# On Data Protection Regulations, Big Data and Sledgehammers in Higher Education
## 6 Conclusions and Future Work  
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, specifically 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. The context of Universidad Politecnica Salesiana was considered a propitious one because, in line with common practice throughout Latin America, the records contain fields that would raise concerns under the scrutiny of the most advanced data protection regulations. Our experiment was set up in a way that would bring to light, at least, the most noticeable relationships between a selection of input variables--presumably, the ones entailing greater opportunities to find something--and the chosen metrics, which looked at academic performance with coarse granularity. From those findings, we would move on to refined neural network designs and more detailed metrics, aiming to make predictions about new students coming every year from the experience accumulated with many others in the past. Contrary to the initial expectations, however, the scan of hyperparameters for the multi-layer network of perceptrons (including settings that fall within the denomination of _deep learning_) showed that there was no correlation or dependency between the many input variables of socio-economic nature and any of the chosen metrics of academic performance. Given the space of possibilities we explored and the size of the dataset, this finding is not about the particular tool provided by the MLP but rather about the nature of the data: there is no additional knowledge to be extracted from the abundant socio-economic data and no other technique would get to find a function mapping inputs to outputs, that could underpin some decision-making at the University. Still, before rushing into the conclusion that this might be the case for all universities in the region--which would extrapolate almost directly to other countries where universities do not gather so extensive data about their students,--we must consider the hypothesis that there might be an implicit pre-filters in place, by which the records of students in the UPS databases conform a uniform population in socio-economical and academic terms: on the one hand, UPS is a private university with a cost of enrollment, tuition and fees that ranges from USD 1765 to USD 2432, which are significant amounts of money for families in Ecuador; on the other, the teaching staff systematically strive to help students pass their subjects. The more uniform the population, the lower the probability of finding meaningful correlations or dependencies among data fields. Accordingly, we have started negotiations in order to check this hypothesis by performing a similar study in the context of an Ecuadoreen public university, where the costs and the pass/fail rations are lower. If we got the same results, the study would further inform the theory in this area of research and reinforce the fact that the widespread adoption of advanced data protection regulations is not hampering potential uses of AI to improve the Higher Education systems but just preventing misuses of personal data as intended. For future research, we also hypothesize that the data fields currently handled by UPS (listed in Table 1) could be supplemented with the students' history in secondary education. It could be valuable to match fine-grained info about related subjects (e.g., on different branches of science) or even specific topics within them. For instance, a student who was skillful with trigonometry but had difficulties with derivatives would be likely to do better in Algebra-related courses than in Physics.