## 2 Summary of the Contributions in This Special Issue
The first article in this group is entitled \"Predicting Students' Behavioral Intention to Use Open Source Software: A Combined View of the Technology Acceptance Model and Self-Determination Theory\" [10], by [PERSON], [PERSON] and [PERSON]. This work focuses on students' behavioral intention to continue using open source software (OSS) after be trained in it. This intention is predicted by applying Self-Determination Theory and the technological acceptance model (TAM). The dataset was built collecting data from a survey. The results obtained by the model confirmed the influence of the intrinsic motivations, autonomy and relatedness, to improve perceptions with regard to the usefulness of OSS and, therefore, on the intention to continue considering OSS. The second article in this group is entitled \"A Multi-Analytical Approach to Predict the Determinants of Cloud Computing Adoption in Higher Education Institutions\" [11], by [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON]. This work predicts the key aspects that influence on the managers of higher education institutions for adopting cloud computing as services provider. To this end, a variance-based structural equation modeling (PLS-SEM) and an artificial neural network (ANN) were applied to data collected from 134 managers involved in the decision making of the institutions. The PLS-SEM approch was used for extracting the significant relationships among the identified factors, whereas ANN ranked the normalized importance among those factors. It is interesting to know that technology readiness is the most important predictor for cloud computing adoption, followed by security and competitive pressure. Furthermore, the authors present an innovative approach useful for decision-makers to develop stategies for adopting cloud computing services. The third article in this group is entitled \"Predicting Student Grades Based on Their Usage of LMS Moodle Using Petri Nets\" [12], by [PERSON] and [PERSON]. In this paper, the data source is the popular learning management system (LMS) Moodle. This platform provides the information needed for analyzing the correlations between access to materials and the final grade in order to predict student's grades. According to the highest correlation, a model with Petri nets predicts what grade the student would get based on their usage of Moodle. The fourth article in this group is entitled \"The Relationship between the Facial Expression of People in University Campus and Host-City Variables\" [13], by [PERSON], [PERSON] and [PERSON]. The authors evaluate the public attitude towards university campuses and detect the relationship with host-city variables by using data about facial expression scores on social networks. It is interesting to know this attitude since it matters for the resource investment to sustainable science and technology. To this end, ML techniques area applied on datasets composed of 4327 selfies collected from social networks. The photos provide scores of happy and sad facial expressions and a positive response index was calculated. After analyzing some interesting results, the main conclusion is that people tend to show positive expression at campuses in cities with more education infrastructures but fewer residences and internet users. The fifth article in this group is entitled \"How to Extract Meaningful Insights from UGC: A Knowledge-Based Method Applied to Education\" [14], by [PERSON], [PERSON], and [PERSON]. Students and teachers are a rich source of user generated content (UGC) on social networks and digital platforms. The vast amount of this type of data can supply useful knowledge by extracting and visualizing samples of readily available content, particularly the tweets published in Twitter. The authors apply latent dirichlet allocation (LDA) to identify topics, which are then subjected to sentiment analysis by using ML and a data visualization algorithm for complex networks. This research allows practitioners to improve short-term education strategies and interventions.