Serious games open up many new opportunities for complex skills learning in higher education. The inherent complexity of such games though, requires large efforts for their development. This paper presents a framework for serious game design, which aims to reduce the design complexity at both conceptual, technical and practical levels. The approach focuses on a relevant subset of serious games, labelled scenario-based games. At the conceptual level it identifies the basic elements that make up the static game configuration; it also describes the game dynamics, i.e. the state changes of the various game components in the course of time. At the technical level it presents a basic system architecture, which comprises various building tools. Various building tools will be explained and illustrated with technical implementations that are part of the Emergo toolkit for scenario-based game development. At the practical level, a set of design principles are presented for controlling and reducing game design complexity. The principles cover the topics of game structure, feedback and game representation, respectively. Practical application of the framework and the associated toolkit is briefly reported and evaluated.

Societal changes demand educators to apply new pedagogical approaches. Many educational stakeholders feel that serious games could play a key role in fulfilling this demand, and they lick their chops when looking at the booming industry of leisure games. However, current toolkits for developing leisure games show severe shortcomings when applied to serious games. Developing effective serious games in an efficient way requires a specific approach and tool set. This article describes the EMERGO methodology and generic toolkit for developing and delivering scenario-based serious games that are aimed at the acquisition of complex cognitive skills in higher education. Preliminary evaluation results with case developers using the EMERGO methodology and toolkit and with learners using EMERGO cases are presented.

This paper examines how learning outcomes from playing serious games can be enhanced by including scripted collaboration in the game play. We compared the quality of advisory reports, that students in the domain of water management had to draw up for an authentic case problem, both before and after collaborating on the problem with (virtual) peer students. Peers studied the case from either an ecological or governance perspective, and during collaboration both perspectives had to be confronted and reflected upon. This paper argues why such type of workplace-based learning scenarios are important for professional development, describes how serious gaming scenarios can be designed to support such complex learning, and reports data on student satisfaction and learning effects of including scripted collaboration. Preliminary results from a pilot study with 12 students show that including scripted collaboration significantly enhances the quality of learning outcomes. Collaboration and serious games for complex learningSerious games are games that can educate, train or inform, either because they have been deliberately designed for learning or just happen to do so by coincidence. Educators call them 'serious' to denote that they are not just fun to play, but also hold potential as cognitive tools for learning and professional development (eg, Michael & Chen, 2006;Prensky, 2006;Rayburn, 2007). Serious games are supposed to offer many new learning opportunities and positive effects on learner motivation and learning outcomes (eg, De Freitas, 2006;Kiili, 2007;Shaffer, 2006). As opposed to serious games, leisure (or amusement) games have already become adopted widely by the new generation of learners. The leisure games industry and educational institutes so far barely have worked together, and continue to act from separated worlds and objectives. The mind set on learning exudes an air of calm reflection, concentration and investigation, while the mind set on gaming is driven by amusement, fast fun and relaxation. Also, to counterbalance this presumed contradiction, educators have started using the term 'serious games' to indicate that games can be both instructive and meaningful for learning, and playful and fun at the same time. Technology (2010Technology ( ) doi:10.1111Technology ( /j.1467Technology ( -8535.2010 Serious games can provide immersive learning opportunities. Some appear crucial for competences required for modern citizens and professionals in business and industry in the current information age. Learning can no longer remain restricted to acquiring knowledge of content matter, but also has to deal with selecting and using this knowledge for certain problem situations in the workplace. Such more complex learning is about acquiring competences like information skills, media literacy, problem-solving, communication and collaboration, and critical reflection about wicked problems. Such competences are usually not addressed by other learning platforms (Gee, 2003). The objective of this study was to see ...

This paper presents a framework (FILTWAM (Framework for Improving Learning Through Webcams And Microphones)) for real-time emotion recognition in e-learning by using webcams. FILTWAM offers timely and relevant feedback based upon learner's facial expressions and verbalizations. FILTWAM's facial expression software module has been developed and tested in a proof-of-concept study. The main goal of this study was to validate the use of webcam data for a real-time and adequate interpretation of facial expressions into extracted emotional states. The software was calibrated with 10 test persons. They received the same computer-based tasks in which each of them were requested 100 times to mimic specific facial expressions. All sessions were recorded on video. For the validation of the face emotion recognition software, two experts annotated and rated participants' recorded behaviours. Expert findings were contrasted with the software results and showed an overall value of kappa of 0.77. An overall accuracy of our software based on the requested emotions and the recognized emotions is 72%. Whereas existing software only allows not-real time, discontinuous and obtrusive facial detection, our software allows to continuously and unobtrusively monitor learners' behaviours and converts these behaviours directly into emotional states. This paves the way for enhancing the quality and efficacy of e-learning by including the learner's emotional states.

This paper presents the voice emotion recognition part of the FILTWAM framework for real-time emotion recognition in affective e-learning settings. FILT WAM (Framework for Improving Learning Through Webcams And Microphones) intends to offer timely and appropriate online feedback based upon learner's vocal intonations and facial expressions in order to foster their learning. Whereas the facial emotion recognition part has been successfully tested in a previous study, the here presented study describes the development and testing of FILTWAM's vocal emotion recognition software artefact. The main goal of this study was to show the valid use of computer microphone data for real-time and adequate interpretation of vocal intonations into extracted emotional states. The software that was developed was tested in a study with 12 participants. All participants individually received the same computerbased tasks in which they were requested 80 times to mimic specific vocal expressions (960 occurrences in total). Each individual session was recorded on video. For the validation of the voice emotion recognition software artefact, two experts annotated and rated participants' recorded behaviours. Expert findings were then compared with the software recognition results and showed an overall accuracy of Kappa of 0.743. The overall accuracy of the voice emotion recognition software artefact is 67 % based on the requested emotions and the recognized emotions. Our FILTWAM-software allows to continually and unobtrusively observing learners' behaviours and transforms these behaviours into emotional states. This paves the way for unobtrusive and real-time capturing of learners' emotional states for enhancing adaptive e-learning approaches.