## 2 Summary of the Contributions in This Special Issue  
Although each paper published in this Special Issue covers different topics, we can identify three groups where the papers can be classified according to their main focus: performance and behaviour prediction, dropout and risk prediction, and intelligent analysis of different learning aspects. However, some of these papers could be classified into more than one of these groups. Finally, a review article is also provided to get a wide perspective of this field. With regard to performance and behaviour prediction, we find four contributions. The first article in this group is entitled \"Implementing AutoML in Educational Data Mining for Prediction Tasks\" [1], by [PERSON], [PERSON], [PERSON] and [PERSON]. This research focuses on examining the potential use of advanced ML strategies on educational settings from the perspective of hyperparameter optimization, because of the complexity of ML when it is applied for a given problem formulation and it must be optimally configured. To this end, the authors analyze the effectiveness of automated ML (autoML) for the task of predicting students' learning outcomes based on their participation in online learning platforms, limiting the search space to tree-based and rule-based models. After carrying out many experiments, the performance of AutoML is verified. This proposal allows educators and instructors in educational data mining (EDM) to perform experiments with good parameter configurations, thus achieving highly accurate results. The second article in this group is entitled \"Predicting Student Performance in Higher Educational Institutions Using Video Learning Analytics and Data Mining Techniques\" [2], by [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON]. The authors have developed a system for predicting student's overall performance at the end of the semester using video learning analytics and data mining techniques. They consider video-based learning with flipped teaching to improve student's academic performance. Particularly, the authors applied eight classification algorithms (where random forest obtained the best results) to data collected from the student information system, learning management system and mobile applications. Additionally, they used genetic search, principle component analysis, rule inducer and multivariate projection to improve different aspects of the study. The third article in this group is entitled \"Towards Portability of Models for Predicting Students' Final Performance in University Courses Starting from Moodle Logs\" [3], by [PERSON], [PERSON], and [PERSON]. This work focuses on the data sources rather than the prediction techniques. Particularly, the work studies the portability of prediction models obtained directly from Moodle logs, according to grouping similar courses by degree or level of usage of activities or using numerical or categorical attributes. To this end, the authors apply a classification algorithm to the datasets in order to obtain decision tree models and test their portability to other courses by comparing the obtained accuracies. The authors conclude that the prediction models can be transferred to different courses under some circumstances. The fourth article in this group is entitled \"Prediction of High Capabilities in the Development of Kindergarten Children\" [4], by [PERSON], [PERSON], [PERSON], and [PERSON]. This paper focuses on a type of the student's behaviour: the early detection of high capabilities, particularly at kindergarten, when the students are children. The prediction of such students is difficult, due to its low number and the focus of the teachers in the learning process. The authors propose a prediction algorithm based on Nearest Neighbor able to tackle this problem with satisfactory results. With regard to dropout and risk prediction prediction, we find five contributions.