This research was funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 644187, the RAGE project (www.rageproject.eu).
This paper introduces a Framework for Improving Learning Through Webcams And Microphones (FILTWAM). It proposes an overarching framework comprising conceptual and technical frameworks for enhancing the online communication skills of lifelong learners. Our approach interprets the emotional state of people using webcams and microphones and combines relevant and timely feedback based upon learner's facial expressions and verbalizations (like sadness, anger, disgust, fear, happiness, surprise, and neutral). The feedback generated from the webcams is expected to enhance learner's awareness of their own behavior. Our research enhances flexibility and scalability in contrast with face- to-face trainings and better helps the interests of lifelong learners who prefer to study at their own pace, place and time. Our small-scale proof of concept study exemplifies the practical application of FILTWAM and provides first evaluation results on that. This study will guide future development of software, training materials, and research. It will validate the use of webcam data for a real-time and adequate interpretation of facial expressions into emotional states. Participants' behaviour is recorded on videos so that videos will be replayed, rated, annotated and evaluated by expert observers and contrasted with participants' own opinions in future research.
The chain for learning scenarios(LSs) and learning objects(LOs) comprises five iterative links: (i) development, (ii) publication, (iii) making resources searchable (iv) facilitating their arrangement (v) towards a runnable unit of learning. E-learning specifications and components-based systems embedded in a service-oriented architecture are conditio sine qua non for enabling this chain. To create a stronger more enduring chain, it must be easier to develop software and content compliant to elearning specifications. Conformance Testing (CT) is the elixir within the chain for LSs and LOs and the Telcert project CT system can be used in strengthening the chain.
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.
In this paper we present a modular approach for learning support in game environments. In applied or educational games both aspects are important – the fun to play and a topic to learn. While traditional games or leisure games only focus on the enjoyment and entertainment of the player, educational games also aim to convey knowledge and competences. However, in order to fulfill this goal, an applied game should take care that the player actually learns. The various components of our approach specifically support the learning aspects in a game in different and balanced ways. This includes adaptation of the game for smooth competence development, maintaining motivation, support in problem solving tasks, support for meta-cognition and reflection, and adaptation to the players' personality.
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