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

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

Combining brain-computer interfaces and assistive technologies: state-of-the-art and challenges

In recent years, new research has brought the field of EEG-based Brain-Computer Interfacing (BCI) out of its infancy and into a phase of relative maturity through many demonstrated prototypes such as brain-controlled wheelchairs, keyboards, and computer games. With this proof-of-concept phase in the past, the time is now ripe to focus on the development of practical BCI technologies that can be brought out of the lab and into real-world applications. In particular, we focus on the prospect of improving the lives of countless disabled individuals through a combination of BCI technology with existing assistive technologies (AT). In pursuit of more practical BCIs for use outside of the lab, in this paper, we identify four application areas where disabled individuals could greatly benefit from advancements in BCI technology, namely,“Communication and Control”, “Motor Substitution”, “Entertainment”, and “Motor Recovery”. We review the current state of the art and possible future developments, while discussing the main research issues in these four areas. In particular, we expect the most progress in the development of technologies such as hybrid BCI architectures, user-machine adaptation algorithms, the exploitation of users’ mental states for BCI reliability and confidence measures, the incorporation of principles in human-computer interaction (HCI) to improve BCI usability, and the development of novel BCI technology including better EEG devices