Many studies have explored the contribution of different factors from diverse theoretical perspectives to the explanation of academic performance. These factors have been identified as having important implications not (Everson, 1995;Garson, 1998

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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.

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 ). 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.

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Introduction.Children with congenital hypothyroidism (CH) detected by newborn screening and adequately treated may have mild cognitive deficits. Objectives. To assess the intelligence quotient of children with CH and identify the presence of specific cognitive deficits. Population and methods. A group of 60 children with CH detected by newborn screening, who were aged 9-10 years old and received adequate treatment since their first month of life was selected and compared to a control group of 60 children without CH in the same age range. Inclusion criteria: children without concurrent diseases, who were attending school in a single shift, and whose parents had at least completed secondary education. The following tests were administered during individual interviews: the Wechsler Intelligence Scale for Children (third edition), the Rey complex figure test, the Woodcock-Muñoz revised test, the Conners Continuous Performance Test II, the Illinois Test of Psycholinguistic Abilities, the verbal fluency test, the Knox Cube Test, the Trail Making Test, the faces test, and the 5 digit test. The statistical analysis was done using Student's t tests (for independent samples) with Bonferroni's correction (p < 0.002). Results. Even within the normal average range, significant differences were observed between both groups in terms of total intelligence quotient and performance intelligence quotient (small and moderate effect sizes, respectively). In terms of performance, children with hypothyroidism had a significantly poorer performance in processing speed, reaction times, attention, cognitive flexibility, visuoconstruction, and long-term memory. No significant differences were found between both groups in the verbal area. Conclusions. Children with congenital hypothyroidism and without mental disability had mild cognitive deficits, which should be taken into account for a comprehensive patient care.

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Results-based financing has guided the development of policies with measurable results improving learning outcomes at micro/macro levels. However, it is then necessary to identify factors which predict early and accurately favorable or challenging conditions for learning. Learning outcomes depend on complex interactions between multiple variables, many of which are not fully understood. The objective was to develop valid and accurate models predicting low and high levels of math performance and Vietnamese language, using machine-learning algorithms, as part of an international large-scale project in primary education in Vietnam. The models achieved very high accuracy (95-100%). A strong common pattern has been found for both Math and Vietnamese language, for the low and high levels of performance: the individual cognitive characteristics, physical factors and daily routines/ activities of the child are very important predictive factors of academic performance, as measured by student performance in the final Grade 5 test in math and Vietnamese, respectively. Parental expectations, preschool attendance and school trajectory of students have added relative importance in the classification. In order to accurately identify an expected low or high academic performance outcome, it is the full pattern of variables contained in the vector of information from each case that should be considered. Because, although each variable in a particular vector has a small contribution to the total predictive weight, it is the overall pattern containing the interactions between these variables that carries the necessary information for the accurate predictions. In addition, the identification of specific patterns for extreme groups of performance provides the necessary guidance for more focused educational interventions/investment and sound educational policies.