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The first step preparing for the new syllabus (before program writing) is mapping the Working Scientifically outcomes against the content points. Unlike the 2017 syllabus, this has not been done for us, so I set out to achieve this over the break. The obvious tool is a spreadsheet because it's nice to have formulas, automated formatting, etc. However, the syllabus equations don't paste into cells which is a huge pain. Doing a copy and paste leaves you with lots of meaningless

We want our exam questions to be instruments; instruments that validly probe student understanding and authentically reflect the degree of development of their scientific schemas. This is a bigger challenge that we might first realise, but let's keep this aspiration as our first priority. How can science help us write better science exams? One of the best evidence-validated and sometimes counterintuitive theory of learning is Cognitive Load Theory (CLT). There are many aspect

It has been said, "you can't outrun AI". There is a truth here, yet I find this mental image exhausting and demoralising. When something is changing as rapidly as generative AI, how can we grab hold of this slippery thing enough to be the guides our students need us to be? Teachers can feel simultaneously exhausted at the thought of staying ahead and guilty because they recognise critical thinking about AI's threats and opportunities is a skill our students need. What is th

Teaching Data Science in NSW science classrooms Skilling our students to evaluate competing claims to truth has never been more important and the NSW science modules, Data Science 1 & 2 provide a wonderful opportunity for this. The CRAAP test is a golden oldie still used in many schools and has probably found its way into some Data Science teaching programs. I argue (challenge) that for this post-AI age, and especially for science education, CRAAP is no longer fit for purpo

Maybe hearing about the staggering environmental cost of using generative AI has promoted you to read this article. Knowing how it works is the key to understanding why these two simple suggestions can significantly reduce your environmental impact when using Generative AI. Before others say it, early estimates of energy use are likely to be 10 times higher than reality and reasonable AI use is a small fraction of your entire energy budget if you live in a developed country.

AI takes ages to generate an image and it is still not quite right (despite consuming a bunch of electricity). When you ask for a revision it starts from scratch and sometimes things that were OK, not longer are. Switch to using Procedural Image generation . OK, its NOT good for generating photo-real images, what it is great for are graphics and diagrams - things teachers use to explain concepts and ideas. Before we jump into the what and how , here are some advantages:

The Conversation - link: https://theconversation.com/how-do-you-feel-about-doing-exams-our-research-unearthed-4-types-of-test-takers-261552 If you had to do a test, how would you respond? Would you relish the chance to demonstrate your knowledge? Or worry you were about to fall short of the mark and embarrass yourself? Research tells us students’ attitudes towards taking tests or doing exams can have an impact on their performance . This is because what they think about them

It has been a joy—and a genuine professional privilege—to work alongside giants in educational research to explore a question that’s rather timely: how do we introduce generative AI to students in a way that supports, rather than overwhelms, their learning? In our recently published paper, we bring together the worlds of educational neuroscience and classroom practice. It’s somewhat ironic: the very tools designed to mimic neural networks can, if introduced poorly, overload t

Two recent gems of educational research are of immediate relevance to all teachers and especially those in remote and regional settings....