Study on Self-consciousness of Children With Learning Disabilities and Related Factors

Objective To study the self-consciousness of children with learning disabilities (LD) and to identify related factors. Methods Five hundred and sixty pupils graded from 1 to 6 in an elementary school were investigated. According to the pupil rating scale revised screening for learning disabilities (PRS), combined Raven’s test (CRT) and achievement of main courses, 35 of 560 pupils were diagnosed as LD children. Thirty-five children were selected from the average children and 35 from advanced children in academic achievement equally matched in class, gender, and age with LD children as control groups. The three groups were tested by Piers-Harris children’s self-concept scale. Basic information of each subject was collected by self-made questionnaire. Results Compared with the average and advanced children, LD children got significantly lower scores in self-concept scale. Based on logistic regression analysis, 3 factors were identified, including family income per month, single child and delivery model. Conclusion The results suggest that self-consciousness of children with LD is lower than that of normal children.

Perceptual Visuo-Motor Skills and Handwriting Production of Children With Learning Disabilities

This study aimed to explore the performance of the perceptual-visuomotor skills and the production of handwriting in children with Learning Disabilities.A total of 56 children participated,being a convenience sample,of both sexes,average age of eight years old,from 3rd to 5th grade level of Elementary School.The children were divided into the following groups:GI(28 children diagnosed with Learning Disabilities);GII(28 children with good academic performance,paired with GI in relation to chronological age and sex).They were evaluated individually in dysgraphic scale,visual perception development test,and fine motor evaluation.Data analysis was performed.There was a significant difference between GI and GII for the subtests of eye-hand coordination,copying,visual closure,fine motor precision,and fine manual control tests.They had difference between the groups for handwriting performance in descending and/or ascending subtests,irregularity of dimension,poor forms,and total score of Dysgraphia Scale.The results presented in this study indicate that children with Learning Disabilities can manifest significant visomotor impairment and deficit in legibility and handwriting quality,causing failures in the elaboration of sensorimotor plans that,added to the intrinsic deficit of long-term memory,result in persistent academic difficulties.

Risk Factors of Learning Disabilities in Chinese Children in Wuhan

Objective To investigate prevalence rate of learning disabilities (LD) in Chinese children, and to explore related risk factors, and to provide theoretical basis for preventing such disabilities. Methods One thousand and one hundred fifty one children were randomly selected in primary schools. According to criteria set by ICD-10, 118 children diagnosed as LD were classified into the study group. Four hundred and ninety one children were classified into the normal control group. Five hundred and forty two children were classified into the excellent control group. The study instruments included PRS (The pupil rating scale revised screening for learning disabilities), Conners' children behavior check-list taken by parents and YG-WR character check-list. Results The prevalence rate of LD in Chinese children was 10.3%. Significant differences were observed between LD and normally learning children, and between the LD group and the excellent group, in terms of scores of Conners' behavior check-list (P<0.05). The study further showed that individual differences in character between the LD group and the control groups still existed even after controlling individual differences in age, IQ, and gender. Some possible causal explanations contributing to LD were improper teaching by parents, low educational level of the parents, and children's characteristics and social relationships. Conclusion These data underscore the fact that LD is a serious national public health problem in China. LD is resulted from a number of factors. Good studying and living environments should be created for LD children.

Translating Interdisciplinary Research on Language Learning into Identifying Specific Learning Disabilities in Verbally Gifted and Average Children and Youth

The current research was grounded in prior interdisciplinary research that showed cognitive ability (verbal ability for translating cognitions into oral language) and multiple-working memory endophenotypes (behavioral markers of genetic or brain bases of language learning) predict reading and writing achievement in students with and without specific learning disabilities in written language (SLDs-WL). Results largely replicated prior findings that verbally gifted with dyslexia score higher on reading and writing achievement than those with average verbal ability but not on endophenotypes. The current study extended that research by comparing those with and without SLDs-WL with assessed verbal ability held constant. The verbally gifted without SLDs-WL (n = 14) scored higher than the verbally gifted with SLDs-WL (n = 27) on six language skills (oral sentence construction, best and fastest handwriting in copying, single real word oral reading accuracy, oral pseudoword reading accuracy and rate) and four endophenotypes (orthographic and morphological coding, orthographic loop, and switching attention). The verbally average without SLDs-WL (n = 6) scored higher than the verbally average with SLDs-WL (n = 22) on four language skills (best and fastest hand-writing in copying, oral pseudoword reading accuracy and rate) and two endophenotypes (orthographic coding and orthographic loop). Implications of results for translating interdisciplinary research into flexible definitions for assessment and instruction to serve students with varying verbal abilities and language learning and endophenotype profiles are discussed along with directions for future research.

