AERA MODEL: A LEARNING MANAGEMENT MODEL FOR DANCE PERFORMANCE SKILLS

Abstract

Background and Objectives: Performance skill learning in dance education involves the integration of perceptual awareness, embodied movement, and holistic cognitive processes. However, current instructional practices predominantly rely on linear, step-by-step teaching approaches that emphasize imitation and repetitive training. While such approaches enable learners to perform accurately, they do not adequately explain how internal learning mechanisms contribute to skill development. In practice, skill learning in dance is inherently nonlinear, recursive, and adaptive. Although psychomotor theories proposed by Bloom, Simpson, Davies, and Harrow—widely applied in Thai dance education research—effectively describe observable learning behaviors, they provide limited explanations of causal mechanisms at the system level. In response to these limitations, this study proposes the AERA model as a mechanism-based adaptive learning system to advance the conceptual understanding of performance skill learning in contemporary Thai contexts.

Methods: This study employed a conceptual synthesis approach by collecting and analyzing the relevant literature on performance skill learning in dance education from the Thai Journals Online (TJO) database between 2017 and 2023. A comparative analysis was conducted to identify conceptual gaps and to synthesize a system-based model capable of providing a deeper explanation of learning mechanisms. 

Key Findings: The AERA model consists of four external learning components: 1) Awareness, 2) Expert, 3) Reflect (Metacognitive Regulation), and 4) Assessment (Performance-Based Assessment). These components are interconnected through feedback calibration within an iterative learning cycle. At the same time, the model incorporates five internal learning mechanisms: 1) perception, 2) cognitive encoding, 3) movement-based learning, 4) metacognitive regulation, and 5) feedback calibration. These internal processes operate as a causal chain supported by recursive feedback loops, resulting in nonlinear learning processes rather than permanently sequential. Consequently, the model generates emergent learning outcomes characterized by individual and context-specific adaptation.

Implications of the Study: The AERA model contributes to the theoretical advancement of performance skill learning by shifting the explanation perspective from descriptive frameworks to a mechanism-based framework. It explains how learning occurs within a system and provides a foundation for designing instructional practices that integrate feedback, reflective processes, and continuous learner adaptation. Furthermore, the model can be applied across disciplines involving performance-based learning that require a deeper understanding of learning mechanisms.

Conclusions and Future Study: The AERA model presents a new perspective on performance skill learning in dance education as a mechanism-based adaptive system emerging from continuous interactions between internal learning mechanisms and external learning processes. Feedback functions as the driving force of the system, enabling nonlinear and context-sensitive learning. Future research should focus on three key areas: 1) the development of measurement tools to capture learning mechanisms in performance skill learning, 2) empirical investigations into the causal relationships among model components to validate the conceptual framework across diverse contemporary contexts, and 3) the extension of the model to various performance-based disciplines to examine its flexibility and applicability across different socio-cultural settings.