June 2019 research round-up

Research highlights in learning and education from around the world
June 2019 research round-up

Why does Montessori schooling remain under-appreciated?

The Montessori school system emphasises many of the traits we see as desirable in learners: free choice, self-guided learning, self-motivation, and a joy in learning that is often absent from conventional classrooms. Furthermore, the system aligns well with educational psychology research, and some evidence indicates that the Montessori system produces better learning outcomes. With the quality and value of the traditional education system often questioned, why does Montessori remain marginalised?

According to author Angeline Lillard, the major challenge for Montessori is that it aligns poorly with many staples of conventional schooling, and wholesale changes are deemed too radical. The author recommends a more complete evidence base be developed on the benefits of Montessori schooling, with the findings potentially used to inform change.

Lillard (2019) Shunned and admired: Montessori, self-determination, and a case for radical school reform. Educational Psychology Review DOI: https://doi.org/10.1007/s10648-019-09483-3

Does learning to code help cognitive skills?

Computer programming is increasingly seen by policymakers as an important skill for students to learn, with the potential to broaden employment opportunities and, through learning transfer, to benefit various cognitive skills. However, the evidence for such transfer of learning remains conflicted.

In this meta-analysis, Scherer and colleagues analysed 105 studies to determine whether learning to code enhances more general cognitive skills, and which cognitive domains benefit most. Overall, they find that computer programming does aid cognitive performance. The biggest improvement was in programming-specific skills, but beneficial effects also extended to cognitive abilities such as creative thinking, reasoning, and mathematical skills. There was little benefit for literacy or school achievement. The analysis supports the idea that learning to code improves some cognitive skills in school students.

Scherer et al. (2019) The cognitive benefits of learning computer programming: A meta-analysis of transfer effects. Journal of Educational Psychology 111(5): 764-792 DOI: http://dx.doi.org/10.1037/edu0000314

The human brain replays memories of non-spatial tasks

During rest and sleep, the hippocampus replays activity patterns that occurred during recent events, helping to consolidate memories of those experiences. This replay, which is much briefer than the actual experience, has typically been observed in rodents following a spatial learning task.

In this study, researchers show that the human hippocampus also replays activity that occurs during a non-spatial, decision making task. Using fMRI, the authors detected sequences of activity at rest that were similar to sequences during the non-spatial task. This was possible despite fMRI being slow compared to the rate of hippocampal replay. The authors conclude that hippocampal replay is not limited to spatial learning, and that it may instead help form cognitive maps of how events relate to each other.

Schuck and Niv (2019) Sequential replay of nonspatial task states in the human hippocampus. Science 364 (6447): eaaw5181 DOI: https://doi.org/10.1126/science.aa25181

Learning-induced changes in hippocampal activity

What changes when we form a new autobiographical memory? In rats, the pattern of cell activation during an experience is later replayed during rest and sleep. Remarkably, a similar pattern also precedes experience, a phenomenon called “preplay”. This effectively means there are “before” and “after” versions of an experience. In this study, the before and after versions were compared as a measure of learning.

The authors found two major changes at the level of cell assemblies (i.e. groups of cells active at the same time). First, after the experience, cells in an active assembly had higher firing rates. Second, the cells were more tightly in sync with each other. These findings indicate that learning produces more coordinated activity by refining pre-existing activity templates.

Farooq et al. (2019) Strengthened temporal coordination within pre-existing sequential cell assemblies supports trajectory replay. Neuron DOI: https://doi.org/10.1016/j.neuron.2019.05.040

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