- Dybuster
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Dybuster Type Public Industry Educational software Founded Zurich, CH (2007) Headquarters Zurich Website Homepage Dybuster is a multi-sensory, computer based therapy system for people with dyslexia. It was developed at ETH Zurich and evaluated in scientific user studies together with neuropsychologists from University of Zurich. The therapy system is marketed by Dybuster International, a university spin-off from ETH Zurich.
Contents
Scientific background
Hypothesis
Dyslexia probably has many causes such as auditory and visual deficits. One main deficit seems to be that dyslexics have difficulties in mapping spoken language to written language. This so called phonological deficit makes it harder to map phonemes (smallest elements of speech) to graphemes (letters or letter combinations representing phonemes) and vice-versa. Neuroscientific research showed that the brain areas responsible for this mapping are less active in dyslexics when reading or writing than in people without reading and writing difficulties. Researchers suggest that these areas may never develop the same capacities in dyslexics independent of training.
The main hypothesis of Dybuster is that by using multisensory learning in a very specific, mathematically controlled way, the dyslexic brain is enabled to use undistorted pathways and areas to map the spoken language to the written language, i.e. to perform the phoneme-grapheme-mapping. It can thus compensate for the phonological deficit and possibly also for auditory and visual deficits.
Mechanics
The Dybuster Therapy Concept hence employs multisensory learning to address new channels for learning orthography. A word is not just represented as black letters on a white sheet of paper; rather it is translated to a sequence of colors, shapes and musical notes. Additionally, the syllabication is displayed as a two-dimensional structure. While training, words are read aloud, the multisensory representation is displayed, and the user has to type the word with the help of the representation. The representation thus permanently links the spoken word (phonemes) to the written word (graphemes).
Using information theory, one of the most important theories in Computer Science, the multisensory representation is designed and computed to contain the same amount of information as the original letter sequence [1]. The goal of this computation is to provide as much information as necessary and as little information as possible to completely represent the entire word information. If not enough information was provided, the phoneme to grapheme mapping through undistorted pathways would remain incomplete. And if too much information was provided, the brain might not be able to automatize. Multisensory learning is considered the most efficient form of learning in Neuropsychology, but if the associations are irregular and too many, automatization can be slowed down.
Using information theory, artificial intelligence and techniques from student modeling, the error and learning behavior of each user is modeled and exactly analyzed, allowing for an optimal, mathematically controlled guidance of the learner [2], [3]. Furthermore, Dybuster provides immediate feedback. A correct key stroke is met with the sound belonging to the letter, where as an error sound is played upon an incorrect key stroke. This allows a user to instantly correct mistakes. The feedback therefore reduces the number of wrong word shapes that a user memorizes.
Effectiveness
The effectiveness of Dybuster training was explored in several user studies [4]. The authors concluded that the children with developmental dyslexia and controls substantially improved their writing by practicing 15-30 minutes a day for about 3 months. This improvement was also evident for non-learned words that they did not practice in the Dybuster training.
The studies report an improvement of up to 35% in writing performance on average over all developmental dyslexics which participated in the studies. In contrary, carefully matched dyslexics that were not allowed to use Dybuster and only attended their regular dyslexia training, only improved by 5 to 6% on average.
The precise impact on the underlying neurological and psychological mechanism is not fully understood. Latest studies [5] showed that the phoneme-grapheme correspondence is strengthened through the Dybuster training. This supports the underlying hypothesis of Dybuster.
Usage
Three different games are included in Dybuster. Dybuster provides a learning overview for intrinsic motivation and a rewards shop for extrinsic motivation.
In the first game, namely the color game, the users have to learn the association between a letter and a color. In the second game, namely the graph game, the users have to segment the word into its syllables and letters graphically. In the third game, the actual learning game, Dybuster presents all alternative representations of a word before the users enter the word itself using the keyboard: the graph appears on screen, and the colors and the shapes (spheres for small letters, cylinders for capital letters, and pyramids for umlauts or accents) are displayed for all letters. A voice dictates the word and the users hear a melody computed from the involved letters and the lengths of the syllables (compare Mechanics above).
In the studies conducted, the training was organized as follows, which is also the suggested usage for home use:
- The children should work fifteen to twenty minutes per day. The units are short, motivating and are played in a game form.
- The training should take place on a regular basis, at least three to four times a week.
- One should train with Dybuster intensively for a minimum of three to four months, so that the associations from the multi-sensory learning are sufficiently strengthened.
Many users keep training continually with Dybuster for further support throughout their school years.
References
- ^ Gross M, Vögeli C (2007). "A Multimedia Framework for Effective Language Training". Computer & Graphics (Elsevier) 31: 761–777.
- ^ Baschera, G.-M. & Gross, M. (2009). "A Phoneme-based Student Model for Adaptive Spelling Training". In Proceedings of Artificial Intelligence in Education (IOS-Press): 614–616.
- ^ Baschera, G.-M. & Gross, M. (2010). "Poisson-Based Inference for Perturbation Models in Adaptive Spelling Training". International Journal of Artificial Intelligence in Education (IOS-Press) 20 (4).
- ^ Kast, M., Meyer, M., Vögeli, C., Gross, M., & Jäncke, L. (2007). "Computer-based Multisensory Learning in Children with Developmental Dyslexia". Restorative Neurology and Neuroscience (IOS-Press) 25 (3-4): 355–369.
- ^ Kast M., Baschera G.-M., Gross M., Meyer M. & Jaencke L. (2011). "Computer-based learning of spelling skills in children with and without dyslexia". Annals of Dyslexia (Springer).
See also
External links
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