Abstract: Autism Spectrum Disorder (ASD) is the fastest growing serious developmental disorder characterized by social deficits, communicative difficulties, and repetitive behaviors. ASD is an emerging public health issue globally which is associated with huge financial burden to the family, community and the nation. The aim of this study was to assess the financial burden of family for the children with Autism spectrum Disorder. This cross-sectional study was carried out from July 2015 to June 2016 among 154 children with ASD to assess the financial burden of family. Data were collected by face-to-face interview with semi-structured questionnaire following systematic random sampling technique. Majority (73.4%) children were male and mean (±SD) age was 6.66 ± 2.97 years. Most (88.8%) of the children were from urban areas with average monthly family income Tk. 41785.71±23936.45. Average monthly direct cost of the children was Tk.17656.49 ± 9984.35, while indirect cost was Tk. 13462.90 ± 9713.54 and total treatment cost was Tk. 23076.62 ± 15341.09. Special education cost (Tk. 4871.00), cost of therapy (Tk. 4124.07) and travel cost (Tk. 3988.31) were the major types of direct cost, while loss of income (Tk.14570.18) was the chief indirect cost incurred by the families. The study found that majority (59.8%) of the children attended special schools were incurred Tk.20001-78700 as total treatment cost, which were statistically significant (p
Abstract: Phonological disorder is a serious and disturbing issue to many parents and teachers. Efforts towards resolving the problem have been undermined by other specific disabilities which were hidden to many regular and special education teachers. It is against this background that this study was motivated to provide data on the prevalence of phonological disorders in children with specific language impairment (CWSLI) as the first step towards critical intervention. The study was a survey of 15 CWSLI from St. Louise Inclusive schools, Ikot Ekpene in Akwa Ibom State of Nigeria. Phonological Processes Diagnostic Scale (PPDS) with 17 short sentences, which cut across the five phonological processes that were examined, were validated by experts in test measurement, phonology and special education. The respondents were made to read the sentences with emphasis on the targeted sounds. Their utterances were recorded and analyzed in the language laboratory using Praat Software. Data were also collected through friendly interactions at different times from the clients. The theory of generative phonology was adopted for the descriptive analysis of the phonological processes. Data collected were analyzed using simple percentage and composite bar chart for better understanding of the result. The study found out that CWSLI exhibited the five phonological processes under investigation. It was revealed that 66.7%, 80%, 73.3%, 80%, and 86.7% of the respondents have severe deficit in fricative stopping, velar fronting, liquid gliding, final consonant deletion and cluster reduction, respectively. It was therefore recommended that a nationwide survey should be carried out to have national statistics of CWSLI with phonological deficits and develop intervention strategies for effective therapy to remediate the disorder.
Abstract: Avoiding learning failures in mathematics e-learning environments caused by emotional problems in students with autism has become an important topic for combining of special education with information and communications technology. This study presents an adaptive emotional adjustment model in mathematics e-learning for students with autism, emphasizing the lack of emotional perception in mathematics e-learning systems. In addition, an emotion classification for students with autism was developed by inducing emotions in mathematical learning environments to record changes in the physiological signals and facial expressions of students. Using these methods, 58 emotional features were obtained. These features were then processed using one-way ANOVA and information gain (IG). After reducing the feature dimension, methods of support vector machines (SVM), k-nearest neighbors (KNN), and classification and regression trees (CART) were used to classify four emotional categories: baseline, happy, angry, and anxious. After testing and comparisons, in a situation without feature selection, the accuracy rate of the SVM classification can reach as high as 79.3-%. After using IG to reduce the feature dimension, with only 28 features remaining, SVM still has a classification accuracy of 78.2-%. The results of this research could enhance the effectiveness of eLearning in special education.
Abstract: Assessment of IEP (Individual Education Plan) is an
important stage in the area of special education. This paper deals
with this problem by introducing computer software which process
the data gathered from application of IEP. The software is intended
to be used by special education institution in Turkey and allows
assessment of school and family trainings. The software has a user
friendly interface and its design includes graphical developer tools.