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Jantke K.P.,Fraunhofer Institute for Digital Media Technology
CSEDU 2010 - 2nd International Conference on Computer Supported Education, Proceedings | Year: 2010

Playful learning is an old dream of mankind since Comenius' early work on didactics (Comenius, 1628). But playful learning should not be oversimplified and thoughtlessly identified with effortless fun. In contrast, playful learning may be some fun, although being demanding requiring concentration, devotion and stamina. Gorge is the name of a digital game designed for the purpose of developing certain technology competence. It is in use with students of an age ranging from about 12 to 24. This poster surveys the concepts and the game. Source

Jantke K.P.,Fraunhofer Institute for Digital Media Technology
Communications in Computer and Information Science | Year: 2013

Memetics and meme media technologies deployed for some purpose of technology enhanced learning need a certain systematization. Didactic principles, patterns of didactically driven activities, and the like may be seen as memes. Those memes are encapsulated as meme media occurring in digital representations of anticipated learning experiences named storyboards. Digital storyboarding is the preferred technology of designing anticipated learning experiences based on didactic knowledge. Encapsulated didactic memes-knowledge, principles, artifices, use cases-occur in digital storyboards and, through using, changing and re-using, may be subject to inheritance, mutation, cross-over and natural selection. © Springer-Verlag Berlin Heidelberg 2013. Source

Dressler K.,Fraunhofer Institute for Digital Media Technology
Proceedings of the 12th International Society for Music Information Retrieval Conference, ISMIR 2011 | Year: 2011

This paper proposes an efficient approach for the identification of the predominant voice from polyphonic musical audio. The algorithm implements an auditory streaming model which builds upon tone objects and salient pitches. The formation of voices is based on the regular update of the frequency and the magnitude of so called streaming agents, which aim at salient tones or pitches close to their preferred frequency range. Streaming agents which succeed to assemble a big magnitude start new voice objects, which in turn add adequate tones. The algorithm was evaluated as part of a melody extraction system during the MIREX audio melody extraction evaluation, where it gained very good results in the voicing detection and overall accuracy. © 2011 International Society for Music Information Retrieval. Source

Jantke K.P.,Fraunhofer Institute for Digital Media Technology
Proceedings of the 2010 IEEE International Conference on Progress in Informatics and Computing, PIC 2010 | Year: 2010

Learning by playing is a rather old dream coming next after learning when sleeping; at least, it is ambitious. Some authors report an enormous success of game-based learning, whereas others speak about a caricature of computer games and a reactionary use of learning theory. Opinions are quite divided. It seems difficult to resolve contradictions and to settle a dispute as long as there is no appropriate terminology available. There is an apparent need of some taxonomy for a digital games science. This paper contributes to a taxonomy of digital games, in general, and to taxonomic concepts of game-based learning, in particular. Taxonomic concepts such as extra game play and meta game play prove successful for the understanding of game playing impact as well as for guiding serious games design. ©2010 IEEE. Source

Nowak S.,Fraunhofer Institute for Digital Media Technology
Proceedings - International Conference on Pattern Recognition | Year: 2010

The Photo Annotation Task is performed as one task in the ImageCLEF@ICPR contest and poses the challenge to annotate 53 visual concepts in Flickr photos. Altogether 12 research teams met the multilabel classification challenge and submitted solutions. The participants were provided with a training and a validation set consisting of 5,000 and 3,000 annotated images, respectively. The test was performed on 10,000 images. Two evaluation paradigms have been applied, the evaluation per concept and the evaluation per example. The evaluation per concept was performed by calculating the Equal Error Rate and the Area Under Curve (AUC). The evaluation per example utilizes a recently proposed Ontology Score. For the concepts, an average AUC of 86.5% could be achieved, including concepts with an AUC of 96%. The classification performance for each image ranged between 59% and 100% with an average score of 85%. © 2010 IEEE. Source

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