The role of virtuality in learning strategies and stress prevention in higher education
DOI:
https://doi.org/10.5944/educxx1.35855Keywords:
learning strategies, anxiety, motivation, self-concept, teaching methods, structural equation modelsAbstract
Higher education has become a setting that increasingly demands hybrid learning models based on face-to-face and virtual methods. Concretely, this educational stage takes place during a complex period for the emerging adult, for which learning strategies must be developed in order to avoid academic stress. This study presents a quantitative, descriptive, ex post facto and cross-sectional design with a measurement in a single group. The objective was to contrast a structural equations model that integrates learning strategies, academic stress and multidimensional self-concept in a sample made up of 2.736 university students [♂ = 33,8% (n=924); ♀ = 66,2% (n=1.812)] with a mean age of 23,33±5,77 years, using as main instruments the MLSQ-SF, AF-5 test and the academic stress at the university questionnaire. Statistical analysis was performed with IBM SPSS® v.23.0 and IBM Amos® v.23.0 software. Results show better developed learning strategies in virtual learning modalities. Critical thinking was configured as being more dependent on the development of learning strategies in face-to-face modalities, whilst studying habits were more strongly associated with the self-regulation of effort in online approaches. Further, motivation was a stronger determinant of critical thinking and the time spent studying when this modality was used. In conclusion, self-concept was negatively associated with stress, obtaining stronger regression weights when distance learning methods were used, thus suggesting it to be preventive in nature. It can be concluded that virtual approaches favour greater interdependence between learning strategies, time spent studying and motivation. This could decrease stress and favour academic performance in a social context that increasingly demands hybrid learning models.
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