# On Data Protection Regulations, Big Data and Sledgehammers in Higher Education
## 2 Selecting Inputs and Outputs to/from the MLP
Some parameters--such as nationality, country of birth or country of residence--were treated separately, because the number of records with values other than 'Ecuadorean' or 'Ecuador' was very low. While it was interesting to study the differences between nationalities, the convergence of the neural network would be hindered both in terms of speed and quality, while the majority of the estimations to make in the future would be for Ecuadoren students anyway. Besides, the choice of a given mobile operator was not considered relevant, even though it did exhibit some correlation with economic variables. * Dependent parameters. In a first approach, we decided to use the overall monthly expenses as the only indicator of economic level. Individual items of expenditure were considered in a second stage. Likewise, we did not consider at first the 'Diploma level' field, which only gets values when 'Has another diploma?' stores 'Yes'. * Missing or sparse values. We noticed that several fields about the students' origin (e.g., province or city) were not systematically filled in, which was not the case for residence data. We thought it was undesirable to handle fields with too many gaps, because we would have to assign some value during the normalization and there would be no clear policies to follow. * Dates. In general, temporal variables ought to be treated carefully before being used as input to a neural network, because their magnitude and range hampers normalization. Incorrect treatment can lead to overfitting and, thereby, to nullifying the informative value of the variables. (Overfitting happens when a neural network models the training data very accurately but fails to provide proper outputs for unknown data.) We used the normalized datum of the student's age at high school graduation. A categorization of dates, corresponding to different generations, would be of little interest because, obviously, all the intended predictions would be made for new records, corresponding to posterior dates. * High dimensionality parameters. Fields like 'High school of origin', 'Parish of residence' or 'Neighborhood of residence' take tens of different values. We did not consider them at first, if there were less granular fields conveying similar information. Thus, for example, in the first stages we used 'Type of high school', 'Province of high school' and 'City of residence' to assess the influence of the locations of pre-University studies and residence on the predictions. Likewise, 'Type of disability' was not considered because it took too many different values and the Boolean 'Has any disability' was used instead. * Infrastructure services. The students' enjoyment of potable water, sewage system, electricity supply, landline phone, Internet and cable TV was treated as an accumulated numerical value, from 0 to 6, instead of managing the 64 different combinations. We tried configurations in which the six variables were given the same weight and others in which water, sewage and electricity got double importance. As for the normalization methods, we applied the following criteria:
* Enumerated types: in general, the method that adds the lowest topological dispersion to the inputs of a neural network for enumerated types is one-to-K. As a shortcoming, the dimensionality of the network grows exponentially with each possible value. * Binary variables: for simplicity, we chose to encode Boolean variables as enumerated types of 2 values. * Numerical variables: we decided to use Gaussian normalization for the numerical fields, preprocessing the data (based on normalized median) in order to remove out-of-range values and false zeros that could have detrimental effects. For each record at the input, we had output data comprising the following numerical fields for each academic year:  
* Number of subjects taken. * Average qualification. * Number of subjects passed. * Number of subjects failed. We defined prediction metrics as a function of these parameters, plus the count of the number of years that each student stayed with the UPS.