- May 2023
A booklet prepared for teachers that introduces key concepts from the Science of Learning (i.e. cognitive neuroscience). The digital booklet is the result of a European project. Its content have been compiled from continuing professional development workshops for teachers and features evidence-based teaching practices that align with our knowledge of the Science of Learning.
- Jun 2022
One of my frustrations with the “science of learning” is that to design experiments which have reasonable limits on the variables and can be quantitatively measured results in scenarios that seem divorced from the actual experience of learning.
Is the sample size of learning experiments really large enough to account for the differences in potential neurodiversity?
How well do these do for simple lectures which don't add mnemonic design of some sort? How to peel back the subtle differences in presentation, dynamism, design of material, in contrast to neurodiversities?
What are the list of known differences? How well have they been studied across presenters and modalities?
What about methods which require active modality shifts versus the simple watch and regurgitate model mentioned in watching videos. Do people do actively better if they're forced to take notes that cause modality shifts and sensemaking?
- Dec 2021
Maxwell's advice was to read the four parts of the Treatise in parallel rather than in sequence.
reading the texts in parallel.
- Sep 2020
Wilkinson, Jack, Kellyn F. Arnold, Eleanor J. Murray, Maarten van Smeden, Kareem Carr, Rachel Sippy, Marc de Kamps, et al. ‘Time to Reality Check the Promises of Machine Learning-Powered Precision Medicine’. The Lancet Digital Health 0, no. 0 (16 September 2020). https://doi.org/10.1016/S2589-7500(20)30200-4.
- electronic health database
- clinical practice
- clinical science
- personalised medical approach
- machine learning
- algorithmic complexity
- prediction of individual responses
- machine learning powered precision medicine
- improved diagnosis
- May 2020
Pinto, S. F., & Ferreira, R. S. (2020). Analyzing course programmes using complex networks. ArXiv:2005.00906 [Physics]. http://arxiv.org/abs/2005.00906
- accumulation of knowledge
- complex network
- course program
- statistical physics
- Sep 2017
out of 878 potentially relevant studies published between 1992 and 2017, only 36 directly compared reading in digital and in print and measured learning in a reliable way. (Many of the other studies zoomed in on aspects of e-reading, such as eye movements or the merits of different kinds of screens.)