User Assessment in Serious Games and Technology-Enhanced Learning

1 Department of Naval, Electric, Electronic and Telecommunications Engineering, University of Genoa, Via all'Opera Pia 11/a, 16145 Genoa, Italy 2 Faculty of Business and Information Technology, University of Ontario Institute of Technology, 2000 Simcoe Street North, Oshawa, Canada L1H 7K4 3Department of Mechanical and Aerospace Engineering, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH 44106, USA 4 Faculty of Computer Science, Universidad Complutense de Madrid, Ciudad Universitaria Universidad Complutense de Madrid, 28040 Madrid, Spai

Usability evaluation of digital Malaysian traditional games

Focusing on measuring the engagement towards digital Malaysian traditional games, this paper presents usability evaluation on three digital versions of Malaysian traditional games; Dam Haji, Congkak and Gasing-X. Usability is a significant contributing factor towards engagement of digital games. Usability helps in verifying the requirements, successes and functionality of the games which are missing.The usability evaluation adopted the heuristic instruments developed by Jakob Nielson in 1990. The instrument consists of 17 heuristic component protocols based on interface design.Evaluation involved 50 respondents who are IT and domain experts.Result analysis is discussed and presented for each game. Results suggested features and aspects to be improved in the development of Digital Malaysian Traditional Games towards better engagement of the games

Usability Evaluation of Learning Management Systems in Sri Lankan Universities

As far as Learning Management System is concerned, it offers an integrated platform for educational materials, distribution and management of learning as well as accessibility by a range of users in cluding teachers, learners and content makerses pecially for distance learning. Usability evaluation is considered as one approach to assess the efficiency of e-Learning systems. It is used to evaluate how well technology and tools are working for users. There are some factors contributing as major reasons why the LMS is not used effectively and regularly. Learning Management Systems, as major part of e-Learning systems, can benefit from usability research to evaluate the LMS ease of use and satisfaction among its handlers. Many academic institutions worldwide prefer using their own customized Learning Management Systems; this is the case with Moodle, an open source LMS platform designed and operated by most of the universities in Sri Lanka

Deliverable 9.7 - GALA Dissemination Report 3

Westera, W., Berta, R., Moreno-Ger, P., Bellotti, F., Nadolski, R., Padrón-Nápoles, C. L., Boyle, L., Beligan, D., & Baalsrud Hauge, J. (2013). Deliverable 9.7 - GALA Dissemination Report 3. Heerlen, The Netherlands: Games and Learning Alliance, European Network of Excellence.This report summarizes the dissemination activities of the GALA Network of Excellence from October 2012 till October 2013.7th Framework Programme of the European Commissio

Reconnaissance de l'émotion thermique

Pour améliorer les interactions homme-ordinateur dans les domaines de la santé, de l'e-learning et des jeux vidéos, de nombreux chercheurs ont étudié la reconnaissance des émotions à partir des signaux de texte, de parole, d'expression faciale, de détection d'émotion ou d'électroencéphalographie (EEG). Parmi eux, la reconnaissance d'émotion à l'aide d'EEG a permis une précision satisfaisante. Cependant, le fait d'utiliser des dispositifs d'électroencéphalographie limite la gamme des mouvements de l'utilisateur. Une méthode non envahissante est donc nécessaire pour faciliter la détection des émotions et ses applications. C'est pourquoi nous avons proposé d'utiliser une caméra thermique pour capturer les changements de température de la peau, puis appliquer des algorithmes d'apprentissage machine pour classer les changements d'émotion en conséquence. Cette thèse contient deux études sur la détection d'émotion thermique avec la comparaison de la détection d'émotion basée sur EEG. L'un était de découvrir les profils de détection émotionnelle thermique en comparaison avec la technologie de détection d'émotion basée sur EEG; L'autre était de construire une application avec des algorithmes d'apprentissage en machine profonds pour visualiser la précision et la performance de la détection d'émotion thermique et basée sur EEG. Dans la première recherche, nous avons appliqué HMM dans la reconnaissance de l'émotion thermique, et après avoir comparé à la détection de l'émotion basée sur EEG, nous avons identifié les caractéristiques liées à l'émotion de la température de la peau en termes d'intensité et de rapidité. Dans la deuxième recherche, nous avons mis en place une application de détection d'émotion qui supporte à la fois la détection d'émotion thermique et la détection d'émotion basée sur EEG en appliquant les méthodes d'apprentissage par machine profondes - Réseau Neuronal Convolutif (CNN) et Mémoire à long court-terme (LSTM). La précision de la détection d'émotion basée sur l'image thermique a atteint 52,59% et la précision de la détection basée sur l'EEG a atteint 67,05%. Dans une autre étude, nous allons faire plus de recherches sur l'ajustement des algorithmes d'apprentissage machine pour améliorer la précision de détection d'émotion thermique.To improve computer-human interactions in the areas of healthcare, e-learning and video games, many researchers have studied on recognizing emotions from text, speech, facial expressions, emotion detection, or electroencephalography (EEG) signals. Among them, emotion recognition using EEG has achieved satisfying accuracy. However, wearing electroencephalography devices limits the range of user movement, thus a noninvasive method is required to facilitate the emotion detection and its applications. That’s why we proposed using thermal camera to capture the skin temperature changes and then applying machine learning algorithms to classify emotion changes accordingly. This thesis contains two studies on thermal emotion detection with the comparison of EEG-base emotion detection. One was to find out the thermal emotional detection profiles comparing with EEG-based emotion detection technology; the other was to implement an application with deep machine learning algorithms to visually display both thermal and EEG based emotion detection accuracy and performance. In the first research, we applied HMM in thermal emotion recognition, and after comparing with EEG-base emotion detection, we identified skin temperature emotion-related features in terms of intensity and rapidity. In the second research, we implemented an emotion detection application supporting both thermal emotion detection and EEG-based emotion detection with applying the deep machine learning methods – Convolutional Neutral Network (CNN) and LSTM (Long- Short Term Memory). The accuracy of thermal image based emotion detection achieved 52.59% and the accuracy of EEG based detection achieved 67.05%. In further study, we will do more research on adjusting machine learning algorithms to improve the thermal emotion detection precision

Mediating skills on risk management for improving the resilience of Supply Networks by developing and using a serious game

Given their importance, the need for resilience and the management of risk within Supply Networks, means that engineering students need a solid under-standing of these issues. An innovative way of meeting this need is through the use of serious games. Serious games allow an active experience on how differ-ent factors influencethe flexibility, vulnerability and capabilities in Supply Networks and allow the students to apply knowledge and methods acquired from theory. This supports their ability to understand, analyse and evaluate how different factors contribute to the resilience. The experience gained within the game will contribute to the studentsâ abilities to construct new knowledge based on their active observation and reflection of the environment when they later work in a dynamic environment in industry. This game, Beware, was developed for use in a blended learning environment. It is a part of a course for engineering master students at the University of Bremen. It was found that the game was effective in mediating the topic of risk management to the students espscially in supporting their ability of applying methods, analyse the different interactions and the game play as well as to support the assessment of how their decision-making affected the simulated network