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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.

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.

The Remoralization Scale (RS) was developed in order to measure an individual's state in terms of how the person perceives him or herself in relation to his/her self-concept, self-value, hope, empowerment and positive anticipation. However, there is no data on the psychometric properties of the instrument in a non-clinical population. The aim of this study was to validate a Spanish version of the Remoralization Scale (RS) in a non-clinical sample with a prevention objective and/or the promotion of mental health. The original version of the RS was translated into Spanish and it was applied to a non-clinical sample of 1443 university students in Argentina (18–25 years old). Exploratory and confirmatory factor analyses were performed to study the factorial structure and the validity of the construct. Results suggest that a two factor-model (self-satisfaction and self-concept) results in the best fit for this non-clinical population. The reliability of the total scale and for both sub-scales was moderate to high. Discriminant analysis and contrasting groups analysis showed significant results: the clinical sample and the depression-symptoms sample showed less “remoralization” than the non-clinical sample and a group without depression symptoms, respectively. Results are discussed taking into account previous conceptualizations and studies. In conclusion, the RS was validated for its use in a non-clinical Argentinean population. Regarding its construct validity, a two-factor model with high reliability was obtained. La Escala de Remoralización (RS) fue desarrollada para medir el estado de un individuo en términos de cómo se percibe a sí mismo con relación a su autoconcepto, autovaloración, esperanza, empoderamiento y anticipación positiva. Sin embargo, no hay datos de propiedades psicométricas en la población no clínica. El objetivo de este estudio fue validar una versión española de la RS en una muestra no clínica con un objetivo de prevención y/o promoción de la salud mental. La versión original de la RS fue traducida al español y se aplicó a una muestra no clínica de 1.443 estudiantes universitarios en Argentina (de 18 a 25 años). Se realizaron estudios de análisis factorial exploratorio y confirmatorio para estudiar la estructura factorial y la validez del constructo. Los resultados indican que un modelo de 2 factores (autosatisfacción y autoconcepto) presenta un mejor ajuste para esta población no clínica. La consistencia interna de la escala total y de ambas subescalas fue de moderada a alta. Los análisis discriminantes y análisis de grupos contrastados fueron significativos: la muestra clínica y sujetos con síntomas de depresión mostraron menos «remoralización» que la muestra no clínica y sujetos sin síntomas depresivos. Los resultados se discutieron teniendo en cuenta conceptualizaciones y estudios previos. En conclusión, la RS fue validada para su uso en una población no clínica de Argentina. Respecto a su validez de constructo, se obtuvo un modelo de 2 factores con una alta confiabilidad.

Los accidentes de tránsito son un fenómeno complejo, resultado de factores ambientales, vehiculares y humanos, y una de las principales causas de muerte a nivel mundial. La inatención es un factor primordial que contribuye a los accidentes de tránsito. El objetivo del presente trabajo fue analizar la relación entre la atención según el modelo de redes atencionales de Posner (1994) y la propensión a cometer errores relacionados con la inatención durante la conducción vehicular. La muestra estuvo compuesta por 70 participantes, edades entre 19 y 59 años, ambos géneros, 9.83 años de experticia como promedio. Se utilizó el Cuestionario de Experiencias durante la conducción (ARDES-ERIC), Test de Redes Atencionales (ANT) y un cuestionario sociodemográfico. Los resultados indican que existe una correlación significativa entre el tiempo de reacción (TR) total y la propensión a cometer errores durante la conducción. La interacción entre la experticia y el TR total sobre la propensión a cometer errores fue significativa. La atención ejecutiva tuvo un efecto significativo sobre la propensión a cometer errores y la dimensión de control. El modelo que incluye la red de orientación y tiempos de reacción explicó el 20% de la propensión a cometer errores en la conducción. Una alta orientación está asociada con una baja propensión a cometer errores, y los tiempos de reacción más lentos están relacionados con altos errores de conducción. Los resultados son consistentes con estudios previos y aportan nueva evidencia sobre el rol de los tiempos de reacción y redes atencionales en interacción con variables sociodemográficas y experticia sobre la propensión a cometer errores en la conducción.

A substantial number of research studies have investigated the separate influence of working memory, attention, motivation, and learning strategies on mathematical performance and self-regulation in general. There is still little understanding of their impact on performance when taken together, understanding their interactions, and how much each of them contributes to the prediction of mathematical performance. With the emergence of new methodologies and technologies, such as the modelling with predictive systems, it is now possible to study these effects with approaches which use a wide range of data, including student characteristics, to estimate future performance without the need of traditional testing (Boekaerts and Cascallar, 2006). This research examines the different cognitive patterns and complex relations between cognitive variables, motivation, and background variables associated with different levels of mathematical performance using artificial neural networks (ANNs). A sample of 800 entering university students was used to develop three ANN models to identify the expected future level of performance in a mathematics test. These ANN models achieved high degree of precision in the correct classification of future levels of performance, showing differences in the pattern of relative predictive weight amongst those variables. The impact on educational quality, improvement, and accountability is highlighted.

[...]the purpose of this special issue is to consider new methodological and conceptual developments in the understanding of self-regulated learning in different domains such as: academic success, mathematical performance, and successful professional development. The authors review the most relevant research related to the use of this assessment technique. [...]an interesting guide whose specific microanalytic questions used to target self-regulation subprocesses (e.g., goal-setting, strategic planning, etc.) are highlighted, focusing on causal attributions.