LEARNING STRATEGIES (ACRA) AMONG UNIVERSITY APPLICANTS FOR THE TOURISM DEGREE: A STRUCTURAL EQUATION MODELING AND NEURAL NETWORK APPROACH
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Abstract
Learning strategies play a fundamental role in the development of academic and professional competencies in the field of tourism, particularly during the transition to higher education. This study analyzes learning strategies based on the ACRA model among university applicants to the Tourism degree, using a combined approach of neural networks and structural equation modeling (SEM). A quantitative, correlational-explanatory, and cross-sectional design was employed, with a probabilistic sample of 136 Tourism students from the Pontificia Universidad Católica del Ecuador. The abbreviated version of the ACRA questionnaire was applied, which evaluates cognitive and control strategies, learning support strategies, and study habits—key competencies for training in dynamic, service-oriented contexts such as the tourism sector. Data analysis was conducted using SPSS 25 and AMOS 24 software. The results show high internal consistency (α = 0.912; ω = 0.912) and factor loadings above 0.70, confirming construct validity. The KMO index (0.811) and Bartlett’s sphericity test (p < 0.001) support the adequacy of the factor analysis. Furthermore, significant structural relationships were identified, with strong associations between cognitive strategies and learning processes (0.80), and moderate associations with study habits (0.63), in line with self-regulated learning principles. In conclusion, the ACRA model is confirmed as a valid and reliable instrument for evaluating learning strategies among university applicants in the tourism field, providing relevant empirical evidence for improving educational processes and decision-making in higher education oriented toward tourism.
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