T-pattern analysis detected temporal-structure differences between children with specific language disorder and typically developing children that frequency and sequential lag analyses did not¶

Claim · All claims
Evidence · 1 study · review r2 · q2
Currency · newest study 2014 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? In Kuvalja et al. (2014), frequency analysis and sequential lag analysis showed no differences between the two groups, but t-patterns of self-directed speech and self-regulatory behaviour of children with specific language disorder were more in number, more complex and typically included self-directed speech utterances. → Inge Molenaar & Sanna Järvelä 2014

Evidence¶

Inge Molenaar & Sanna Järvelä 2014¶

Inge Molenaar & Sanna Järvelä. (2014). Sequential and temporal characteristics of self and socially regulated learning. Metacognition and Learning. https://doi.org/10.1007/s11409-014-9114-2

q2 · i? · review · r2

Observational study comparing children with specific language disorder and typically developing children during a planning task, coding self-directed speech and self-regulatory behaviour from verbal and non-verbal behaviour. T-pattern analysis revealed group differences undetectable by frequency or lag analysis.

"T-patterns did show a difference between the two groups: temporal patterns of self-directed speech and self-regulatory behaviour of children with specific language disorder were more in number, more complex and typically included self-directed speech utterances."

Discussion¶

Related Claims¶

• The field lacks micro-level S-SRL theory and methodological guidelines, and studies have not connected temporal/sequential characteristics to learning gains — related
• T-pattern analysis reveals qualitative differences in six-year-olds' self-directed speech between typically developing children and peers with SLI that lag sequential and other methods do not detect — possibly the same claim (merge candidate)
• Lag-sequential Analysis reveals temporal ordering patterns in CSCL interaction that traditional coding-and-counting approaches hide — related
• Trace data can capture the temporal progress of regulation as learners engage in learning tasks over time — related
• Temporal process analysis reveals group- and task-related differences that the frequency accounts did not show, in the interchange of CPS elements over time — a narrower finding that bears on this claim
• Verbal-utterance-based methods leave collaboration in groups with asymmetrical interactions, such as young children, unexamined — related
• Multimodal data collection is increasingly common but analyses rarely align data streams temporally — related
• Children with developmental language disorder and strong verbal reasoning could partly compensate delays in mathematics at the individual level, though group-level analyses revealed no protective effects (reported by the editors, citing Kleemans et al.) — related