Active Ingredients, Additive Components, or Synergy? A Component Network Meta-Analysis of Self-Regulated Learning Interventions on Academic Achievement
Year: 2026
Type: preprint
Abstract: Self-regulated learning (SRL) interventions improve academic achievement on average, and they are multi-component packages: most combine cognitive strategy instruction, metacognitive strategy instruction, goal setting, self-monitoring, feedback, and other components. Prior syntheses estimated the role of components with meta-regression, which compares studies differing in many respects; component network meta-analysis (CNMA) instead estimates the incremental effect of each component from all arms of all trials, including active-control and dismantling comparisons. No CNMA of general SRL interventions has been located. This registered systematic review codes every arm (including control arms) of eligible trials against a 10-component framework (cognitive strategies, metacognitive strategies, metacognitive knowledge, motivational regulation, goal setting, self-monitoring, process feedback, resource management, cooperative learning, digital scaffolding) and fits pairwise meta-analysis, standard network meta-analysis, and additive and interaction CNMA models. Competing theoretical views make different predictions: an active-ingredient view (metacognitive instruction carries the effect), an additive view (components contribute independent increments), and a synergy view (components from different SRL phases interact positively). A prespecified feasibility pilot (50 randomly sampled full texts) with fixed decision rules determines whether the CNMA is confirmatory or exploratory. Coded component matrices and R scripts will be posted on OSF.
Source: OSF Preprints (OSF Preprints)
Author: Song Zhou
Language: English
Subfield: Developmental and Educational Psychology
Field: Psychology
Domain: Social Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W7219758791