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The teaching of communication skills is a labour-intensive task because of the detailed feedback that should be given to learners during their prolonged practice. This study investigates to what extent our FILTWAM facial and vocal emotion recognition software can be used for improving a serious game (the Communication Advisor) that delivers a web-based training of communication skills. A test group of 25 participants played the game wherein they were requested to mimic specific facial and vocal emotions. Half of the assignments included direct feedback and the other half included no feedback. It was investigated whether feedback on the mimicked emotions would lead to better learning. The results suggest the facial performance growth was found to be positive, particularly significant in the feedback condition. The vocal performance growth was significant in both conditions. The results are a significant indication that the automated feedback from the software improves learners’ communication performances. [ABSTRACT FROM PUBLISHER]

Serious games foster the acquisition of complex problem-solving skills. Assessment of such skills should be in line with instruction, and within a serious game environment its content validity should equal face-to-face assessment. Research on assessment in serious gaming has remained rather scarce. This article shows how assessment can be implemented in serious gaming in a way that assures content validity. The core of the authors’ validation method entails mapping learning activities (as contained in the game scenario) on performance indicators and outputs (as derived from formal attainment levels). They present how they have elaborated and applied the method for an assessment game for ICT managers in secondary vocational education. They describe the procedure and extent to which this assessment is content-valid compared to face-to-face assessment. [ABSTRACT FROM AUTHOR]

This article describes the validation study of our software that uses combined webcam and microphone data for real-time, continuous, unobtrusive emotion recognition as part of our FILTWAM framework. FILTWAM aims at deploying a real-time multimodal emotion recognition method for providing more adequate feedback to the learners through an online communication skills training. Herein, timely feedback is needed that reflects on the intended emotions they show and which is also useful to increase learners’ awareness of their own behavior. At least, a reliable and valid software interpretation of performed face and voice emotions is needed to warrant such adequate feedback. This validation study therefore calibrates our software. The study uses a multimodal fusion method. Twelve test persons performed computer-based tasks in which they were asked to mimic specific facial and vocal emotions. All test persons’ behavior was recorded on video and two raters independently scored the showed emotions, which were contrasted with the software recognition outcomes. A hybrid method for multimodal fusion of our multimodal software shows accuracy between 96.1% and 98.6% for the best-chosen WEKA classifiers over predicted emotions. The software fulfils its requirements of real-time data interpretation and reliable results. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM PUBLISHER]

In an e-learning environment there is a need to integrate various e-learning services like assessment services, collaboration services, learning design services and communication services. In this article we present the design and implementation of a generic integrative service framework, called CopperCore Service Integration (CCSI). We will concentrate on the integration of two services: CopperCore, an IMS Learning Design service and an IMS Question and Test Interoperability service called Assessment Provision through Interoperable Segments (APIS). One of the design goals of the architecture was to minimize the intrusion for both the services as well as any legacy client that already uses these services. The result of this work is that the flow of learning activities can be made dependent on test results. [ABSTRACT FROM AUTHOR]

An instrument for measuring the complexity of learning tasks in the field of Law was developed and tested in 3 experiments. In Experiments 1 and 2, teachers used the card-sort method to rate the complexity of learning tasks. Based on the outcomes, a benchmark scale with 4 criterion tasks was used in Experiment 3. The results showed the benchmark instrument to be valid and easy to use, allowing instructional designers to design competency-based learning environments that better take task complexity into account. The Instructional Design model, in which teachers determine task complexity, is briefly described. [ABSTRACT FROM AUTHOR]