Abstract
Mutualistic theories assume that the mastering of a skill, either cognitive or academic, supports and amplifies the development of other such abilities. The current study uses network science to model cross-sectional associations between cognitive and academic performance in two age-matched developmental cohorts. One cohort was a community sample drawn from the general school population, while the other included struggling learners. The community sample outperformed the struggling learners across all measures. Network models suggested that although the tasks were similarly interrelated across cohorts, there were some notable differences in association strength: Academic skills were more closely coupled in the community sample, while maths was more strongly related to cognitive skills in the struggling learners. We demonstrate the utility of network models as an analytic framework that is consistent with contemporary theories of learning difficulties and the nature of the relationship between cognitive and learning skills more broadly.
Highlights
We demonstrate the utility of network models as an analytic framework that is consistent with contemporary theories of learning difficulties and the nature of the relationship between cognitive and learning skills more broadly
We argue that researchers aiming to understand learning difficulties should consider that while cognitive skills can influence learning, the process of learning can influence cognitive skills
We included assessments of processing speed, working memory, executive function, and nonverbal reasoning, all of which have been previously linked to academic performance in both typical and atypical learners (Altemeier et al, 2008; Booth et al, 2010; Gathercole et al, 2004; Geary, 2011; Green et al, 2017; Holmes et al, 2020; Mayes & Calhoun, 2007; Peng & Fuchs, 2016; Taub et al, 2008; Yeniad et al, 2013)
Summary
The aim of the current study was to apply networks models to explore and compare the interrelations between cognitive and academic abilities in a community sample and a sample of struggling learners. We included assessments of processing speed, working memory, executive function, and nonverbal reasoning, all of which have been previously linked to academic performance in both typical and atypical learners (Altemeier et al, 2008; Booth et al, 2010; Gathercole et al, 2004; Geary, 2011; Green et al., 2017; Holmes et al, 2020; Mayes & Calhoun, 2007; Peng & Fuchs, 2016; Taub et al, 2008; Yeniad et al, 2013) To our knowledge, this is the first application of network science to cognitive-academic interrelationships in learners of different abilities. We were agnostic as to whether and how task interrelationships would differ across cohorts
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