Deep learning self-regulation strategies: Validation of a situational model and its questionnaire.

Abstract

Measuring self-regulated learning is crucial to improve our educational interventions. Self-report has been the major data collection method and a number of questionnaires exist. Importantly, the vast majority of the questionnaires are constructed from general theoretical models. Our aim was to develop a model and its questionnaire –i.e. Deep Learning Strategies questionnaire- to investigate how students regulate their learning strategies in more realistic learning situations. Four scales were created: (1) Basic learning self-regulation strategies; (2) Visual elaboration and summarizing strategies; (3) Deep information processing strategies; and (4) Social learning self-regulation strategies. A total of 601 higher education students formed the sample. We analyzed, first, the internal validity of the questionnaire. Three structural models were tested: (M1) mono-factor; (M2) scales correlate among them freely, and (M3) the scales are indicators of a general construct. The latter model showed a slight better fit. Additionally, a path analysis was carried out to study the degree in which the use of the Deep learning strategies depends on personal factors and is associated to performance. It was found that the use depends directly and positively on Learning goal orientation, on the self-messages defining the Self-regulation style of emotion and motivation focused on learning, and on Effort. Besides, these two last variables convey the effect of Self-efficacy that, at the same time, affects Effort. Academic performance depends positively on Effort but negatively to the use of Deep learning strategies. It is hypothesized this negative relationship is due to the method of measurement of academic performance.