## 5 Experimentation and Results
### Configuration of Classification Algorithms  
The experimentation of this is designed to offer a fair comparison between MIL and the traditional single-instances paradigm evaluating the same metrics in the same problem with the same information and in the same wide set of algorithms, part of the state of the art in supervised learning. They have been selected 23 algorithms considering the  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Code** & **Attributes** \\ \hline @relation assignments-course & \(1\times\) assignment\_type \\  & \(1\times\) assignment\_weight \\  & \(1\times\) assignment\_advance \\  & \(1\times\) assignment\_score \\  & \(1\times\) assignment\_banked \\  & \(1\times\) attribute assignment\_weight \\  & \(1\times\) attribute assignment\_aware \\  & \(1\times\) attribute \\  & \  
main paradigms of machine learning and the most popular methods for predicting student performance (see Section 2.2).  
The experimentation has been developed using Weka [49], a framework for machine learning in Java. In order to ensure solid evaluation, each experiment is executed with a 10-fold cross-validation. In addition, stochastic algorithms are executed 5 times with different seeds, having a total of 50 executions per algorithm and course. The datasets in ARFF format ready to be used in Weka have been published in open access mode in the web repository associated to this paper ([[http://www.uco.es/kdis/mildistanceeducation/](http://www.uco.es/kdis/mildistanceeducation/)]([http://www.uco.es/kdis/mildistanceeducation/](http://www.uco.es/kdis/mildistanceeducation/)), accessed on 23 October 2021). In order to easily reproduce the experimentation, this section presents the studied algorithms as well as their configurations. Since the purpose of this study is to compare types of learning under equal conditions, the configuration of the predictive algorithms should not favor one or the other representation paradigm. Thus, these configurations has been chosen based on the default settings that the authors specified according to the Weka workbench [49], where more information can be consulted.  
The 23 predictive algorithms and their configurations are listed bellow:  
* Methods based on trees : Decision Stump [50], J48 [51], Random Tree [52] and Random Forest [53]. See configurations in Table 7.
* Methods based on rules: ZeroR [49], OneR [54], NNge [55], PART [56] and Ridor [57]. See configurations in Table 7.
* Method based on Bayesian models: naive Bayes classifier [58]. See configuration in Table 7.
* Methods based on logistic regression: the algorithm considered in this paradigm is the proposal of [PERSON] [59]. See configuration in Table 7.
* Methods based on Support Vector Machines (SVM): LibSVM [60], SGD with SVM as loss function [61], SMO with polynomial kernel [62] and SPegasos [63]. See configurations in Table 8.
* Methods based on Artificial Neural Networks (ANN): Multilayer Perceptron [64] and RBFNetwork [64]. See configurations in Table 8.
* Methods based on ensembles: AdaBoostM1 [65] and Bagging [66]. These meta-algorithms have been used with three distinct base classifiers previously commented: Random Forest, PART and Naive Bayes. See configurations in Table 8.