#oeb_midsummit Cognitive neuroscience and learning by @BekkeringHarold

Harold Bekkering reminds me of some of my Dutch family, funny feeling. Once he took the stage, it really was like listening to cousin Folkert.

Everything is connected in the brain, and the brain is a predictive machine.

A neuron at work is incoming data is output in just one unit. So in neuroscience we see that multiple inputs triggers a process into one output.

Hebbian learning: everything is connected (Hebb’ theory).

A human can adapt to one specific tone of voice and timbre. Our brain adjusts.

The brain is a prediction machine. Read Karl Friston and Andy Clark.

Distributed knowledge in the brain. Exteroceptive, autonomic (interoceptive), motoric (proprioceptive) given the conceptual multimodel representations. The brain tries to make the best things for you. It is a multimodal representations, which makes it of interest.

The whole brain is summed up in error correction.

E.g. you walk to a door (prediction is going through the door), but if a door is closed you adjust your action based upon the perception after the prediction.

Amal and Giraud, TICS 2012, beta and gamma power. The brain is only active when you make errors. Only after making an error your model is updated [interesting].

We learn more from negative feedback. Oh no! 😃

Creating a safe environment to learn from errors.

Social learning: humans are by nature social, homo imitans.

Dunbar (1998) The social Cortex, it is big because we have huge networks. The Dunbar’s number.

Social learning is needed due to the brain needing to be matured over time (baby to adult).

Good tip for learning: saying: hi (insert name) and then say the action. The brain does not ignore the calling of a name to become active.

Mirror neurons: have revolutionised neuroscience. Motorcortex was investigated, and in the lab, monkeys mimicked humans picking up peanuts. There are cells that only get active if we see someone else moving. Your brain cannot help to observe and react.

Learning analytics: extracting data, predicting data, … data driven education.

It is not useful to only learn in one learning style is the real point of interest, if you want to really learn the best thing to do is to use all styles for learning.  

Lessons for online learning

Hebbian learning: be careful with what you offer

Predictive learning: structure and provoking errors (job aids similar)

Social learning

#LearningTechDay in Belgium, come and join experts #EdTech #elearning

One of the highlights for Professional eLearning is coming up next month. On Tuesday 20 June 2017, Mathias Vermeulen (CEO of Winston Wolfe) organizes the Belgian LearningTechDay an annual event that always seems to trigger multiple ideas.

Location: Zebra in Ghent, Belgium. Zebra is a unique project that combines housing, culture and economy all in one. And it has cheap parking space.

This is not just any other eLearning opportunity. If you are in the neighborhood and you are interested in eLearning, this is the place to be! The three keynotes alone will make it worth your while: Stephen Downes (integrating MOOCs in corporations), Nell Watson (Artificial Intelligence in all aspects of learning, living and society), and Jamie Good (Neuroscience, habit-building and technology).

Although some of the break-out sessions are in Dutch, the keynotes are in English, and I am pretty sure everyone participating speaks English.

I will also share a presentation, specifically on the Instructional Design Variation Matrix, an ongoing work which looks at parameters that are often opposite to each other, and bridging these opposites with possible online learning steps befitting diverse instructional design demands. This is slowly but surely resulting in a manual that can be used as a job aid to quickly look for options to implement diverse learning purposes, providing solutions for your own specific learning environment or tailored to your own target population (e.g. master students, informal learners, professional learners).

To give an idea, the Instructional Design Variation Matrix lists an array of actions that can be taken to address different learning parameters (individual versus sociaal learning, memorizing content versus challenge-based education, standardized learning versus contextualized learning, just-in-time performance versus long-term implementation, …). Because the designs are linked to different (intended) learning objectives, it enables the online material to be used in a variety of learning trajectories.