arXiv subjects

Mary Elizabeth Azukas

Publications and source records attributed to Mary Elizabeth Azukas.

2 recordsLinked to original sources

arXiv2026

A Taxonomy of Metacognitive Learning Scenarios in Professional Contexts: Integrating Systems Theory with Empirical Constraints

Metacognitive theories provide foundational frameworks for understanding self-regulated learning, yet they lack systematic integration into comprehensive scenario taxonomies capable of guiding AI-enhanced professional development interventions. Existing models inadequately specify how metacognitive components combine into distinct learning scenarios or how professionals progress from novice to expert functioning. A six-node open systems model, consisting of Environment, Input, Processes, Structures, Output, and Feedback, was developed by synthesizing four major theoretical frameworks. Combinatorial enumeration generated 216 mathematically possible learning scenarios. Four sequential constraint-based filters, including psychological plausibility, educational relevance, measurement feasibility, and intervention potential, informed by empirical workplace learning research, reduced this space to 24 priority scenarios. Five focal scenarios were subjected to formal concept analysis. The 24 priority scenarios were distributed across three developmental tiers: novice, with 6 scenarios; developing, with 10 scenarios; and expert/adaptive, with 8 scenarios. Analysis revealed critical theoretical gaps regarding the dynamic reconfiguration of monitoring-control relationships across expertise levels, the role of feedback topology in metacognitive development, and trade-offs between internal integration and external connectivity. Multiple viable developmental trajectories were identified. The taxonomy enables targeted, scenario-specific professional development interventions and generates testable predictions for advancing metacognition theory beyond primarily descriptive accounts.

cs.HC↗

arXiv2026

Integrating Cognitive Load and Embodied Cognition Theories Through Representations as Multi-Scale Attractors

This article proposes a formal rapprochement between cognitive load theory and embodied cognition by reconceptualizing psychological representations as dynamic multiscale attractors within a temporal-hierarchical prediction architecture. The apparent conflict between the two theories dissolves when viewed through a complex systems lens. Cognitive load theory describes compressed representations operating at medium timescales, while embodied cognition describes fast sensorimotor loops. These two theories describe complementary, timescale-separated processes that operate simultaneously without contradiction. Drawing on dynamical systems theory, hierarchical predictive processing, and a six-node open-systems architecture, the article proposes that learning is best understood as attractor sculpting across coupled temporal layers, from millisecond sensorimotor loops through seconds-to-minutes working memory compression to the slow, years-long reshaping of knowledge structures. Three theoretical reconciliations are developed: time-scale separation, spatially extended hierarchies, and developmental trajectories from novice to expert configurations. From these understandings, five novel, testable predictions are advanced concerning cross-timescale interference, embodied load reduction, metacognition as timescale coupling, feedback topology, and the schema flexibility paradox. For each prediction, converging empirical evidence is reviewed, and formal empirical research designs are proposed. Implications for instructional design, assessment practice, and educational leadership are developed throughout, grounded in the principle that cognitive load and embodied engagement are not competing demands but complementary expressions of a unified temporal-hierarchical cognitive system.

q-bio.NC↗