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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.

This study explores the effects of the shift to emergency remote teaching (ERT) on teachers’ levels of well-being, emotions, and motivation. A total of 936 Spanish teachers participated in this nationwide survey from all educational levels, thus allowing comparison among levels, which is a novelty and strength of our study. Four aspects were explored: (1) instructional adaptation to ERT; (2) well-being changes and the main challenges in this regard; (3) changes in emotions; and (4) changes in motivation and the main factors. Importantly, we explored a number of teacher characteristics (e.g., gender, age) for the three last aspects. Our results show that teachers felt the impact of ERT on their well-being, emotions, and motivation. Additionally, female teachers, teachers with students of low socioeconomic status (SES), in public schools, and primary and secondary teachers were the most affected groups. This indicates that the impact of ERT differed and some populations of teachers are more at risk of suffering burnout because of ERT.

This manuscript may not exactly replicate the published version due to editorial changes and/or formatting and corrections during the final stage of publication. Interested readers are advised to consult the published version.

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