## 6 Experimental Process and Results  
To conduct our experiments, we exploited the sci-kit Python library along with its integrated regressors and an implementation of computing the necessary Shapley values [37,54]. In order to systematically examine the efficiency of the extended COREG variant over the problem of early prognosis on student's performance, various choices of instance-based selectors and different learning model for the case of the regressors were chosen. Furthermore, we investigated two separate cases of the total dataset based on the measured indicators: Regarding only the first semester (\(D_{1}\)-first scenario) and only the first two semesters (\(D_{2}\)-second scenario). Thus, our predictions excuse the characterization of the early prognosis task, providing in time predictions using indicators that stem from the initial stages of an academic year. To be more specific, the size and the attributes of each view per dataset-scenario are reported here:  
* First scenario: \[D_{1}=F_{1}\cup F_{2}\] \[|F_{1}|=4,\ F_{1}=(\textit{gender},\textit{NewStudent},\textit{Ocs}_{1}, \textit{Wri}_{1})\] \[|F_{2}|=10,F_{2}=(L_{1},V_{11},V_{21},V_{31},V_{41},V_{51},P_{11},P_{21},P_{3 1},P_{41})\]
* Second scenario: \[D_{2}=F_{1}\cup F_{2}\] \[|F_{1}|=6,F_{1}=(\textit{gender},\textit{NewStudent},\textit{Ocs}_{1}, \textit{Wri}_{1},\textit{Ocs}_{2},\textit{Wri}_{2})\] \[|F_{2}|=20,F_{2}=(L_{1},L_{2},V_{11},V_{12},V_{21},V_{22},V_{31},V_{32},V_{41}, V_{42},V_{51},V_{52},P_{11},P_{12},P_{21},P_{22},P_{31},P_{32},P_{41},P_{42})\]  
Besides the multi-view role of our extended COREG framework, the diversity of the SSR algorithm is enriched by the fact that each selector\({}_{i}\) cannot select during one iteration the same \(x_{j^{\prime}}\) instance, while during the initial design of the COREG, randomly selected subsamples of the original \(U\) set were selected per iteration. Although we also attempted to implement this strategy, our results were constantly worse than the case of exploiting the full length of the original \(U\) set. This is probably due to the relatively small size of our total problem \(D\), which we hope to undertake during the next semesters to enrich our collected data. As it regards the choice of the investigated selectors and regressors for the extended COREG framework, we mention here all the different variants/models that were included in our experiments:  
* (selector1, selector2): We have selected kNN algorithm for detecting the appropriate neighbors and fitting appropriate models. Following the original COREG scheme for injecting further diversity between the two separate views, we kept different power parameter for the internal distance that is exploited for formatting the neighborhood. Thus, we used Euclidean distance and Minkowski of 5 th power for first and second selector, respectively. Moreover, we examined four separate cases based on the number of the nearest neighbors that are considered per case: (k\({}_{1}\), k\({}_{2}\)) \(\equiv\) (1, 1), (1, 3), (3, 1), and (3, 3). * (regressor1, regressor2): A different pair of same models have been used for this choice. To be more specific, we have used kNN with \(k=3\), a typical Linear regressor (LR), the Gradient Boosting regressor which is an additive model that operates under a forward-stage manner with 2 different loss functions: Least squares regression (ls) and 'huber'--a hybrid between ls and least absolute deviation--which are depicted as GB(ls) and GB(huber) and multi-layer perceptron that optimizes the squared-loss function by using the 'lbfgs' quasi-Newton solver. The last regressor is denoted as MLP, while its default hyperparameters were used: The 'Relu' as activation function and a hidden layer with 100 neurons.