Abstract-Although several definitions of gamification can be found in the literature, they all have in common certain aspects: the application of strategies, models, dynamics, mechanics and elements of the games in other contexts than games, and the objective of producing a playful experience that fosters motivation, involvement and fun. In this paper, our approach gamifying the learning process of a subject is presented. Our experience throughout time in using games and gamification in learning have led us to propose, lately, a personalized, automated and gamified learning system. As a result of this experience and after several years of continuous feedback from our students, we have learned several lessons on how to approach the task of gamification. These lessons are summarized in the following concepts: fun, motivation, autonomy, progressiveness, feedback, error tolerance, experimentation, creativity and adaptation to the specific case. The final aim is sharing our experience and opening a debate about what key elements the gamification lies in.

Video games were initially considered as a form of entertainment. Today this perception has changed. Many video games have been designed for a wide range of purposes: education, rehabilitation, etc. This paper presents our experience developing video games in collaboration with an association of users with cerebral palsy. Cerebral palsy is characterized by a group of permanent disorders of the development of movement and posture, causing activity limitation. This means that people affected by this disease are unable to handle the usual devices used in video games. Moreover, video games offer these people a form of leisure that can also benefit them in many ways: autonomy, strength, coordination, self-confidence, learning from error, etc. Three adapted video games have been developed as well as a guide for designing accesible video games for people with cerebral palsy. This experience has served to study and design new ways of making video games accessible to disabled people, giving them the chance to exercise their right to entertainment.

Many researchers consider Gamification as a powerful way to improve education. Many studies show improvements with respect to traditional methodologies. Several educational strategies have also been combined with Gamification with interesting results. Interest is growing and evidence suggest Gamification has a promising future. However, there is a barrier preventing many researchers from properly understanding Gamification principles. Gamification focuses of engaging trainees in learning with same intensity that games engage players on playing. But only some very well designed games achieve this level of engagement. Designing truly entertaining games is a difficult task with a great artistic component. Although some studies have tried to clarify how Game Design produces fun, there is no scientific consensus. Well established knowledge on Game Design resides in sets of rules of thumb and good practices, based on empirical experience. Game industry professionals acquire this experience through practice. Most educators and researchers often overlook the need for such experience to successfully design Gamification. And so, many research papers focus on single game-elements like points, present non-gaming activities like questionnaires, design non-engaging activities or fail to comprehend the underlying principles on why their designs do not yield expected results. This work presents a rubric for educators and researchers to start working in Gamification without previous experience in Game Design. This rubric decomposes the continuous space of Game Design into a set of ten discrete characteristics. It is aimed at diminishing the entry barrier and helping to acquire initial experience with Game Design fundamentals. The main proposed uses are twofold: to analyse existing games or gamified activities gaining a better understanding of their strengths and weaknesses and to help in the design or improvement of activities. Focus is on Game Design characteristics rather than game elements, similarly to professional game designers. The goal is to help gaining experience towards designing successful Gamification environments. Presented rubric is based on our previous design experience, compared and contrasted with literature, and empirically tested with some example games and gamified activities.

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The paper describes the training, validation, testing and application of models of artificial neural networks (ANN) for computing the cross-shore beach profile of the sand beaches of the province of Valencia (Spain). Sixty ANN models were generated by modifying both the input variables as the number of neurons in the hidden layer. The input variables consist of wave data

The volume and quality of data, but also their relevance, are crucial when performing data analysis. In this paper, a study of the influence of different types of data is presented, particularly in the context of educational data obtained from Learning Management Systems (LMSs). These systems provide a large amount of data from the student activity but they usually do not describe the results of the learning process, i.e., they describe the behaviour but not the learning results. The starting hypothesis states that complementing behavioural data with other more relevant data (regarding learning outcomes) can lead to a better analysis of the learning process, that is, in particular it is possible to early predict the student final performance. A learning platform has been specially developed to collect data not just from the usage but also related to the way students learn and progress in training activities. Data of both types are used to build a progressive predictive system for helping in the learning process. This model is based on a classifier that uses the Support Vector Machine technique. As a result, the system obtains a weekly classification of each student as the probability of belonging to one of three classes: high, medium and low performance. The results show that, supplementing behavioural data with learning data allows us to obtain better predictions about the results of the students in a learning system. Moreover, it can be deduced that the use of heterogeneous data enriches the final performance of the prediction algorithms.

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