Validating a Deep Learning Model: The Nexus of Self-Regulation Strategies and Student Well-Being

Keywords

Deep learning strategies questionnaire, Self-regulated learning, Student well-being, Self-efficacy, Academic performance

Abstract

This study was principally focused on verifying the suitability of the Deep Learning Strategies Questionnaire for Romanian academic environments and examining the interrelations among deep learning strategies, self-efficacy, subjective well-being, and academic performance. Utilizing a correlational-cross-sectional approach, the research involved 130 university students from various Romanian institutions. Data gathering was conducted via an extensive multidimensional questionnaire, which assessed components such as deep learning strategies, perceived self-efficacy, subjective well-being, and academic performance indicators. The methodological process included extensive collaboration with several higher education institutions for participant recruitment. The data analysis was carried out using JASP version 0.18.1, which combined descriptive and inferential statistical approaches with structural equation modeling. The research aimed to endorse a theoretical model that interconnects deep learning self-regulation strategies with elements like student well-being, perceived self-efficacy, and their collective influence on academic achievement. Notably, the exploratory factor analysis revealed the presence of five distinct factors, an enhancement from the four factors identified in the original model, providing a more comprehensive understanding of deep learning strategies. Furthermore, the hierarchical model related to deep learning strategies exhibited strong congruence. The study's instruments demonstrated robust reliability and validity, as evidenced by internal consistency metrics ranging from acceptable to high levels. This substantiates the efficacy of these scales in evaluating a broad range of learning strategies in an educational setting.

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