#CogSciSci is a grassroots collective of science teachers who are interested in promoting the use of Cognitive Science in the teaching of science. No one really remembers exactly how it started and no one is “in charge.” Generally, we just aim to support each other, steal ideas, and become better teachers. We get a lot of people signing up so this blog is just a simple explainer about who we are and what we stand for.
- Cognitive Science
- Mailing group
- People to follow on Twitter
1. Cognitive Science
Cognitive science (CS) is the study of thought and memory. It is a branch within psychology that seeks to understand how people’s minds work at a level distinct from neuroscience. It isn’t really interested in physical brain structures and neurons and suchlike; it’s more interested in the emergent properties of that physical structure, how we can conceptualise and utilise its processes.
For a really straightforward introduction to CS from an educational perspective, you should read:
For more “hands-on” applications, check out:
- Rosenshine’s Principles of Instruction
- Clark, Kirschner and Sweller: The Case for Fully Guided Instruction
There is more and more great stuff being published on this all the time, and there is a more developed list here (which I am still updating at the moment). Founder Niki Kaiser has written a really excellent summary here of what a lot of us have been thinking about and trying to do which I really recommend.
2. Mailing group
We have a mailing group which is now upwards of 250 science teachers (and some others!). You can sign up by by filling out this form. All topics are up for grabs and we have had some amazingly helpful conversations. Feel free to jump straight in or just lurk for a bit. Introductions are also great and if you have a twitter handle or blog do let everyone know.
Generally there is loads of stuff that science teachers could talk about and in truth it’s all fair game but where possible try and bring a CS perspective to bear.
We have now had a national get together two years in a row. The first year was in Norwich hosted by Niki Kaiser and the second one at Brunel University in London hosted by Andrew Carroll. Not only would it be great to see you at those events, but a lot of people said how nice it would be if we could have smaller regional meetings. Do let us know if you would be interested in hosting one of those.
There aren’t so many blogs out there focusing on CS in science teaching but the ones we know of are below. If we have missed any out please let us know! On each blog you should be able to pop your email address in and subscribe which is highly recommended so you don’t miss anything.
There are a few terms that we use quite a bit so I thought it might be helpful if I just make a little glossary. Let me know if there is anything else you think should go in there.
Bar Model: an instructional technique using bars to make quantitative information easier to grasp. Ben Rogers is one of the main teachers promoting the bar model and you can read about it here.
Cognitive Load: the burden placed on working memory by a given task. The reading at the top of the page has more information about this.
Domain General Skills: general thinking skills like creativity, critical thinking, problem solving, analysis and “working scientifically.” A lot of CS is about where these skills come from and to what extent they are dependent on domain knowledge. See here for an explainer.
Domain knowledge: a person’s knowledge of a particular domain e.g. mechanics, molecular biology, inorganic chemistry.
Dual Coding: the instructional technique of using visuals to support verbal explanations. See Pritesh’s work on this here.
Encoding: the process of embedding new information in long term memory
Epistemology: the study of knowledge. Within CS this refers to the rules which govern how knowledge is added to a particular domain. See cognitive scientist Paul Kirschner’s take on it here as applied to inquiry learning or my summary here.
Explicit Instruction: an approach to teaching that gives students all the information they need and does not rely on inquiry or discovery based approaches. See Greg Ashman’s explainer here.
Novices and experts: the idea that the cognitive architecture of a novice learner is fundamentally different to that of an expert learner. See also surface/deep structure and epistemology.
Sequencing: this is how you design instruction to make sure that one concept leads effortlessly onto the next concept without confusing the student. See Pritesh’s big picture thinking here.
SLOP: Shed Loads of Practice. This is a code phrase for any time we have decided to make our own textbooks/worksheets which feature loads and loads of practice work for students to complete. See here and here for more.
Surface/deep structure: any problem has surface structure and deep structure. The surface are the particular details involved in the problem and the deep structure is the conceptual information required to solve it. The seminal reading on the topic is here
Threshold Concepts: a concept that must be grasped before another concept can be understood. Niki is the real expert on this and you can find all her material here.
Transfer: this is “application” in old money. It’s the ability to transfer your knowledge to new situations. This is an incredibly difficult thing to achieve, and actually some psychologists believe it is borderline impossible. See here for some more reading.
6. People to follow on Twitter
This list is almost laughably incomplete, and I hope to be able to update it as time goes on (or if someone else wants to do that job for me…). Every so often I will just search the CogSciSci hashtag on twitter, so if you want to connect with other CogSciSci people on twitter then use the hashtag!
We’re really, really keen to learn and support colleagues both near and far. Please don’t hesitate to get in touch and contribute – we would love to hear your voice. And if you think there is anything that belongs in this particular introduction throw me an email or drop a line in the comments.