Trace data can capture the temporal progress of regulation as learners engage in learning tasks over time¶

Claim · All claims
Evidence · 1 study · review r2 · q2
Currency · newest study 2019 · not yet checked against its sources · not reviewed by a person · overdue for a recheck since 2022: tool and model accuracy changes quickly

Subclaims¶

q2 i? Trace data capture students' immediate learning activities without interrupting learning, revealing patterns and changes in regulatory processes over time. → Järvelä 2019

Evidence¶

Järvelä 2019¶

Järvelä, S., Järvenoja, H., & Malmberg, J. (2019). Capturing the dynamic and cyclical nature of regulation: Methodological Progress in understanding socially shared regulation in learning. International Journal of Computer-Supported Collaborative Learning. https://doi.org/10.1007/s11412-019-09313-2

q2 · i? · review · r2

Narrative review of trace-methodology work following Winne's idea of trace as data reflecting learners' engagements over time, where "Traces capture students' immediate learning activities without interrupting the learning process". No effect size is printed, so impact is null.

"Traces capture students' immediate learning activities without interrupting the learning process, thus making it possible to follow the students' use of different studying techniques in the learning context."

Discussion¶

Related Claims¶

• 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
• A temporal extraction rule classified the GENERAL INSTRUCTION -> PLANNER action pattern as Planning in the first 15 minutes but as Monitoring in the last 30 minutes — a narrower finding that bears on this claim
• Epistemic network analysis provides a better account of temporal patterns between socio-cognitive events than coding-and-counting — related
• 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 — related
• Groups' progress during collaboration falls into four data-driven categories differing in sequence frequencies and the regulation phases groups return to — related
• Most PBL groups settled into stable regulatory patterns early in the course, with transitions usually between similar patterns — related
• Model-based clustering of tied durations identifies four weekly regulation patterns in PBL group discussions — related
• Weekly group data on PBL interactions showed substantial between-group variability (ICC 0.4), requiring multilevel growth models — related