## 5 Conclusions  
This work presented a comparative study aiming to find the best combination between dataset and classification algorithm (using and not using pre-processing algorithms) to early predict at-risk students in introductory programming courses. Thirteen dataset combinations together with five classification algorithms (k-Nearest Neighbor, Multilayer Perceptron, Naive Bayes, AdaBoost and Random Forest) were used in the experiments. The literature has works that also analyze log data from Moodle for generating predictive models for early identification of at-risk students, though the present work differs from them by providing a categorization of the counting of the logs according to the three elements required for a successful computer-mediated learning experience proposed by [PERSON] et al. [22], that is, cognitive, social and teaching presences. We tested to which extent the classification of the counting of the logs into these three dimensions would serve as better datasets for the generation of more accurate predictive models. The main idea was that the different classes of students (Approved versus Reproved) would interact differently in those dimensions of presence and that could help the models to better capture students behavior in the learning settings. However, results have shown that there is no improvement in the performance of the models using those three dimensions: cognitive, social and teaching presences. Because of that, one can assume that the simple counting of interactions can be used to generate predictive \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Semester**} & \multirow{2}{*}{**Combination**} & \multirow{2}{*}{\(p\)**-Value**} & \multirow{2}{*}{\begin{tabular}{c} **Median** \\ **(Oversample)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Median** \\ **(Questionnaire Data** \\ **with Oversample)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Questionnaire Data** \\ **with Oversample)**} \\ \end{tabular} \\ \hline \multirow{4}{*}{2016-1} & DB2—AdaBoost & 0.06890 & 0.59615 & 0.58974 \\ \cline{2-5}  & DB5—AdaBoost & 0.21254 & 0.63461 & 0.58974 \\ \cline{2-5}  & DB2—Random Forest & 0.00376 & 0.50641 & 0.50641 \\ \cline{2-5}  & DB5—Random Forest & 0.13058 & 0.47436 & 0.46795 \\ \hline \multirow{4}{*}{2016-2} & DB2—AdaBoost & 0.00506 & 0.51282 & 0.34615 \\ \cline{2-5}  & DB5—AdaBoost & 0.1488 & 0.42949 & 0.39102 \\ \cline{2-5}  & DB2—Random Forest & 0.0087 & 0.67949 & 0.71795 \\ \cline{2-5}  & DB5—Random Forest & 0.03328 & 0.63461 & 0.71795 \\ \hline \multirow{4}{*}{2017-1} & DB2—AdaBoost & 0.28199 & 0.54575 & 0.57189 \\ \cline{2-5}  & DB5—AdaBoost & 0.2510 & 0.51961 & 0.49346 \\ \cline{2-5}  & DB2—Random Forest & 0.0015 & 0.57516 & 0.66013 \\ \cline{2-5}  & DB5—Random Forest & 0.0533 & 0.63399 & 0.60457 \\ \hline \multirow{4}{*}{2017-2} & DB2—AdaBoost & 0.0000 & 0.75641 & 0.79487 \\ \cline{2-5}  & DB5—AdaBoost & 0.0000 & 0.75320 & 0.79487 \\ \cline{1-1} \cline{2-5}  & DB2—Random Forest & 0.2260 & 0.91987 & 0.95833 \\ \cline{1-1} \cline{2-5}  & DB5—Random Forest & 0.02940 & 0.91987 & 0.96154 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison between the ROC values on the normal data with oversample and data with oversampled plus questionnaire. models, corroborating with previous work [59]. This contradicts the findings of other authors, such as [PERSON] et al. [37] that say that predictive models cannot be generalized only by the LMS data logs and additional data sources are needed. Considering that our interest is to early predict at-risk students, we measured the performances of the models until the middle of the semester (8 weeks). It is possible to say that the models achieved performances that can be considered satisfactory (with AUC ROC values of 90% already in the first week) and it is similar to the results found in the literature, for example, [PERSON] et al. [25], [PERSON] et al. [46], [PERSON] et al. [31], and [PERSON] et al. [47].