# An Early Warning System to Detect At-Risk Students in Online Higher Education
## 2 Related Work  
### Predictive Models  
As described in different systematic reviews [4; 5], many models can be applied to education. Students' performance [6], students' dropout within an individual course [7; 8], program retention [9], recommender systems in terms of activities [10], learning resources, [11] and next courses to be enrolled [12; 13] are some examples of the application of those models. Independently of the desired outcome, models have used many different types of data in order to perform the predictions. Different variables (or features) have been explored ranging from demographic data [14] (e.g., age, gender, ethnic origin, marital status, among others), self-reported questionnaires [15; 16], continuous assessment results [17; 18], user-generated content [19] to LMS data [20; 21]. Numerous classification algorithms have been analyzed through the proposed predictive models. Decision Tree (DT) [22], Naive Bayes (NB) [23], Support Vector Machine (SVM) [24], Logistic Regression [25; 26; 27], Hierarchical Mixed models [17; 28], K-Nearest Neighbors (KNN) [26], Neural Network models [29], or Bayesian Additive Regressive Trees [30] are some examples of the employed techniques. As previously mentioned, we focus on identifying at-risk students by checking the likelihood to fail a course following the claims of other researchers [24; 25; 31] to create specific course predictive models. A simple model based on grades of the continuous assessment activities (named GAR model) is used instead of using a complex model. Activities performed by students throughout their learning process are indicators of the performance they will reach in the future. Although we use this simple model, we have comparable results with other predictive models to detect at-risk students. Table 1 illustrates a comparison of the predictive GAR model with other related works where more complex models were proposed. The table shows for each comparison, the intervention point in the semester timeline (_Semester Intervention Point_), the compared accuracy metric (_Accuracy Metric_), the value of the metric in the referenced work (_Value Metric Ref._), the LOESS regression of the GAR model at the intervention point in the complete set of courses at our university (_LOESS Regr. GAR whole institution_), and the percentages of courses with a metric value larger than the metric of the referenced work (_Perc. Courses Metric GAR > Metric Ref._). Authors in [23] obtained an F\({}_{1,5}\) (F-score) of 62.00% with Naive Bayes at 40% of the semester timeline; meanwhile, our model reaches a LOESS regression value of 78.55% on average in the whole courses at our university at the same point of the semester timeline. When courses are evaluated individually, we found that more than 70% of the courses have an F\({}_{1,5}\) larger than 62.00% at this point of the semester. Authors in [25] reached a TNR (True Negative Rate) of 75.40% at the end of the course meanwhile the GAR model reaches a LOESS regression value of 97.56%, and more than 99% of the courses have a TNR larger than 75.40%. Authors in [30] computed the MAE (Mean Absolute Error) at 40% of the semester timeline. They reached a MAE value of 0.07, whereas the GAR model has a LOESS value of 0.08, and more than 64% of the courses have a MAE smaller than 0.07 at this point of the semester. The approach presented in [32] reached a TPR (True Positive Rate) of 81.00% at the 40% of the semester timeline while our model obtained a LOESS regression value of 80.46% but more than 40% of the courses have a TPR larger than 81.00% at this point of the semester. On the approach presented in [33], where SMOTE was used in order to balance classes, the AUC (Area Under The Curve) ROC (Receiver Operating Characteristics) curve was used to evaluate the model. The best model reached a ROC value near to 91.00% at 60% of the semester timeline. The GAR model without SMOTE has a LOESS value near to 93.00% and more than 59% of the courses have a ROC value larger than 91.00% at this point of the semester.