The Differences in the Attitudes of Students With and Without Learning Disabilities Towards Including Students With Mild Intellectual Disability in Elementary Regular Schools in Bahrain

The purpose of this study was to investigate the differences in the attitudes of the students with and without learning disabilities(LD)towards including the learners with mild intellectual disability in regular elementary schools.Participants were 120 elementary boys and girls in Bahraini elementary schools.A survey was used here to identify the students’attitudes towards the inclusion of the children with mild intellectual disability in regular schools.The results indicated that:(1)There was a positive attitude among elementary students towards including the children with mild intellectual disability in regular schools;(2)there were statistically significant differences among the students with and without learning disabilities in their attitudes towards including the children with mild intellectual disability in regular elementary schools in favour of the typically developing group;and(3)there were statistically significant differences among the male and female students with and without learning disabilities in their attitudes towards including the children with mild intellectual disability in regular schools in favour of females.

Preschoolers＇ Learning Disabilities Assessment： New Perspectives in Computerized Clinical Tools

This study looks into new perspectives in preschoolers＇ assessment of being at risk for learning disabilities. Precisely, two innovative assessment approaches are examined in order to reveal new research perspectives. The first tool, a traditional approach, is the ＂Early Dyslexia Identification Test＂ and the second tool, a computerized approach, is an lnternet based Speech Pathology Diagnostic Expert System named ＂APLo＂. Both evaluate the sectors of phonological awareness, memory, psychomotor development, pre-writing and pre-reading skills in Greek. The findings o f the current study formulate three directions： （1） the complementary of speech language and learning disorders as a systemic approach, （2） the diagnosis of suspicious factors and compatibilities of learning disabilities even at the preschool age, and （3） the application of alternative methods of assessment aiming for a multidimentional approach with the combined prospect and potential of web tools in the early diagnosis and intervention in learning disabilities.

Neuropsychological characteristics of selective attention in children with nonverbal learning disabilities

Background Children with nonverbal learning disabilities (NLD) usually manifest defective attention function This study sought to investigate the neuropsychological characteristics of selective attention, such as attention control, working memory, and attention persistence of the frontal lobe in children with NLD Methods Using the auditory detection test (ADT), Wisconsin card sorting test (WCST), and C WISC, 27 children with NLD and 33 normal children in the control group were tested, and the results of C WISC subtests were analyzed with factor analysis Results Compared with the control group, the correct response rate in the auditory detection test in the NLD group was much lower ( P <0 01), and the number of incorrect responses was much higher ( P <0 01); NLD children also scored lower in WCST categories achieved (CA) and perseverative errors (PE) ( P <0 05) Factor analysis showed that perceptual organization (PO) related to visual space and freedom from distractibility (FD) relating to attention persistence in the NLD group were obviously lower than in the control group ( P <0 01) Conclusions Children with NLD have attention control disorder and working memory disorder mainly in the frontal lobe We believe that the disorder is particularly prominent in the right frontal lobe

Family Support Situation and Educational Strategies for Primary School Children with Intellectual Disabilities Learning in Regular Class

This paper aims to verify the family support situation for primary school children with intellectual disabilities learning in regular class and to explore various educational strategies to promote their development.A self-made questionnaire was used in this survey,and the parents of 380 intellectual disabled students were the subjects of this survey.It turns out that the overall family support for intellectual disabled children learning in regular class in China is good,but it is affected by the degree of obstacles.Factors such as grade,gender,and parental education had no significant effect on family support.It is the shared responsibility of the government,schools,and parents to promote the level of family support.Governments at all levels must implement family support projects,schools must carry out family education guidance to impart scientific parenting knowledge,and parents must take note of their own responsibilities,so as to promote the physical and mental development of children with intellectual disabilities.

