Low motor competence (MC) can cause low participation in physical activities in preschool children, and together with a high caloric intake, it can lead to obesity. Interventions on motor skills are effective in the short term to improve MC, therefore the objectives of this study were (1) to investigate the effect of a short six-week program on levels of motor competence in preschool children, and (2) to examine the effects of gender-based intervention. A total of 156 preschool children (5.20 ± 0.54 years old) from Lugo (Spain) participated. A quasi-experimental pre–post-test design was used with a control group of 76 students. The Movement Assessment Battery for Children—2nd Edition (MABC-2) was used to collect the data. Significant differences between the control and experimental groups were found after the intervention program in aiming and catching (p < 0.001), balance (p < 0.001), the total score of eight tests (p < 0.001), and total percentile score (p < 0.001). The results regarding gender in the experimental group showed a reduction in differences with respect to the initial results except in aiming and catching, where scores were higher in boys. The data suggest that the application of specific intervention programs in MC could positively influence the improvement of MC in preschool children, thus reducing differences between genders.
The COVID-19 pandemic, and the containment measures adopted by the different governments, led to a boom in online education as a necessary response to the crisis posed against the education system worldwide. This study compares the academic performance of students between face-to-face and online modalities in relation to the exceptional situation between the months of March and June 2020. The academic performance in both modalities of a series of subjects taught in the Psychology Degree at the European University of the Atlantic (Santander, Spain) was taken into account. The results show that student performance during the final exam in the online modality is significantly lower than in the face-to-face modality. However, grades from the continuous evaluation activities are significantly higher online, which somehow compensates the overall grade of the course, with no significant difference in the online mode with respect to the face-to-face mode, even though overall performance is higher in the latter. The conditioning factors and explanatory arguments for these results are also discussed.
The purpose of this research article was to contrast the benefits of the optimal probability threshold adjustment technique with other imbalanced data processing techniques, in its application to the prediction of post-graduate students’ late dropout from distance learning courses in two universities in the Ibero-American space. In this context, the optimization of the Logistic Regression, Random Forest, and Neural Network classifiers, together with different techniques, attributes, and algorithms (Hyperparameters, SMOTE, SMOTE_SVM, and ADASYN) resulted in a set of metrics for decision-making, prioritizing the reduction of false negatives. The best model was the Neural Network model in combination with SMOTE_SVM, obtaining a recall index of 0.75 and an f1-Score of 0.60. Likewise, the robustness of the Random Forest classifier for imbalanced data was demonstrated by achieving, with an optimal threshold of 0.427, very similar metrics to those obtained by the consensus of the three best models found. This demonstrates that, for Random Forest, the optimal prediction probability threshold is an excellent alternative to resampling techniques with different optimal thresholds. Finally, it is hoped that this research paper will contribute to boost the application of this simple but powerful technique, which is highly underrated with respect to data resampling techniques for imbalanced data.
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