Dybuster
| Dybuster | |
|---|---|
| Basic data
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| developer | Dybuster AG |
| operating system | Windows 2000 and newer, Mac OS X 10.4 and newer |
| category | Educational software |
| License | EULA |
| German speaking | Yes |
| Homepage | |
Dybuster is a multi-sensory, computer-based learning system for people with dyslexia . Dybuster was developed at the Swiss Federal Institute of Technology Zurich ( ETH Zurich ) and tested for its effectiveness in joint research projects with neuropsychologists from the University of Zurich University of Zurich . The learning system was developed from a prototype to a product by Dybuster AG, a spin-off from ETH Zurich.
Scientific background
hypothesis
Dyslexia (today more and more LRS, reading and spelling difficulties) can probably have several causes. It is assumed u. a. auditory, visual and phonological deficits. To put it simply, these deficits lead to difficulties in the task of translating the written language into the spoken language when reading and, conversely, of mapping the spoken language onto the written language when writing.
The basic principle of Dybuster is therefore to offer a word not just as black letters on a white sheet of paper. Instead, each word is presented as a well-defined and calculated combination of colors, shapes, structures and tones. Dybuster thus implements multi-sensory learning , which is generally considered an efficient form of learning. As a specific motivation for dyslexia, this additional information should make it easier to learn and retrieve the mapping of spoken to written language by allowing the brain to use regions (so-called modes) that are rarely used in conventional learning of written language.
Proof of effectiveness
The effectiveness of Dybuster in improving spelling was tested in two user studies by the University of Zurich. The subjects each exercised 15-20 minutes a day on 3 to 4 days a week for 3 months. In the first study, the authors found a significant reduction in spelling errors by an average of 35% in the exercising subjects compared to an improvement of an average of 5% in control groups, who were only allowed to use Dybuster in a second training phase.
The second study reproduced the training progress of the first study. In addition, it could be shown that the test subjects were less and less likely to commit phoneme-grapheme errors during the course of the training. The authors concluded from this a confirmation of the hypothesis, namely that multi-sensory learning enables the mapping of written to spoken language to be strengthened, even if the underlying neurological mechanisms e.g. B. have not yet been shown with fMRI scans.
use
Dybuster is split into three games. The color game trains the assignment of the colors to the letters, in the graph game hyphenation and their representation as a 2D structure are practiced, and in the educational game the words are spoken and must be entered using the multi-sensory display (see above). With the help of information theory and machine learning , the learning and error behavior of the user is analyzed in the background. Based on the analysis, the words are selected individually for each user. In addition, Dybuster contains a learning state for intrinsic motivation and a reward system for extrinsic motivation .
The users can use Dybuster independently. The following use is suggested, as it was also described for the studies:
- Exercise should last 15 to 20 minutes a day.
- The units should be regular, i. H. 3 to 4 times a week.
- When training with Dybuster is started, you should work with it at the intensity described for about 4 months so that the multi-sensory connections can be established.
A computer program cannot replace support from trained specialists. Unfortunately, it is hardly possible for specialists to work more than an hour a week with those affected. It is therefore suggested that Dybuster supplement the support with independent, possibly home-based training.
Individual evidence
- ^ Gross M, Vögeli C: A Multimedia Framework for Effective Language Training . In: Elsevier (Ed.): Computer & Graphics . 31, 2007, pp. 761-777.
- ↑ Kast, M., Meyer, M., Vögeli, C., Gross, M., & Jäncke, L .: Computer-based Multisensory Learning in Children with Developmental Dyslexia . In: IOS-Press (Ed.): Restorative Neurology and Neuroscience . 25 (3-4), 2007, pp. 355-369.
- ↑ Kast M., Baschera G.-M., Gross M., Meyer M. & Jaencke L .: Computer-based learning of spelling skills in children with and without dyslexia . In: Springer (Ed.): Annals of Dyslexia . 2011.
- ↑ Baschera, G.-M. & Gross, M .: A Phoneme-based Student Model for Adaptive Spelling Training . In: IOS-Press (Ed.): In Proceedings of Artificial Intelligence in Education . 2009, pp. 614-616.
- ↑ Baschera, G.-M. & Gross, M .: Poisson-Based Inference for Perturbation Models in Adaptive Spelling Training . In: IOS-Press (Ed.): International Journal of Artificial Intelligence in Education . 20, No. 4, 2010.