How to select the best learning strategies for children
Should teachers use different strategies depending on their students’ age? While the answer to this question may be obvious for educators, it is surprisingly obscure in educational research.
Excellent overviews of research on learning strategies exist that evaluate techniques with the goal of identifying the most effective ones for all learners. But it is unclear whether these strategies are equally effective for all age groups because most of the studies comprising these overviews involved university students. Little systematic research has been done regarding age-related differences in the strategies’ effectiveness.
For instance, a popular group of learning strategies is called “Generative Learning Strategies”, in which learners actively make sense of new material by relating it to their existing knowledge. Examples include creating concept maps, generating questions for a text or explaining a concept. Studies indicate that these strategies are more effective than passive approaches (such as re-reading the material), at least in adult learners. But are they similarly effective in children?
In a new study, Jasmin Breitwieser and I investigated this question. We compared the learning success of university students and late elementary school children. All participants from both age groups performed a facts learning task under two conditions, which differed only in the generative learning strategy employed. The first strategy prompted learners to generate a prediction (e.g., “What do you think, how many out of 10 mammals can fly?”) before showing them the correct fact. The second strategy prompted learners to generate a fitting example instead (e.g., “Can you name a mammal?”).
“Even strategies that seem similar at first glance may require different skills, and these skills may differ in their age trajectories.”
We found university students were successful when using either strategy; however, elementary school children were much more successful when they generated a prediction rather than an example. Why the age-related difference?
To find the answer, we need to think about what makes these strategies successful apart from encouraging learners to use their existing knowledge.
Our earlier research shows that generating predictions—in particular wrong ones—enables both adults and children to be surprised, which increases attention to new information and leads to better learning. Other research indicates that generating a good example serves as a memory cue that helps to recall the information. To come up with a good example requires analogical reasoning skills, which are known to improve substantially at least until late adolescence. Could it be that immature analogical reasoning skills are responsible for children’s trouble with examples?
We tested this by having children perform a standard analogical reasoning test after they had completed the learning tasks. Indeed, we found that children’s analogical reasoning abilities were related to their benefitting from generating examples. Furthermore, the better their reasoning abilities, the more the children resembled the adults in successfully using either strategy. These findings support the hypothesis that good analogical reasoning skills are a prerequisite for benefitting from example generation as a learning strategy.
“There are vast differences between individual children, and children’s abilities can change quickly, which will impact the efficacy of a strategy.”
Our findings suggest that educators should consider the prerequisites of a learning strategy when deciding whether to use it, particularly with children. Even strategies that seem similar at first glance may require different skills, and these skills may differ in their age trajectories. Similarly, learning strategies that are shown to be successful in high school and university students may not have the same results for elementary-age children whose abilities are still developing.
The finding that some children – those with very good analogical reasoning skills – could benefit from generating examples adds a further dimension to the decision of which strategy to give to whom. There are vast differences between individual children, and children’s abilities can change quickly, which will impact the efficacy of a strategy.
Thus, researchers should strive to provide evidence-based guidelines for selecting optimal learning strategies for each individual child, and these guidelines must take into account the child’s rapidly changing abilities. In the meantime, both educators and educational researchers should keep in mind that what’s good for adults is not always good for children.
Breitwieser, J. & Brod, G. Cognitive prerequisites for generative learning: Why some learning strategies are more effective than others. Child Development.
Brod, G., Breitwieser, J., Hasselhorn, M., & Bunge, S. A. (2019). Being proven wrong elicits learning in children – but only in those with higher executive function skills. Developmental Science. e12916.
Brod, G., Hasselhorn, M., & Bunge, S. A. (2018). When generating a prediction boosts learning: The element of surprise. Learning and Instruction, 55, 22–31.
Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., & Willingham, D. T. (2013). Improving students’ learning with effective learning techniques: Promising directions from cognitive and educational psychology. Psychological Science in the Public Interest, Supplement, 14(1), 4–58.
Rawson, K. A., & Dunlosky, J. (2016). How Effective is Example Generation for Learning Declarative Concepts? Educational Psychology Review, 28(3), 649–672.
Whitaker, K. J., Vendetti, M. S., Wendelken, C., & Bunge, S. A. (2018). Neuroscientific insights into the development of analogical reasoning. Developmental Science, 21(2), e12531.