Examining Individual Differences in Preschoolers’ Active Learning

• In Event: 2-105 - The Child as an Active Learner: Influences of Social-models, Curiosity, Strategy, and Executive Function

Integrative Statement

Recent studies suggest that active information gathering boosts children's performance in learning experiments (e.g., Ruggeri, Markant, Gureckis, & Xu, 2016). For example, Sim and colleagues found that 5- and 7-year-olds learn categories better after self-selecting examples of category membership than when passively presented a random sequence of examples (Sim, 2016; Sim, Tanner, Alpert, & Xu, 2015). While this literature suggests an overall active learning advantage, studies also report substantial variability in performance, suggesting that some children may have trouble selecting helpful information and integrating it into a useful learning strategy.

Yet little is known about individual differences in children’s active learning. What skills help children be good active learners? Do all children equally benefit from active control over passive learning? How do experimental measures of active learning map onto real-world learning outcomes? These questions have important theoretical implications and may inform education interventions, particularly for children from under-resourced backgrounds who are at increased risk for poor academic outcomes.

We examined variability in active learning skills in two studies with diverse samples of preschoolers. We focused on the role of executive functions (EF) as key control skills to support active learning. EF, neurocognitive self-regulation skills including working memory, inhibitory control, and attention shifting, are robustly related to academic learning (Ursache, Blair, & Raver, 2012). But little is known about how EF relates to children’s real-time learning processes.

Study 1 examined performance in an active category learning task (modified from Sim et al., 2015) and a battery of well-validated EF, attention, and academic assessments with 101 low-SES preschoolers. The goal of the learning task was to find the category boundary that separated a row of trees into two groups based on the animals living inside. The task consisted of 4 blocks. In each block, children self-selected 2 trees to learn the category membership (revealing the animal inside the tree), and were then asked to identify the category boundary. Results showed that as a group, children were above chance in selecting informative examples and in classifying trees based on the category boundary they learned during selection. Using linear mixed effect models, we found that attention skills were related to children’s selection performance, whereas working memory and inhibitory control were related to classification performance. Children who were better at selection and classification scored higher on standardized academic assessments, over and above the effects of EF, attention, and demographics. These findings suggest that young children’s trial-by-trial learning decisions may reveal insight into how EF supports the acquisition of knowledge.

Study 2 expanded on these results using a similar category learning task featuring within-subject active and passive conditions with 240 high- and low-SES preschoolers. Data analysis is ongoing, but preliminary results suggest that EF may moderate the relation between active vs. passive performance, such that children with higher EF skills demonstrate a more robust active learning advantage than peers with lower EF. Additional analyses will examine the interaction between SES and EF on selection and classification performance in each condition, and relations to early math skills.

Authors

• Kat Adams, New York University
  Presenting Author

• George Kachergis, Radboud University
  Non-Presenting Author

• Todd Gureckis, New York University
  Non-Presenting Author

• Cybele Raver, New York University
  Non-Presenting Author