Hand Gesture Recognition for Disabled People Using Bayesian Optimization with Transfer Learning

Sign language recognition can be treated as one of the efficient solu-tions for disabled people to communicate with others.It helps them to convey the required data by the use of sign language with no issues.The latest develop-ments in computer vision and image processing techniques can be accurately uti-lized for the sign recognition process by disabled people.American Sign Language(ASL)detection was challenging because of the enhancing intraclass similarity and higher complexity.This article develops a new Bayesian Optimiza-tion with Deep Learning-Driven Hand Gesture Recognition Based Sign Language Communication(BODL-HGRSLC)for Disabled People.The BODL-HGRSLC technique aims to recognize the hand gestures for disabled people’s communica-tion.The presented BODL-HGRSLC technique integrates the concepts of compu-ter vision(CV)and DL models.In the presented BODL-HGRSLC technique,a deep convolutional neural network-based residual network(ResNet)model is applied for feature extraction.Besides,the presented BODL-HGRSLC model uses Bayesian optimization for the hyperparameter tuning process.At last,a bidir-ectional gated recurrent unit(BiGRU)model is exploited for the HGR procedure.A wide range of experiments was conducted to demonstrate the enhanced perfor-mance of the presented BODL-HGRSLC model.The comprehensive comparison study reported the improvements of the BODL-HGRSLC model over other DL models with maximum accuracy of 99.75%.

Lotus LearningSpace在高等特殊教育教学领域的应用

Lotus LearningSpace是Lotus在知识管理策略基础上推出的最新教学解决方案,旨在提供一个可协作、可按计划、辅助指导、分布式的虚拟学习环境。Lotus LearningSpace简体中文版,为国内有关高等特殊教育的研究机构提供了一个很好的参考。斯塔福德郡大学的实验表明,相关软件开发必须要了解各类残疾人群的特点,考虑残疾学生的主要缺陷,开发专门适合残疾学生的、适用领域广泛的虚拟学习系统。

Deep Transfer Learning-Enabled Activity Identification and Fall Detection for Disabled People

The human motion data collected using wearables like smartwatches can be used for activity recognition and emergency event detection.This is especially applicable in the case of elderly or disabled people who live self-reliantly in their homes.These sensors produce a huge volume of physical activity data that necessitates real-time recognition,especially during emergencies.Falling is one of the most important problems confronted by older people and people with movement disabilities.Numerous previous techniques were introduced and a few used webcam to monitor the activity of elderly or disabled people.But,the costs incurred upon installation and operation are high,whereas the technology is relevant only for indoor environments.Currently,commercial wearables use a wireless emergency transmitter that produces a number of false alarms and restricts a user’s movements.Against this background,the current study develops an Improved WhaleOptimizationwithDeep Learning-Enabled Fall Detection for Disabled People(IWODL-FDDP)model.The presented IWODL-FDDP model aims to identify the fall events to assist disabled people.The presented IWODLFDDP model applies an image filtering approach to pre-process the image.Besides,the EfficientNet-B0 model is utilized to generate valuable feature vector sets.Next,the Bidirectional Long Short Term Memory(BiLSTM)model is used for the recognition and classification of fall events.Finally,the IWO method is leveraged to fine-tune the hyperparameters related to the BiLSTM method,which shows the novelty of the work.The experimental analysis outcomes established the superior performance of the proposed IWODL-FDDP method with a maximum accuracy of 97.02%.

Parameter-Tuned Deep Learning-Enabled Activity Recognition for Disabled People

Elderly or disabled people can be supported by a human activity recognition(HAR)system that monitors their activity intervenes and pat-terns in case of changes in their behaviors or critical events have occurred.An automated HAR could assist these persons to have a more indepen-dent life.Providing appropriate and accurate data regarding the activity is the most crucial computation task in the activity recognition system.With the fast development of neural networks,computing,and machine learning algorithms,HAR system based on wearable sensors has gained popularity in several areas,such as medical services,smart homes,improving human communication with computers,security systems,healthcare for the elderly,mechanization in industry,robot monitoring system,monitoring athlete train-ing,and rehabilitation systems.In this view,this study develops an improved pelican optimization with deep transfer learning enabled HAR(IPODTL-HAR)system for disabled persons.The major goal of the IPODTL-HAR method was recognizing the human activities for disabled person and improve the quality of living.The presented IPODTL-HAR model follows data pre-processing for improvising the quality of the data.Besides,EfficientNet model is applied to derive a useful set of feature vectors and the hyperparameters are adjusted by the use of Nadam optimizer.Finally,the IPO with deep belief network(DBN)model is utilized for the recognition and classification of human activities.The utilization of Nadam optimizer and IPO algorithm helps in effectually tuning the hyperparameters related to the EfficientNet and DBN models respectively.The experimental validation of the IPODTL-HAR method is tested using benchmark dataset.Extensive comparison study highlighted the betterment of the IPODTL-HAR model over recent state of art HAR approaches interms of different measures.

