Situational Formation of Enduring Traits: A Comprehensive Review of the Hierarchical Nature of Self-Regulated Learning

Abstract

Self-regulated learning (SRL) researchers acknowledge both top-down influences of dispositions and bottom-up influences of situational learning processes. However, the hierarchical–bidirectional influence linking dispositions and SRL behaviors has not been systematically theorized. Existing SRL frameworks (e.g., MASRL) characterize monitoring and control processes in detail, but researchers tend to treat dispositions as largely static rather than as outcomes shaped through SRL engagement. To address this gap, we propose the Hierarchical–Bidirectional Model of SRL, which synthesizes (a) a hierarchical structure spanning general, academic, subject, and task/situation levels; (b) bidirectional dynamics between dispositions and SRL processes; and (c) domain similarity, which moderates cross-domain transfer and the strength of hierarchical links. We specify two central mechanisms. First, self-regulation in specific domains is influenced by proximal dispositions within those domains (Specification effect). Second, situational self-regulation fosters dispositions at corresponding levels, and this feedback loop contributes to the development of more resilient and general dispositions, which may extend beyond academic domains (Generalization effect). To empirically test these hierarchical–bidirectional relations, we highlight methodological approaches including intensive longitudinal designs, models that incorporate both lagged and contemporaneous effects, and psychological network analyses. We also emphasize the role of computational modeling for achieving clearer construct specification and for formally capturing the dynamic processes through which SRL cycles accumulate into dispositional development. Overall, we present a model that reframes SRL as a developmental, hierarchical, and reciprocal process, providing a unifying framework that integrates diverse constructs and clarifies how dispositions both guide and emerge from SRL across domains.