Erratum to: Computer-based learning of spelling skills in children with and without dyslexia

Our spelling training software recodes words into multisensory representations comprising visual and auditory codes. These codes represent information about letters and syllables of a word. An enhanced version, developed for this study, contains an additional phonological code and an improved word selection controller relying on a phoneme-based student model. We investigated the spelling behavior of children by means of learning curves based on log-file data of the previous and the enhanced software version. First, we compared the learning progress of children with dyslexia working either with the previous software (n = 28) or the adapted version (n = 37). Second, we investigated the spelling behavior of children with dyslexia (n = 37) and matched children without dyslexia (n = 25). To gain deeper insight into which factors are relevant for acquiring spelling skills, we analyzed the influence of cognitive abilities, such as attention functions and verbal memory skills, on the learning behavior. All investigations of the learning process are based on learning curve analyses of the collected log-file data. The results evidenced that those children with dyslexia benefit significantly from the additional phonological cue and the corresponding phoneme-based student model. Actually, children with dyslexia improve their spelling skills to the same extent as children without dyslexia and were able to memorize phoneme to grapheme correspondence when given the correct support and adequate training. In addition, children with low attention functions benefit from the structured learning environment. Generally, our data showed that memory sources are supportive cognitive functions for acquiring spelling skills and for using the information cues of a multi-modal learning environmen

A phoneme-based student model for adaptive spelling training

We present a novel phoneme-based student model for spelling training. Our model is data driven, adapts to the user and provides information for, e.g., optimal word selection. We describe spelling errors using a set of features accounting for phonemic, capitalization, typo, and other error categories. We compute the influence of individual features on the error expectation values based on previous input data using Poisson regression. This enables us to predict error expectation values and to classify errors probabilistically. While our main focus is on spelling training for dyslexic children, our model is generic and can be utilized within any intelligent language learning environment

Modelling and optimizing the process of learning mathematics

This paper introduces a computer-based training program for enhancing numerical cognition aimed at children with developmental dyscalculia. Through modelling cognitive processes and controlling the level of their stimulation, the system optimizes the learning process. Domain knowledge is represented with a dynamic Bayesian network on which the mechanism of automatic control operates. Accumulated knowledge is estimated to select informative tasks and to evaluate student actions. This adaptive training environment equally improves success and motivation. Large-scale experimental data quantifies substantial improvement and validates the advantages of the optimized training

Computer-based learning of spelling skills in children with and without dyslexia

ISSN:0736-9387ISSN:1934-724