June 2020 research round-up

Research highlights in learning and education from around the world
Published in Neuroscience
June 2020 research round-up

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Resilience in the face of mathematics anxiety

Mathematical anxiety (MA) is a common problem, with a 2012 PISA survey finding that 30% of 15-year-old students reported feeling powerless or nervous when dealing with mathematical problems. But how much of this anxiety is mathematics-specific, and how much might be due to related problems like general anxiety (GA) or test anxiety (TA)?

In this study, the authors looked to tease out how these forms of anxiety related to mathematics performance, and whether traits of resilience – resourcefulness, strength of character, flexibility of functioning – could protect against the various types of anxiety. They found that high resilience was associated with better maths performance and lower GA scores. The authors recommend early interventions to build students’ emotional regulation, communication and problem solving skills, as a way to limit general anxiety and thus protect against mathematics anxiety.

Donolato et al. (2020) Going beyond mathematics anxiety in primary and middle school students: the role of ego-resiliency in mathematics. Mind, Brain, and Education DOI: https://doi.org/10.1111/mbe.12251

Replay during memory retrieval

When we retrieve a memory, what happens in the brain? In this study, participants were asked to remember the sequential order of images (cue and target) they had seen the previous day. As they retrieved this information, their brain activity was recorded with magnetoencelphalography (MEG). Successful retrieval (i.e. correct recall of whether cue preceded target, or vice versa) was associated with replay of neural activity seen during the initial exposure. Moreover, the direction of replay – forward or backward – depended on whether the target image came before or after the cue. This shows that successful memory retrieval in humans relies on replay of event-related activity, and that the direction of replay depends on the demands of the task.

Wimmer et al. (2020) Episodic memory retrieval success is associated with rapid replay of episode content. Nature Neuroscience DOI: https://doi.org/10.1038/s41593-020-0649-z

What do people really believe about learning styles?

The learning style myth holds that people have preferred modes of learning, such as through auditory, visual or kinesthetic instruction. Although inaccurate, surveys indicate 80-95% of people – including educators – believe learning styles are real. This study investigated what learning style adherents actually believe in, such as whether the (supposed) traits are innate or changeable.

Based on questionnaire responses, the researchers identified two groups – those who believed the traits were innate, genetically determined and unchangeable, and those who thought learning styles were more variable across contexts and experience. They found that educators and non-educators were split evenly into these two groups. However, amongst educators, those who worked with younger children were more likely to believe in the innate, unchangeable nature of learning styles.

Nancekivell et al. (2020) Maybe they’re born with it, or maybe it’s experience: toward a deeper understanding of the learning style myth. Journal of Educational Psychology 112(2): 221-235. DOI: https://doi.org/10.1037/edu0000366

Brain network changes during learning depend on the reward

Brain activity occurring during an experience can later be reactivated during sleep or quiet waking periods, helping to consolidate memories. In this study, researchers looked at how these reactivations change the organization of brain networks, and how that underlies learning.

They found that experiences linked to a reward during learning (a tasty treat) were reactivated more often than non-rewarded experiences, and this led to better learning. Moreover, the network changed to make rewarded experiences easier to reactivate. In contrast, networks coding for non-rewarded experiences became harder to reactivate. This shows that changes in brain networks during learning depend on how relevant the experience is to the animal.

Sugden et al. (2020) Cortical reactivations of recent sensory experiences predict bidirectional network changes during learning. Nature Neuroscience DOI: https://doi.org/10.1038/s41593-020-0651-5

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