Kaikki aineistot
Lisää
Direct and moderation effects of swimming competence using an integrated model of self-determination theory (SDT) and theory of planned behaviour (TPB) were examined in two large-scale studies among young children. Specifically, we examined whether swimming competence had direct and moderation effects on social psychological variables of perceived need support, autonomous motivation, TPB social cognition constructs, and intention. In Study 1, using a cross-sectional survey of 4959 primary school children, swimming competence formed significant positive relationships with all model variables (β =.061 to.330, p < .05) except intention (β = -.009, p > .05), and its moderation effect on model parameters were small in size or not statistically significant. In Study 2, using a pre-post-test quasi-experiment among 1,609 primary school children, improvement of swimming competence was associated with change-scores in all model variables (β =.046 to.230, p < .05) except subjective norm (β =.049, p > .05). Swimming competence did not significantly moderate the parameter estimates of the integrated model (p > .05) at the change-score level. Findings indicate that swimming competence is associated with higher autonomous motivation; TPB social cognitions of attitude, subjective norm, and perceived behavioural control; and intention. However, swimming competence did not moderate the parameter estimates of the integrated model.
Machine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of ∼1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets.