## 2 Summary of the Contributions in This Special Issue
The first article in this group is entitled \"Predicting Students Success in Blended Learning-Evaluating Different Interactions Inside Learning Management Systems\" [5], by [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON]. The authors apply ML techniques for detecting at-risk students earlier in courses involving algorithms and programming topics in undergraduate programs, where dropout and failure rates are usually high. This research finds the best combination of datasets collected from Moodle (considering cognitive, social and teaching presence) and classification algorithms. The best ML model was able to detect students at-risk in the first week of the course. The second article in this group is entitled \"The Machine Learning-Based Dropout Early Warning System for Improving the Performance of Dropout Prediction\" [6], by [PERSON] and [PERSON]. This researc tries to identify students who are at risk of dropping out of school. To this end, the authors developed a dropout early warning system characterized by two features. On the one hand, the system addresses the class imbalance issue using the synthetic minority oversampling techniques (SMOTE) and the ensemble methods in ML. On the other hand, the system evaluates the trained classifiers with both receiver operating characteristic (ROC) and precision-recall (PR) curves. The authors trained random forest, boosted decision tree, random forest with SMOTE, and boosted decision tree with SMOTE by using large datasets. Among these ML techniques, boosted decision tree obtained the best results. The third article in this group is entitled \"A Learning Analytics Approach to Identify Students at Risk of Dropout: A Case Study with a Technical Distance Education Course\" [7], by [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON]. This article continues in the line of the early prediction of at-risk students. Here, the authors consider students' interactions with the virtual learning environment as data source. With the goal of maximizing the prediction results, the authors apply an elitist genetic algorithm for tuning the hyperparameters of some classifiers: classic decision tree (DT), random forest (RF), multilayer perceptron (MLP), logistic regression (LG), and the meta-algorithm AdaBoost (ADA). The fourth article in this group is entitled \"An Early Warning System to Detect At-Risk Students in Online Higher Education\" [8], by [PERSON], [PERSON], [PERSON], and [PERSON]. The authors centered the research effort on finding accurate predictive models to identify at-risk students. The authors considered several classifiers, whose prediction quality was evaluated by a proposed method. Furthermore, they developed an early warning system tested in a real educational setting, which demonstrated accuracy and usefulness for detecting at-risk students in online higher education. This system shows different dashboards where students and teachers can analyze the information and perform some interventions to reduce at-risk situations. The fifth article in this group is entitled \"Automated Assessment and Microlearning Units as Predictors of At-Risk Students and Students' Outcomes in the Introductory Programming Courses\" [9], by [PERSON] and [PERSON]. This work predicts at-risk students particularly in the context of introductory programming courses, where some students with limited programming skills can be discouraged by key aspects as the ability to think abstractly, solve problems, and design solutions. This work analyzed the automated source code assessment of assignments and the implementation of a set of microlearning units as predictors of at-risk students and students' outcomes. The authors found a significant contribution of automated code assessment in students' learning outcomes and proved a certain dependence between the students' activity and achievement in the activities and final students' outcomes. With regard to intelligent analysis of different learning aspects, we find seven contributions.