Abstract
In this article we propose an automatic system that informs students of abnormal deviations of a virtual learning path that leads to the best grades in the course. Our motivation is based on the fact that by obtaining this information earlier in the semester, may provide students and educators an opportunity to resolve an eventual problem regarding the student's current online actions towards the course. Our goal is therefore to prevent situations that have a significant probability to lead to a pour grade and, eventually, to failing. Our methodology can be applied to online courses that integrate the use of an online platform that stores user actions in a log file, and that has access to other student's evaluations. The system is based on a data mining process on the log files and on a self-feedback machine learning algorithm that works paired with the Moodle LMS. Our results shown that it is possible to predict grade levels by only taking interaction patterns in consideration.

Abstract

Abstract
The European Union (EU) has been promoting an integrated approach to climate protection and energy policy, through a set of key objectives for 2020, 2030 and 2050, linking Europe’s green agenda with its need for energy security and competitiveness. This paper aims to evaluate the environmental efficiency of European Countries from 2010 to 2015 towards 2020 targets, through a Data Envelopment Analysis (DEA) model. The DEA model assesses the ability of each country in minimizing current resources while maximizing the gross domestic product (GDP) and minimizing undesirable outputs, such as GhG emissions. The DEA model is based on Directional Distance Function (DDF), imposing weak disposability for the undesirable output (UO). Results obtained show that globally, in the period under analysis, the EU has increased its environmental efficiency which is consistent with the analysis of the indicators of the 2020 climate and energy package. © 2019, Springer Nature Switzerland AG.

Abstract
For the majority of students, assessment moments are associated with significant levels of stress and anxiety. While a certain amount of stress motivates the individual and improves performance, too much stress will have the contrary effect. Stress has therefore a fundamental role on student performance. It should be the educational organizations' mission to understand the underlying mechanisms that lead to performance anxiety and provide their students with the best coping tools and strategies. In the present study we analyze student behavior during e-assessment in terms of mouse dynamics. Two major behavioral patterns can be identified, based on ten features that quantify the performance of the student's interaction with the computer: (1) students who are able to sustain performance during the exam and (2) students whose performance varies significantly. Data shows that the behavior of each student during the exam correlates strongly with the time it takes the student to complete it. Several classifiers were trained that predict the completion time of each exam based on the students' interaction patterns. Two of them do it with an average error of around twelve minutes. Results show that there are still mechanisms that can be explored to better understand the complex relationship between stress, performance and human behavior, that can be used for the implementation of better stress detection, monitoring and coping strategies.

Abstract
Currently smartphones are used extensively by students. Thus, their proper use within teaching/learning activities is a reality which needs to be addressed. How teachers and students without advanced programming skills can develop their own mobile devices applications? An attempt to answer this question is presented in control engineering teaching and learning activities. It will be shown that the MIT App Inventor 2 provides an excellent tool to develop simple applications for Android based mobile devices. Examples of applications designed both by lecturers and students using the MIT App Inventor 2 are described, outlining different uses and features. A case study of using applications to support teaching/learning of PLC programming is presented.

Abstract
Electrical, heating and cooling energy demands of the end users are increasing day by day. For the sake of using fewer fossil fuels, decreasing the energy costs and gas emissions as well as increasing the efficiency and flexibility of the traditional energy systems, multi-energy systems (MESs) have begun to be used. In this study, a MES structure which also includes renewable-based generation units as suppliers together with combined heating and power (CHP) and heat pumps (HPs) is presented. The proposed MES structure is modelled as a mixed integer linear programming (MILP) problem with the objective of minimizing total gas and electricity costs in daily operation. Furthermore, electric vehicles (EVs) as a new type of electrical load with inherently different characteristics are evaluated considering different end-user types as residential and commercial together with the capability of offering operational flexibility. In order to tackle with the intermittent structure of the renewable energy sources, a scenario oriented stochastic programming concept is taken into account by addressing real radiation, temperature, and wind data. Moreover, actual time-of-use (TOU) tariffs for electricity prices along with the real gas prices are evaluated. The simulation results of the devised model are given for different case studies and the effectiveness of the system is demonstrated via a comparative study. As a result, it is found that the operational costs are decreased nearly 5.49% by integrating only photovoltaic (PV) production according to the case which has no additional sources. Also, a substantial reduction of 13.45% is achieved by considering both PV and wind generation. Moreover, the flexibility is increased with taking EVs into account on the demand side and this leads to a cost reduction of 8.81% even if EVs are integrated to the system as an extra load.