# On Data Protection Regulations, Big Data and Sledgehammers in Higher Education
## 1 Introduction  
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:  
1. Data must be collected for specified, explicit and legitimate purposes and not further processed in a manner that is incompatible with those purposes. Archiving purposes in the public interest, just like scientific or historical research purposes or statistical purposes, are not considered incompatible. 2. Data must be adequate, relevant and limited to what is necessary in relation to the purposes for which they are processed. These requirements on purpose and data minimization have been part of the global debate about data protection rights for years [1], with some stances criticizing the fact that they may create unnecessary/unwanted barriers to trade or to unforeseen uses of the information that would bebeneficial for the data subjects [2; 3]. 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. We have tackled this question in a context that is not up to the highest standards of fair data use, namely that of Ecuadoran universities, which are representative of common practices held throughout the whole of Latin America. In particular, we have worked with the data gathered by Universidad Politecnica Salesiana (henceforth, UPS) over more than 20 years in the campuses of Cuenca, Guayaquil and Quito, containing profuse information about more than 6000 students. The managers of this institution--motivated by the positive findings of the studies cited above--were interested in analyzing the data, with the expectation of detecting useful relationships among socio-economic variables (e.g., familiar income, health-related conditions, places of origin/residence,...) and metrics of academic performance. Whereas the previous works always fed academic data to the AI (in some cases, along with other data like personality traits [10] or demographic features [13]), we made an experiment to assess the predictive value of socio-economic data alone, in order to better inform the theory in this area of research. Our experiment consisted in performing a scan of hyperparameters for a Multi-Layer Perceptron (MLP) neural network, in search for the configuration that attained the greater accuracy in predicting academic outcomes from the socio-economic data. We chose the MLP for being one of the best understood machine learning models, commonly used in the related literature [18; 19]; its best configuration would be used as a benchmark for the comparison of other techniques, including the ones used in References [7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17] and more advanced neural network schemes. However, the scan of hyperparameters revealed no correlations or dependencies between the input variables and the chosen metrics in any case, showing that--at least for the UPS and alike settings--there is no actual gain from applying machine learning techniques on extensive socio-economic data. This finding yields valuable observations in relation to the hype about AI in Higher Education management and the convenience of modern data management policies. The paper is organized as follows. First, Section 2 explains how the UPS data were processed, to be fed into the MLP or to assess its outputs. Section 3 describes the criteria for the preparation of the scan of hyperparameters.