog of the Week: 13 December 2019 – Modelling modelling: into the classroom with live drawing

A Chemical Orthodoxy

This post comes unashamedly on the tails of Pritesh Raichura’s excellent series on teacher explanation which you can read here. I’ve written recently on dual coding and the multimedia effect because, like Pritesh, I’m worried that dual coding is in danger of lethally mutating beyond its evidence base. For me, dual coding is a process that is best used when explaining difficult material, not when making jazzy posters or the like. To summarise my previous article:

  • The working memory includes two channels: verbal (language) and visual (things you see that don’t have anything to do with language)
  • Utilising both increases the capacity of working memory and allows for a greater number of entities to be processed at once
  • This is called dual coding
  • When dual coding is carried out effectively, there is a boost to processing and learning, known as the multimedia effect

Teachers have been using diagrams to…

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Blog of the Week: 6 December 2019 – Deep learning (2): structuring and organising knowledge – responsive teaching update

What comes next?

  1. James I, Charles, I, Charles II, James II, _________
  2. 2, 3, 5, 7, __
  3. Je suis allé, je vais, j’irai; j’ai été, je suis, _________
  4. A B A B C D C D E F E F _ _
  5. Helium, Neon, Argon, Krypton, _______

Some of these clues may feel frustratingly cryptic: readers know A, 2, and perhaps ‘je suis’; they know about neon lights, and probably something about Charles I.  Answering depends not just on what we know however, but on how it’s organised: the answer seems obvious once we recognise the structure (answers below).  Deep learning means developing mental models – organised knowledge structures – which allow students to apply their knowledge flexibly: this post discusses the architecture of mental models; what structures and organisation make knowledge usable?

Mental models: organising knowledge usably

Flexible knowledge is an important step towards deep learning.  If an item of knowledge is flexible, students can access it via a range of cues, not just the ones they originally learned.  So – for example – flexible knowledge of Charles I would allow them to think about him when asked about the Stuarts, Ship Money, or the Divine Right of Kings (more on this here).  This flexibility supports transfer of knowledge to …

 

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