## 4 Discussion  
In this section, we are going to discuss our findings from two perspectives: (1) findings that can be generalized to any dataset and (2) findings specific to our dataset. Looking at Table 10, which summarizes the best ML models per term, one can see the answer to the research question, namely, which representation of college academic performance is more relevant for predicting student graduation academic performance. When the models are compared based on how the academic performance is defined, that is whether _by courses_ or _by GPA_ as explained in Section 2.2, we notice, in general, that the models where the academic records are used _by GPA_ achieve higher accuracy scores at the later terms whereas the models where the academic records are used _by courses_ achieve higher accuracy scores at the earlier terms. To be more precise, we can see in Table 10 that course grades yields better results until the end of term 2, and GPA gives better results afterwards. However, in an attempt to gain more insight, we analyzed the variance of the training and testing performance of ML models. We observed that after term 2, the course models all had a higher variance. For example, Figure 9 illustrates the train and test accuracy scores by the number of experiments of all ML models on term 6. In all the plots, it is clearly visible that the difference between the green and the red curves (train and test of the GPA models) is less than the difference between the blue and the orange curves (train and test of the Course models). This indicates that if we introduce academic performance into the models _by courses_, in later terms, the models learn the noise (i.e., overfitting), which results in performance loss. This is not the case for GPA models in later terms, since one GPA per term would replace several (up to six at IAU-CCSIT) individual course grades, thus rep \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **Term 1** & **Term 2** & **Term 3** & **Term 4** & **Term 5** & **Term 6** \\ \hline Random Guess & 0.333 & 0.333 & 0.333 & 0.333 & 0.333 & 0.333 \\ \hline Naive Model & 0.539 & 0.618 & 0.671 & 0.736 & 0.734 & 0.742 \\ \hline Best cumulative ML Performance & 0.656 & 0.709 & 0.803 & 0.874 & 0.922 & 0.949 \\ \hline Best cumulative ML Model & LR & LR & LR & LR & LR & LR \\ \hline Academic performance & by course & by course & by GPA & by GPA & by GPA \\ \hline Scenario & S1 & S3 & S1 & S4 & S3 & S1 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Summary of the best models per term. Figure 8: Accuracy plots along the terms of the ML Models with the academic, demographic, and pre-college features. information with reduced size of academic performance features. On the other hand, if we introduce the academic performance into the model as GPA at earlier terms, then we are over-simplifying the model by reducing the size of the academic features to a single number per term. We expect this observation to hold in any academic dataset and thus conclude that for earlier predictions academic performance should be used _by courses_ and for later predictions _by GPA_. The conducted experiments also allow answering several common EDM questions with respect to the specific IAU-CCSIT dataset. Our experiment results show that all the models perform better than a random guess (which can be considered to be roughly 33.3%). Moreover, ML models perform significantly better than the naive models that we defined in Section 2.3 as our base line result. We can conclude that the prediction models can be used as early as by the end of term 1, knowing that delaying the prediction thus gathering more academic data about the student will improve the performance of the classifier. Within ML models, LR shows the best overall performance with GNB being the runner up. As for the highest performance, both LR and GNB records 94.9% accuracy using all six current GPAs cumulatively. The poor performance of RFC can be explained by the overfitting which is very clear from Figure 9c,d where training performance reaches 100%.