This a set of summaries of three articles on the work by Erin Maire Furtak on the role of formative assessment, inquiry and the crucial role of epistemic engagment by teachers and students.
In a short paper I hope to get published, I use the framework from Imre Lakatos, to distinguish between research programmes that a degenerating or progressing. I argue that the Cognitive Readiness and Demand Theory developed using Neo-Piagetian Theory, is able to make bold falsifiable predictions into new areas of teaching.This largely due to its development of objective measures of the Cognitive level of thinking that students bring to a task and a way of measuring the cognitive demand of these tasks.
Whereas Cognitive Load Theory has major problems in particular measurement and has had to make ad hoc additions to its core theory which have reduced its openness to falsification.
Susan Carey was a prominent figure at the very start of cognitive science. Her work centers on how humans develop concepts from infancy through adulthood, emphasizing core knowledge systems, conceptual discontinuities, and learning mechanisms. She is a superb antidote to the idea that learning is a change in long term memory rather a change in how beliefs and ideas are organised. She shows how children build increasingly complex conceptual systems—such as number, agency, and causality—and how language and culture shape conceptual change.
Prediction is a simple but powerful way to help students learn science. When students guess or predict outcomes before seeing the results, they pay more attention, feel more curious, and remember information better (Brod, 2021). One key reason this works is surprise: when students’ predictions are wrong, the unexpected result grabs their attention and helps them remember the correct answer (Brod, Hasselhorn & Bunge, 2018). Studies even show that students’ pupils dilate more during prediction tasks, which is a sign that their brains are focusing and engaged (Brod & Breitwieser, 2019).
Prediction is especially useful for challenging misconceptions. When students predict outcomes that go against what they thought, they are more likely to learn the correct ideas (Brod et al., 2022). Prediction also makes learning more interesting, increasing curiosity and motivation, though the impact on memory can vary depending on the type of task (Brod & Breitwieser, 2019).
Because of these benefits, prediction-based strategies like Predict–Explain-Observe–Explain (PEOE) can be very effective in science lessons. Teachers can ask students to predict what will happen in an experiment, explain their prediction, observe the results, and then explain them. This encourages students to think critically, confront misconceptions, and understand science concepts more deeply (Brod, 2021).
In short, using prediction in science classrooms helps students focus, stay curious, and learn better, making it a practical strategy that teachers can easily apply to improve understanding and engagement.
We all probably know that regular exercise has wide ranging health benefits. This meta study seems to show that a well planned exercise programme can have positive effects on the crucial executive functioning of school age children. We also know these can be a key to success in schools
This research article presents a meta-analysis investigating how moderate-intensity exercise influences the executive functioning of healthy children aged six to twelve. By examining twenty-five studies, the authors conclude that physical activity significantly enhances working memory and cognitive flexibility, while providing more modest gains in inhibitory control. The data suggests that age-specific benefits exist, as older children show greater improvements in memory, whereas younger participants excel in flexibility tasks. To achieve the best results, the researchers recommend a specific intervention program consisting of thirty-minute sessions performed three to four times weekly over two to three months. Ultimately, the study advocates for exercise as a cost-effective tool to support neurological development and academic success during critical childhood stages.
This research explores how making explicit predictions improves science learning in primary school children by examining the role of executive functions, specifically inhibition and switching. Through a water displacement experiment, the study compares children who predict outcomes with those who evaluate them after the fact. Bayesian computational models reveal that while children with lower executive function scores struggle in post-event evaluation, the act of predicting “equalises” performance by promoting greater metacognitive flexibility. These results suggest that prediction scaffolds the belief revision process, allowing children to update their internal models regardless of their baseline cognitive control. Consequently, the authors propose that prediction serves as a vital learning strategy to offload the cognitive demands of scientific reasoning in developing minds.
This research, by Lorraine McCormack, explores the implementation of the Cognitive Acceleration through Science Education (CASE) programme as a method to support students moving from primary to secondary school in Ireland.
The study addresses common academic regression and declining interest in science caused by repetitive curricula and poor communication between school levels. By utilising bridging units, the intervention aims to enhance formal operational thought and high-level reasoning skills through concepts like cognitive conflict and metacognition.
Results indicated that pupils participating in the programme achieved significantly higher cognitive growth than those in non-intervention groups. Furthermore, the initiative provided valuable professional development for teachers, boosting their confidence in delivering complex scientific content. Ultimately, the source highlights that while curriculum time is limited, investing in structured thinking programmes can effectively sustain and advance student achievement during educational transitions.
A very interesting study was performed by Daniel Schwarz https://web.stanford.edu/~danls/Dyad%20Abstraction.pdf
This research investigates how collaborative problem solving in pairs, or dyads, produces more abstract mental representations than individuals working alone. Through three experiments involving gear mechanics and biological systems, the author demonstrates that pairs frequently develop parity rules and symbolic visualisations that exceed the performance of the most capable individuals. These findings suggest that abstraction is a natural byproduct of the need to establish common ground and resolve differing perspectives between partners. Rather than simply being more efficient, the social interaction itself forces participants to strip away surface details to create a shared, structured understanding. Ultimately, the studies provide evidence for socially situated cognition, where complex knowledge emerges from the unique communicative demands of group interaction.
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