Thursday, October 01, 2015

Donald Clark: Algorithmic/Adaptive Learning #DevLearn

These are my live blogged notes from the opening session at this year's DevLearn, hosted by the eLearning Guild and happening in Las Vegas. Forgive any typos and incoherencies.

Donald Clark @donaldclark “Learning Prediction for 2015 and Beyond: Two Small Letters”

Algorithms and predictive analytics – Netflix knows what you want to watch. It’s time to do the same for learning.

Learning styles don’t exist. Don’t build your adaptive learning system based on them. They have no scientific validity. Same with Myers Brigg.

Aggregate data let the machine algorithms kick in so the data gets better and better.

Learning to Adapt – Adaptive Learning – this is what the Gates Foundation has pumped a lot  of money into this area.

Create a network of learning objects – adaptive sets you free from A to Z. It understand you and finds the best way through. Adaptive learning gets you back on course – it spots your misconceptions. It knows you’ve failed and tries to identify why you’ve failed.

In the real world using Google – we’re in charge. Unlike in eLearning.

University of Edinburg is piloting this for courses and now rolling out to even more.
ASU is doing massive adaptive learning courses with American History and Biology -- 101 courses.

Good adaptive platforms that can understand the structure  and network of American History objects – that’s good.

Ask questions, ask your confidence level as you answer those questions – that helps with predictive.

Hitachi Data Systems – let’s scrap courses and instead have one big knowledge base with lots of competencies. The future for this company isn’t the traditional L&D course – instead it’s competencies that every learner vectors through in different ways. The algorithms keep an eye on what’s next.

Look at – highly algorithmic.

Could algorithms create elearning from scratch? 

Roger Schank’s PhD systems went

Journalism: algorithms more powerful than news editors. The algorithms are smarter than the journalists – they have data back for 100s of years.

WiQi -- -- cloud based system – you type in some content.  It creates some eLearning with a semantic engine. All open response questions – not MCQs which are just about picking from a list.  ( You can put your compliance documents into this – This works right now and no cost.  It pulls up copyright free images from Wikipedia – you can do it with any text document you want.

(Check Donald's slides for a link to WiQi)

Automated Essay Marking.

Spaced Practice
Ebbinghaus – we don’t learn a damn thing without repeated practice.  Every student has  a mobile phone – you have an umbilical cord for spaced practice.

With spaced practice – do lots of repetition in the first few weeks and then you can scale that down.

Interleave: things we get right are called knowns; those we get wrong are unknowns. But we forget everything, so we have to reinforce both the knowns and unknowns and the half knowns.  If we want automaticity – we have to interleave the knowledge going forward.

Hopping: hoping between levels from easy, medium, hard

Cognitive spread – items spread on next few days to smooth out workload…if you miss a day of work, then it spreads that out over the next few days, it doesn’t just shove your mixed work into the next day.

9 things algorithms do that teachers can't:
  •       ignore gender, race, social background
  •       free from cognitive biases
  •       never get tired, ill…
  •       do things that brains cannot
  •       personalize learning
  •       personal reporting
  •       prevent failure and drop-out
  •       automatically improve courses
  •       scale

AI Robots

Bloom wrote a paper showing that 1-1 tutorial provided massive results – it’s incredibly powerful. What if we can use that fact to embody that with a robot…

Introducing NAO – working with autistic children using robots.

Teachers find it really hard to do formative assessment -- computers do formative really well.

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