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Effortful control (EC) is a dimension of temperament that encompass individual differences in self-regulation and the control of reactivity. Much research suggests that EC has a strong foundation on the development of executive attention, but increasing evidence also shows a significant contribution of the rearing environment to individual differences in EC. The aim of the current study was to predict the development of EC at 36 months of age from early attentional and environmental measures taken in infancy using a machine learning approach. A sample of 78 infants participated in a longitudinal study running three waves of data collection at 6, 9, and 36 months of age. Attentional tasks were administered at 6 months of age, with two additional measures (i.e., one attentional measure and another self-restraint measure) being collected at 9 months of age. Parents reported household environment variables during wave 1, and their child's EC at 36 months. A machine-learning algorithm was implemented to identify children with low EC scores at 36 months of age. An "attention only" model showed greater predictive sensitivity than the "environmental only" model. However, a model including both attentional and environmental variables was able to classify the groups (Low-EC vs. Average-to-High EC) with 100% accuracy. Sensitivity analyses indicate that socio-economic variables together with attention control processes at 6 months, and self-restraint capacity at 9 months, are the most important predictors of EC. Results suggest a foundational role of executive attention processes in the development of EC in complex interactions with household environments and provide a new tool to identify early markers of socio-emotional regulation development. [ABSTRACT FROM AUTHOR]

The objective of this research was to develop robust predictive models of the gains in working memory (WM) and fluid intelligence (Gf) following executive attention training in children, using genetic markers, gender, and age variables. We explore the influence of genetic variables on individual differences in susceptibility to intervention. Sixty‐six children (males: 54.2%) aged 50.9–75.9 months participated in a four‐weeks computerized training program. Information on genes involved in the regulation of dopamine, serotonin, norepinephrine, and acetylcholine was collected. The standardized pre‐ to post‐training gains of two dependent measures were considered: WM Span backwards condition (WISC‐III) and the IQ‐f factor from the Kaufman Brief Intelligence Test (K‐BIT). A machine‐learning methodology was implemented utilizing multilayer perceptron artificial neural networks (ANN) with a backpropagation algorithm. Both ANN models reached high overall accuracy in their predictive classification. Variations in genes involved in dopamine and norepinephrine neurotransmission affect children's susceptibility to benefit from executive attention training, a pattern that is consistent with previous studies. The goal of this study was to use genetic markers related to the regulation of neurotransmitters associated with cognitive control and attention, to achieve an accurate predictive classification of cognitive gain outcomes following an attentional training module in children. We developed machine learning models including the genetic markers, gender and age, with data from a sample of 66 children, both genders, ages 4‐6 years old, who participated in a four‐week computerized training program. Results suggest that we can use a machine learning approach to identify, with high accuracy, which children would benefit from a specific cognitive training program increasing their fluid intelligence and/or working memory capacity, based only on the patterns of information from the variables in the study. These results have important implications for the design of targeted and early interventions during children's cognitive development and they highlight the importance of genetic information in the understanding of cognitive performance. [ABSTRACT FROM AUTHOR]

Predicting and understanding different key outcomes in a student's academic trajectory such as grade point average, academic retention, and degree completion would allow targeted intervention programs in higher education. Most of the predictive models developed for those key outcomes have been based on traditional methodological approaches. However, these models assume linear relationships between variables and do not always yield accurate predictive classifications. On the other hand, the use of machine-learning approaches such as artificial neural networks has been very effective in the classification of various educational outcomes, overcoming the limitations of traditional methodological approaches. In this study, multilayer perceptron artificial neural network models, with a backpropagation algorithm, were developed to classify levels of grade point average, academic retention, and degree completion outcomes in a sample of 655 students from a private university. Findings showed a high level of accuracy for all the classifications. Among the predictors, learning strategies had the greatest contribution for the prediction of grade point average. Coping strategies were the best predictors for degree completion, and background information had the largest predictive weight for the identification of students who will drop out or not from the university programs. [ABSTRACT FROM AUTHOR]