Identifying learning patterns of children at risk for Specific Reading Disability

Barbot, Baptiste; Krivulskaya, Suzanna; Hein, Sascha; Reich, Jodi; Thuma, Philip E.; Grigorenko, Elena L.

doi:10.1111/desc.12313

Cited by 8 publications

(6 citation statements)

References 87 publications

(140 reference statements)

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“…Participants are often recruited according to stringent criteria that are arbitrarily set and inconsistently applied across studies (Stuebing, Fletcher, Branum-Martin, Francis, & Van Der Heyden, 2012; Tolar, Fuchs, Fletcher, Fuchs, & Hamlett, 2014). For example, selection criteria for children with reading difficulties ranges from reading scores of −1 standard deviation to −2.5 standard deviations below the population mean (Barbot et al, 2016; Catts, Suzanne, & Ellis, 2006). As a consequence, children included in one study may be excluded from others, yielding evidence that may not be generalizable to the broad, mixed population of children with learning-related difficulties (Coghill & Sonuga-Barke, 2012; Kotov et al, 2017).…”

Section: A New Approach To Learning Problemsmentioning

confidence: 99%

Cognitive dimensions of learning in children with problems in attention, learning, and memory.

Holmes¹,

Guy²,

Kievit³

et al. 2021

Journal of Educational Psychology

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A data-driven, transdiagnostic approach was used to identify the cognitive dimensions linked with learning in a mixed group of 805 children aged 5 to 18 years recognized as having problems in attention, learning, and memory by a health or education practitioner. Assessments included phonological processing; information processing speed; short-term and working memory; executive functions; and attainments in word reading, spelling, and math. Data reduction methods identified 3 dimensions of phonological processing, processing speed, and executive function for the whole sample. This model was comparable for children with and without attention-deficit/hyperactivity disorder (ADHD). The severity of learning difficulties in literacy was linked with phonological processing skills, and in math with executive control. Associations between cognition and learning were similar across younger and older children and individuals with and without ADHD, although stronger links between learning-related problems and both executive skills and processing speed were observed in children with ADHD. These results establish clear domain-specific cognitive pathways to learning that distinguish individuals in the heterogeneous population of children struggling to learn.

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Section: A New Approach To Learning Problemsmentioning

confidence: 99%

Cognitive dimensions of learning in children with problems in attention, learning, and memory.

Holmes¹,

Guy²,

Kievit³

et al. 2021

Journal of Educational Psychology

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show abstract

“…Deviations from the growth function can also be fairly analyzed (e.g., capturing stimulus absorption , namely the person’s “preference” for one CI stimulus over another, likely to cause the stimulus-dependency challenge in DT tasks; (Barbot et al, 2016a). Extensions of trial-by-trial latent growth curve models for microdevelopmental data (Barbot et al, 2016b) could nicely accommodate such effort, while further controlling for stimulus-dependency (e.g., “method” factors by type of stimulus; Grimm et al, 2009).…”

Section: Mtci Frameworkmentioning

confidence: 99%

The Dynamics of Creative Ideation: Introducing a New Assessment Paradigm

2018

Self Cite

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Despite six decades of creative cognition research, measures of creative ideation have heavily relied on divergent thinking tasks, which still suffer from conceptual, design, and psychometric shortcomings. These shortcomings have greatly impeded the accurate study of creative ideation, its dynamics, development, and integration as part of a comprehensive psychological assessment. After a brief overview of the historical and current anchoring of creative ideation measurement, overlooked challenges in its most common operationalization (i.e., divergent thinking tasks framework) are discussed. They include (1) the reliance on a single stimulus as a starting point of the creative ideation process (stimulus-dependency), (2) the analysis of response quality based on a varying number of observations across test-takers (fluency-dependency), and (3) the production of “static” cumulative performance indicators. Inspired from an emerging line of work from the field of cognitive neuroscience of creativity, this paper introduces a new assessment framework referred to as “Multi-Trial Creative Ideation” (MTCI). This framework shifts the current measurement paradigm by (1) offering a variety of stimuli presented in a well-defined set of ideation “trials,” (2) reinterprets the concept of ideational fluency using a time-analysis of idea generation, and (3) captures individual dynamics in the ideation process (e.g., modeling the effort-time required to reach a response of maximal uncommonness) while controlling for stimulus-specific sources of variation. Advantages of the MTCI framework over the classic divergent thinking paradigm are discussed in light of current directions in the field of creativity research.

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“…First, groups are often recruited according to stringent inclusion criteria that are arbitrarily set and inconsistently applied across studies (Stuebing, Fletcher, Branum-Martin, Francis, & VanDerHeyden, 2012;Tolar, Fuchs, Fletcher, Fuchs, & Hamlett, 2014). For example, selection criteria for children with reading difficulties ranges from reading scores of -1 to -2.5 SD below the population mean (Barbot et al, 2016;Catts, Adlof, & Ellis, 2006). As a consequence, children included in one study may be excluded from others, yielding evidence that may not be generalisable to the broad, mixed population of children with learning-related difficulties (Coghill & Sonuga-Barke, 2012;Kotov et al, 2017).…”

Section: A New Approach To Learning Problemsmentioning

confidence: 99%

Cognitive dimensions of learning in children with problems in attention, learning and memory

Holmes¹,

Guy²,

Bryant³

et al. 2020

Preprint

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A data-driven, transdiagnostic approach was employed to identify the cognitive dimensions linked with learning in a large mixed group of children recognised to have problems in the areas of attention, learning and memory by a health or education practitioner (N=805). The children completed a battery of cognitive assessments tapping phonological processing, information processing speed, short-term and working memory, and executive functions, and attainments in reading, spelling, vocabulary and maths. Data reduction methods identified three cognitive dimensions of phonological processing, processing speed and executive function for the sample as a whole. This model was comparable for children with and without ADHD. The severity of learning difficulties in literacy was linked to phonological processing skills, and in maths to executive control. Associations between cognition and learning were broadly comparable across subgroups, but learning-related problems were more strongly linked to executive skills and processing speed in children with ADHD than without ADHD. The results establish that domain-specific cognitive pathways to learning distinguish a large heterogeneous population of struggling learners. They also indicate that robust cognitive skills may be vital in compensating for the adverse impact of ADHD behaviours on learning.

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Identifying learning patterns of children at risk for Specific Reading Disability

Cited by 8 publications

References 87 publications

Cognitive dimensions of learning in children with problems in attention, learning, and memory.

Cognitive dimensions of learning in children with problems in attention, learning, and memory.

The Dynamics of Creative Ideation: Introducing a New Assessment Paradigm

Cognitive dimensions of learning in children with problems in attention, learning and memory

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