Guess the score, fostering collective intelligence in the class

This paper proposes the use of serious games as a tool to enhance collective intelligence of undergraduate and graduate students. The development of social skills of individuals in a group is related to the performance of the collective intelligence of the group manifested through the shared and collaborative development of intellectual tasks [1]. Guess the Score GS, is a serious game implemented by means of an online tool, created to foster the development, collaboration and engagement of students. It's has been designed with the intention of facilitating the development of individual’s social skills in a group in order to promote education of collective intelligence. This paper concludes that the design of learning activities using serious games as a support tool in education, generate awareness about of utilities of gaming in the collective learning environment and the fostering of collective intelligence education.Postprint (published version

Morality Play: A Model for Developing Games of Moral Expertise

According to cognitive psychologists, moral decision-making is a dual-process phenomenon involving two types of cognitive processes: explicit reasoning and implicit intuition. Moral development involves training and integrating both types of cognitive processes through a mix of instruction, practice, and reflection. Serious games are an ideal platform for this kind of moral training, as they provide safe spaces for exploring difficult moral problems and practicing the skills necessary to resolve them. In this article, we present Morality Play, a model for the design of serious games for ethical expertise development based on the Integrative Ethical Education framework from moral psychology and the Lens of the Toy model for serious game design

Heuristic Evaluation for Serious Immersive Games and M-instruction

© Springer International Publishing Switzerland 2016. Two fast growing areas for technology-enhanced learning are serious games and mobile instruction (M-instruction or M-Learning). Serious games are ones that are meant to be more than just entertainment. They have a serious use to educate or promote other types of activity. Immersive Games frequently involve many players interacting in a shared rich and complex-perhaps web-based-mixed reality world, where their circumstances will be multi and varied. Their reality may be augmented and often self-composed, as in a user-defined avatar in a virtual world. M-instruction and M-Learning is learning on the move; much of modern computer use is via smart devices, pads, and laptops. People use these devices all over the place and thus it is a natural extension to want to use these devices where they are to learn. This presents a problem if we wish to evaluate the effectiveness of the pedagogic media they are using. We have no way of knowing their situation, circumstance, education background and motivation, or potentially of the customisation of the final software they are using. Getting to the end user itself may also be problematic; these are learning environments that people will dip into at opportune moments. If access to the end user is hard because of location and user self-personalisation, then one solution is to look at the software before it goes out. Heuristic Evaluation allows us to get User Interface (UI) and User Experience (UX) experts to reflect on the software before it is deployed. The effective use of heuristic evaluation with pedagogical software [1] is extended here, with existing Heuristics Evaluation Methods that make the technique applicable to Serious Immersive Games and mobile instruction (M-instruction). We also consider how existing Heuristic Methods may be adopted. The result represents a new way of making this methodology applicable to this new developing area of learning technology

The Role of Practice in Chess: A Longitudinal Study

We investigated the role of practice in the acquisition of chess expertise by submitting a questionnaire to 104 players of different skill levels. Players had to report their chess rating, the number of hours of individual and group practice, their use of different learning resources and activities, and whether they had been trained by a coach. The use of archival data enabled us to track the rating of some of the players throughout their career. We found that there was a strong correlation between chess skill and number of hours of practice. Moreover, group practice was a better predictor of high-level performance than individual practice. We also found that masters had a higher chess rating than expert players after only three years of serious dedication to chess, although there were no differences in the number of hours of practice. The difference that may explain the variation in rating is that masters start practising at an earlier age than experts. Finally, we found that activities such as reading books and using computer software (game databases, but not playing programs) were important for the development of high-level performance. Together with previous data and theories of expert performance, our results indicate limits in the deliberate practice framework and make suggestions on how best to carry out learning in chess and in other fields