Carlos Villagrá-Arnedo

Gamification of the Learning Process: Lessons Learned

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 lie in.

Boosting the Learning Process with Progressive Performance Prediction

A prediction system to early detect learning problems is presented. The starting point is a gamified learning system from which a massive set of usage and learning data is collected. They are analyzed using Machine Learning techniques and a prediction of each student’s performance is obtained. The information is weekly presented as a progression chart, with valuable information about students’ progression. The system has a high degree of automation, is progressive, uses learning outcomes as well as usage data, allows the evaluation and prediction of the acquired skills, and contributes to a truly formative assessment.

PLMan: Towards a Gamified Learning System

Gamification is set to be a disruptive innovation in the field of education in the next years, as a way to encourage learning, since when the fun impregnates the learning process, motivation increases and stress is reduced. However, most experiences in learning gamification just remain on the surface, just offering a layer of standardized game elements such as badges, leader boards and medals. Instead, a deeper transformation of the learning process is needed, making up a true process reengineering. As a practical example, PLMan learning system is presented, an attempt to redefine the learning process in the context of a particular subject. It is based on a unique and simple type of problem: solving mazes of the PLMan game, an adaptation of the famous Pac-Man game. The maze, as the building block of our learning strategy, has a set of properties that allows us to introduce all the features of games in the learning process. From this experience some important lessons about the gamification of the teaching-learning process can be obtained: the importance of fun as a consequence of learning, the need of having an immediate feedback of our actions, the trial and error possibility as a major source of learning and progress, the relevance of experimentation and creativity as a means to develop the learners skills and the importance of autonomy to give the learners the control of their learning process. All these features are propellants for learning and a way to improve the motivation of learners.

Predicting Academic Performance from Behavioural and Learning 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 spe cially 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.

Effects of Low-Level Development on Learning to Program

There are subjects in which teaching and learning is hard by experience. Some subjects in physics, maths or computing seem to be difficult by nature. Teachers test many ways to help student learn these subjects. In Computer Programming the approach seems to be using higher-level languages, concepts and abstractions. It seems reasonable that languages similar to human language can ease the task of computer programming. Similar ideas are explored in other subjects. However, this seems contradictory with the way we construct knowledge: lower-level concepts support the development of higher-level ones. Is it possible to master higher-level concepts without previously mastering lower-level ones?

Analytics-driven redesign of an instructional course

Learning Analytics1 is a powerful tool that provides rich information for students, teachers and academic authorities. There is a wide range of possible applications, and one of them is leveraging the information to improve the instructional design of a course. In this research, we introduce the results of a Learning Analytics engine to improve all the stages of an Action Research experience. We have carried out three iterations: iteration 1, devoted to design an instructional course using an automated learning platform that collects data from the students; iteration 2, focused on the analysis of the individual and aggregated data collected from the students to obtain group behaviors; and iteration 3, currently under way, devoted to improve the structure of the course using the results of the previous iterations. We have made use of some graphical representations of the data that help to understand the aggregated data and to detect important events and moments of intervention. We have detected that the behavior of the students is strongly conditioned by the deadline structure of the course and that there is usually a crucial moment, by halfway of the course, where the situation is tending to stabilize and which is a good moment to reinforce the supervision on the student. Finally, we have stated that low performance students are usually recoverable to the last moment.

Training Socially Responsible Engineers by Developing Accessible Video Games

University has an active social responsibility that is addressed both by acting responsibly as institution and by transferring this ethical duty to the students. Our proposal is achieving the social inclusion of disabled people, as part of the social responsibility of future engineers, through the realization of the final degree project in collaboration with associations of disabled users, in particular through the design and development of adapted video games. This paper presents our experience in developing these projects in collaboration with an association of users with cerebral palsy. The objective is training the students in the social responsibility but also solving some problems of inclusion in the collective of disabled users (such as the access to digital entertainment) and carrying out an in-depth study about the interaction problems for users with cerebral palsy, providing concrete and practical solutions. The methodology for this experience is Action Research, with four research stages (plan, implement, evaluate and reflect) that are iteratively repeated. Following this methodology, three iterations have been carried out and a fourth one is planned. As a result, three adapted video games have been developed and a guide for adapting video games to people with cerebral palsy has been defined. This experience has served to introduce the aspects of social responsibility in the curricula of engineers in a very effective way and to study and design new ways of making video games accessible to disabled people, giving them the chance to exercise their right to entertainment.