## 4 Survey Results
### Predictive Models of Learning Outcomes  
In learning analytics, predictive modeling focuses primarily on improving the accuracy of student performance predictions. In contrast, explanatory modeling focuses on identifying and explaining the factors that lead to the predicted achievements of students [105]. The intelligent models suggested for predicting learning outcomes were mainly predictive in nature (52 studies, 87.09%), with only ten models (16.12%) trying to explain the predictions by linking them to the exact features leading to the observed performance, i.e., [46; 47; 48; 62; 77; 82; 85; 87; 95; 102]. Fifty-four (87.70%) studies employed single intelligent models for predicting the attainment of learning outcomes. Remarkably, only eight studies (i.e., [60; 65; 66; 80; 84; 93; 96; 101]) explored the use of hybrid intelligent models to improve the accuracy of academic perfor \begin{table}
\begin{tabular}{c c c} \hline \hline
**Learning Outcome Type** & **Number of Occurrences** & **Studies** \\ \hline Performance classes & & [45; 46; 55; 59; 70; 73; 77; 79; 80, 83; 89; 91; 93; 94; 100; 101; 103] \\ (Categorical; Binary, nominal and ordinal) & & \\ \hline Achievement/grade scores & & \\ (Continuous; Interval) & 20 & [43; 44; 50; 51; 52; 53; 54; 55; 56; 57; 78; 81; 82; 84; 90; 92; 96; 97; 102] \\ \hline Perceived competence and achievements & & \\ (Continuous; Interval scale) & 5 & [47; 57; 71; 72; 97] \\ \hline Self-reports about educational aspects & 3 & [56; 81; 97] \\ (Continuous; Interval scale) & & \\ \hline Failure/dropout/graduation rates & 3 & [48; 49; 95] \\ (Continuous; Ratio) & & \\ \hline Other (e.g., college enrollment, careers, time to & 6 & [45; 48; 49; 56; 58; 66] \\ graduate, attendance,... etc.) & & \\ \hline NS & 1 & [104] \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Distribution of studies based on the type of learning outcomes predicted. Figure 11: Distribution of student performance class labels predicted in the models predicting learning outcome as standings (i.e., ranks). mance predictions. Hybrid or ensemble classifiers involve the integration of heterogeneous learning techniques to boost the predictive performance [106]. Table 7 categorizes the 62 articles according to the intelligent learning genre they implemented to predict academic performance. Overall, five types of predictive analytics emerged with statistical models appearing the most (28 studies, 45.61%), followed by supervised learning models (25 studies, 40.32%). The use of unsupervised learning alone appeared only in one study [67]. We delved into the types of intelligent methods and algorithms that are used to forecast the attainment of student outcomes, and clustered the proposed models into six categories. Table 8 shows that regression analysis was the most frequently used (51.61%) prediction techniques. Artificial neural networks and tree-based models came into the second position, making together a total of 29.02%. Bayesian approaches made only 8% of the predictive models. Notably, support vector machines were employed in two studies (i.e., [59; 60]). The literature postulates several metrics to evaluate the proposed machine learning models [2; 3; 5]. Accuracy is probably the most popular metric employed to judge the effectiveness of a predictive model. Accuracy refers to the ratio between the correctly predicted outputs over the total predictions. Other evaluation metrics include precision, recall, ROC-AUC, R Square, F1-score, among others. The surveyed papers used multiple performance metrics to evaluate the quality of their student performance predictions. Twenty-one (33.87%) studies used a single metric to evaluate the prognosis of learning outcomes. Seven studies (11.29%) used two metrics, while seven other studies (11.29%) used three performance metrics. Notably, two studies used five evaluation metrics, including accuracy, kappa, AUC, sensitivity, specificity, relative absolute error (RAE), relative