Structural equation models for explaining autonomous learning: a systematic literature review
Keywords:
autonomous learning; structural equation modeling; digital competence; intrinsic motivation; perceived efficacyAbstract
Introduction: Autonomous learning is a multidimensional construct that integrates cognitive, motivational, technological, and self-regulatory processes. Materials and methods: A systematic review with a theoretical-methodological scope was conducted. Empirical studies and methodological documents on autonomous learning, self-regulation, digital competence, intrinsic motivation, perceived efficacy, and structural equation modeling were examined. Results: The evidence showed recurrent associations between digital competence and autonomy, intrinsic motivation and self-regulated engagement, and perceived efficacy, persistence, and study strategies. CB-SEM was mainly used for confirmatory purposes; PLS-SEM emphasized explanation and prediction; fsQCA was identified as a configurational complement. Discussion: The findings indicated that autonomy is better explained through a network of resources and beliefs than through a single predictor. Conclusions: Structural equation models provide a relevant framework for integrating latent constructs, measurement error, and direct and indirect effects. However, cross-sectional evidence does not support a universal causal sequence.
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