Cognitive Prerequisites for Generative Learning: Why Some Learning Strategies Are More Effective Than Others

Abstract

This study examined age-related differences in the effectiveness of two generative learning strategies (GLSs). Twenty-five children aged 9-11 and 25 university students aged 17-29 performed a facts learning task in which they had to generate either a prediction or an example before seeing the correct result. We found a significant Age 9 Learning Strategy interaction, with children remembering more facts after generating predictions rather than examples, whereas both strategies were similarly effective in adults. Pupillary data indicated that predictions stimulated surprise, whereas the effectiveness of example-based learning correlated with children's analogical reasoning abilities. These findings suggest that there are different cognitive prerequisites for different GLSs, which results in varying degrees of strategy effectiveness by age. Garvin Brod was supported by a Jacobs Foundation Early Career Research Fellowship. This study was preregistered on the Open Science Framework. All data and materials, along with analysis scripts, have been made publicly available and can be accessed at https://osf.io/e4h9n/?view_only=37f9109f122b42 faaec678796b225037. We thank Freya Moßig and Manuel Lieb for their help in collecting the data and Silvia Bunge for valuable discussions. Open access funding enabled and organized by Projekt DEAL.

How Can We Make Active Learning Work in K–12 Education? Considering Prerequisites for a Successful Construction of Understanding

“…Knowledge of prerequisites thus suggests that although generating examples can work well for undergraduates, it should be rather ineffective for elementary-school children. In keeping with this hypothesis, results showed that elementary-school children learned less when they generated examples than when they generated predictions, whereas undergraduates learned comparably well in both conditions (Breitwieser & Brod, 2021). The difference in effectiveness between generating predictions and examples was related to children's reasoning abilities, in line with its assumed role as a prerequisite.…”

“…Third, knowledge of prerequisites for active learning can help instructors choose those active-learning practices that are likely to work best for a particular group of students and to provide the right level of guidance. In a recent study that illustrates this approach (Breitwieser & Brod, 2021), late-elementary-school children and undergraduates studied science facts using either of two active-learning practices: They had to either generate a prediction or come up with an example before seeing the correct information (a numerical fact). These two active-learning practices were contrasted because their cognitive prerequisites are known to differ.…”

“…Knowledge of prerequisites and their development can further explain successes and failures of various active-learning practices in the classroom. On the one hand, the findings of Breitwieser and Brod (2021) suggest that engaging students in prediction-observation cycles is an active-learning practice that is likely to work already in elementary school. In line with this claim, prediction-observation cycles form part of many successful STEM curricula that have proved effective for elementary-and secondary-school students (e.g., Hardy et al, 2006;Liew & Treagust, 1995;Monaghan & Clement, 2000).…”

“…Both analogical reasoning and working memory capacity exhibit strong and largely parallel age-related improvements that continue across the secondary-school years (Luna et al, 2004; Richland et al, 2006). A recent study demonstrated that children’s analogical reasoning capacities are a strong predictor of how much they will profit from actively generating examples of abstract concepts (Breitwieser & Brod, 2021). Coming up with a good example of an abstract scientific concept requires analogical reasoning because students have to compare potential examples to find the best match to the scientific model (Duit, 1991).…”

Generative Learning: Which Strategies for What Age?

“…A less ambitious approach would be to cross-sectionally compare two different GLSs or two different implementations of one GLS between age groups and to examine whether agerelated differences in effectiveness are related to differences in prerequisites. A recent study that took just this approach compared the effectiveness of two GLSs in late-elementary-school children and university students (Breitwieser and Brod 2020). Participants were given a learning task in which they generated either predictions or examples before being presented with the correct information (i.e., numerical facts).…”