# On Data Protection Regulations, Big Data and Sledgehammers in Higher Education
## 5 Discussion vs. Related Work  
As noted in the introduction, the challenge of predicting students' performance in Higher Education institutions has been addressed by many authors in the recent past, using more or less abundant and fine-grained data of different types (always including, at least, academic data) and employing different machine learning techniques. The following are the most relevant highlights from the literature:  
* The authors of Reference [7] ran a comparative study on a dataset of 257 student records, showing that Bayesian networks (76.8% accuracy) outperformed decision trees (73.9%) and these in turn outperformed the Multi-Layer Perceptron (71.2%). * Similarly, a study was presented in Reference [8] with data about 280 students, making predictions with 10 off-the-shelf algorithms implemented in the Weka data mining framework ([[https://www.cs.waikato.ac.nz/ml/weka/](https://www.cs.waikato.ac.nz/ml/weka/)]([https://www.cs.waikato.ac.nz/ml/weka/](https://www.cs.waikato.ac.nz/ml/weka/))). The Naive Bayes classifier was found to be the best predictor (65% accuracy). * Another comparison was made in Reference [9] on a dataset containing 225 student records, with 10 attributes of academic performance each. Once again, a Bayesian network (92% accuracy) turned out to be slightly better than other classifiers (Naive Bayes, ID3 and J48) and than the Multi-Layer Perceptron. * [PERSON] et al. conducted a study including some social and emotional parameters in the students' profiles, which the evaluation showed to be much less relevant to the predictions than the records of previous academic results. Random Tree happened to be the most accurate algorithm [10]. * Some socio-economic data--a small subset of the fields than ours (Table 1)--were used in Reference [11] to make predictions from only 165 student records by using several techniques. The Multi-Layer Perceptron (74.8% accuracy) stood out above NBTree (73%), REPTree (71%) and others. * The authors of Reference [12] applied a range of classifiers and clustering methods on the academic results of 480 students in order to predict the outcomes of 25 others in new subjects, taking as \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Learning Rate** & \(\mathbf{\alpha}\) & **Optimizer** & **Activation Function** & **Hidden Layers** & **Training Accuracy** & **Test Accuracy** \\ \hline
0.01 & 0.48 & [PERSON] & cReLu & (560) & 1 & 0.825 \\
0.01 & 0.12 & [PERSON] & [PERSON] & (160) & 1 & 0.815 \\
0.01 & 0.576 & [PERSON] & [PERSON] & (128) & 0.965 & 0.83 \\
0.1 & 0.01 & SGD & tanh & (200) & 0.785 & 0.81 \\
0.1 & 0.1 & SGD & tanh & (200) & 0.734 & 0.82 \\
0.001 & 0.01 & Adadelta & ReLu & (200,200) & 0.5225 & 0.832 \\
0.01 & 0.21 & [PERSON] & cReLu & (81,81) & 1 & 0.81 \\
0.01 & 0.4 & SGD & cReLu & (320) & 0.87 & 0.82 \\
0.1 & 0.025 & SGD & cReLu & (104) & 0.955 & 0.825 \\
0.001 & 0.358 & [PERSON] & [PERSON] & (104) & 0.99 & 0.825 \\
0.01 & 0.0358 & [PERSON] & [PERSON] & (104) & 1 & 0.81 \\
0.1 & 0.25 & [PERSON] & [PERSON] & (163,163) & 1 & 0.82 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Details of the MLP configurations represented in Figure 2.  
input the recent grades obtained by the latter in the preceding semesters too. They attained an accuracy of 80% in classifying the students' performance as low, medium or high. * [PERSON] and [PERSON] [13] used the J48 classifier to predict (with 85.8% accuracy) the students' probability of abandoning the University at the end of the year, working with some demographic variables as well as from the results of preceding semesters. This study used--to the best of our knowledge--the most extensive dataset in the literature to date, with records of 1980 students. In relation to these precedents, plus those of References [14; 15; 16; 17], our work involves the most extensive and fine-grained dataset of student records (30 times as many as the average from above).