## 1 Introduction  
The use of web-based education systems or e-learning systems has grown exponentially in the last years, spurred by the fact that neither students nor teachers are bound to any specific location and that this form of computer-based education is virtually independent of a specific hardware platform. Adopting these e-learning systems in higher educational institution can provide us with enormous quantities of data that describe the behavior of students. In particular, Learning Management Systems (LMSs) are becoming much more common in universities, community colleges, schools, and businesses, and are even used by individual instructors in order to add web technology to their courses and supplement traditional face-to-face courses. One of the most popular LMS is Moodle [1], a free and open-source learning management system that allows the creation of completely virtual courses (electronic learning, e-learning) or courses that are partially virtual (blended learning, b-learning). Moodle accumulate a vast amount of information, which is very valuable for analyzing students' behavior and could create a gold mine of educational data. Moodle keeps detailed logs of all events that students perform and keeps track of what materials students have accessed. However, due to the huge quantities of log data that Moodle can generate daily, it is very difficult to analyze them, thus, it is necessary to use Educational Data Mining (EDM) and Learning Analytics (LA) tools [2]. EDM and LA techniques discover useful, new, valid, and comprehensible knowledge from educational data in order to resolve educational problems [3]. There is a wide range of EDM/LA tasks or applications, but one of the oldest and most important ones is to predict student performance [4]. The objective of prediction is to estimate the unknown value of a variable that describes the student. In education the values normally predicted are performance, knowledge, score, or mark [5]. This value can be numerical/continuous value (regression task) or categorical/discrete value (classification task). In fact, nowadays, there is a great interest in analyzing and mining students' usage/interaction information gathered by Moodle for predicting students' final mark in blended learning [6; 7]. Blended learning combines the e-learning and the classical face-to-face learning environments. It has been termed as blended learning, hybrid, or mixed learning [8]. Since either pure e-learning or traditional learning hold some weaknesses and strengths, it is better to mix the strengths of both learning environments into a new method of instruction delivery called blended learning. Most of the research about predicting students' performance has focused on scenarios that assume that the training and test data are drawn from the same course [9]. As a matter of fact, the obtained/discovered models are mostly built on the samples that researchers have ready at hand, whether it is the current population of students at a university developing a model, the current user base of the adaptive learning system for which the model is being built, or just students who are relatively easy to survey or observe [10]. However, in real educational environments, we historical data are not always available from all the courses. Let us imagine, for example, the case of a new course that is taught for the first time in an institution. Here, we would not have data for training model for predicting student performance. Yet, it is fair that the tutors and students of this new subject have the chance to work with predictive models that notify them of possible unwanted at risk situations such as student drop out. Thus, model portability can be very useful to create and use transferable models of other similar course in which we have a prediction model. The idea of Portability is that knowledge extracted from a specific environment can be applied directly to another different environment.