Abstract
Universities in Latin America commonly gather much more information about their students than allowed by data protection regulations in other parts of the world. We have tackled the question of whether abundant socio-economic data can be harnessed for the purpose of predicting academic outcomes and, thereby, taking proactive actions in student attention, course planning and resource management. A study was conducted to analyze the data gathered by a private university in Ecuador over more than 20 years, to normalize them and to parameterize a Multi-Layer Perceptron neural network, whose best-performing configuration would be used as a benchmark for the comparison of more recent and sophisticated Artificial Intelligence techniques. However, an extensive scan of hyperparameters for the perceptron—exploring more than 12,000 configurations—revealed no significant relationships between the input variables and the chosen metrics, suggesting that there is no gain from processing the extensive socio-economic data. This finding contradicts the expectations raised by previous works in the related literature and in some cases highlights important methodological flaws.
Highlights
Many countries are implementing data protection regulations by which any personal data collected by public or private entities must be handled according to two general principles: Data must be collected for specified, explicit and legitimate purposes and not further processed in a manner that is incompatible with those purposes
If the value of training accuracy is greater than test accuracy, it can be asssumed that the neural network has overfitted
We have run one experiment on the potential use of neural networks for the detection of correlations and dependencies among the diverse data fields stored in databases with thousands of records of Higher Education students, focusing on whether socio-economic variables, familiar and health-related conditions and places of origin/residence could influence metrics of academic performance to a statistically-significant extent
Summary
Many countries are implementing data protection regulations by which any personal data collected by public or private entities must be handled according to two general principles: Data must be collected for specified, explicit and legitimate purposes and not further processed in a manner that is incompatible with those purposes. Higher Education institutions have not been alien to the discussions, as some scholars argued that gathering as much information as possible about university students, professors and administration staff could enable deep analysis and, thereby, proactive actions in student attention, course planning and resource management [4,5,6] In this line, there have been numerous studies in the recent past about predicting student outcomes using Artificial Intelligence (AI) techniques [7,8,9,10,11,12,13,14,15,16,17] and it is generally assumed that the more abundant the data, the more accurate the predictions
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