Deep Transfer Learning Driven Automated Fall Detection for Quality of Living of Disabled Persons

Mobile communication and the Internet of Things(IoT)technologies have recently been established to collect data from human beings and the environment.The data collected can be leveraged to provide intelligent services through different applications.It is an extreme challenge to monitor disabled people from remote locations.It is because day-to-day events like falls heavily result in accidents.For a person with disabilities,a fall event is an important cause of mortality and post-traumatic complications.Therefore,detecting the fall events of disabled persons in smart homes at early stages is essential to provide the necessary support and increase their survival rate.The current study introduces a Whale Optimization Algorithm Deep Transfer Learning-DrivenAutomated Fall Detection(WOADTL-AFD)technique to improve the Quality of Life for persons with disabilities.The primary aim of the presented WOADTL-AFD technique is to identify and classify the fall events to help disabled individuals.To attain this,the proposed WOADTL-AFDmodel initially uses amodified SqueezeNet feature extractor which proficiently extracts the feature vectors.In addition,the WOADTLAFD technique classifies the fall events using an extreme Gradient Boosting(XGBoost)classifier.In the presented WOADTL-AFD technique,the WOA approach is used to fine-tune the hyperparameters involved in the modified SqueezeNet model.The proposedWOADTL-AFD technique was experimentally validated using the benchmark datasets,and the results confirmed the superior performance of the proposedWOADTL-AFD method compared to other recent approaches.

Deep Learning-Based Sign Language Recognition for Hearing and Speaking Impaired People

Sign language is mainly utilized in communication with people who have hearing disabilities.Sign language is used to communicate with people hav-ing developmental impairments who have some or no interaction skills.The inter-action via Sign language becomes a fruitful means of communication for hearing and speech impaired persons.A Hand gesture recognition systemfinds helpful for deaf and dumb people by making use of human computer interface(HCI)and convolutional neural networks(CNN)for identifying the static indications of Indian Sign Language(ISL).This study introduces a shark smell optimization with deep learning based automated sign language recognition(SSODL-ASLR)model for hearing and speaking impaired people.The presented SSODL-ASLR technique majorly concentrates on the recognition and classification of sign lan-guage provided by deaf and dumb people.The presented SSODL-ASLR model encompasses a two stage process namely sign language detection and sign lan-guage classification.In thefirst stage,the Mask Region based Convolution Neural Network(Mask RCNN)model is exploited for sign language recognition.Sec-ondly,SSO algorithm with soft margin support vector machine(SM-SVM)model can be utilized for sign language classification.To assure the enhanced classifica-tion performance of the SSODL-ASLR model,a brief set of simulations was car-ried out.The extensive results portrayed the supremacy of the SSODL-ASLR model over other techniques.

Use of Social Networks to Affect the Teaching and Learning Effectiveness of 3D Design

In recent years, with the rapid development of technologies, information technological software and social networks have been widely accepted. Therefore, social networks can be integrated with information technologies for teaching purposes. In addition, the sharing of learning outcomes via social networks can improve students’ learning effectiveness. This study used an information technology teaching environment to teach students 3D skills, and used 3D SketchUp to enable students to explore, operate, and complete their personal works by themselves. Moreover, this study used Facebook as the media of a WBI (web-based instruction) community, and used the discussions and sharing between students and students, and students and teachers, to improve learning effectiveness and reduce learning disabilities. The research results showed that, proper use of social networks to provide students with opportunities to discuss and share outcomes can help improve students’ learning effectiveness and reduce learning disabilities.

Curriculum Adaptation in Special Schools for Students with Intellectual Disabilities(SID):A Case Study of Project Learning in One SID School in Hong Kong

Using a qualitative case study approach,the authors analyzed the curriculum adaptation process for one project learning activity in School K,which is a SID school in the context of school-university collaboration.Multiple sources of data were collected for triangulation,including interviews,documents and observations.Curriculum adaptation strategies in this study were analyzed from five perspectives:instructional goals,instructional content,instructional strategies,instructional settings,and student behavioral needs.It was found that curriculum adaptation efforts could help students with ID develop potential at their own level through project learning activates and teachers could also gain professional development during the university-school collaboration process.As for future studies,enlarging the sample size,involving teachers’past orientations and motivations in the project learning process,and collecting quantitative data could all be taken into account.

Automated Disabled People Fall Detection Using Cuckoo Search with Mobile Networks

Falls are the most common concern among older adults or disabled peo-ple who use scooters and wheelchairs.The early detection of disabled persons’falls is required to increase the living rate of an individual or provide support to them whenever required.In recent times,the arrival of the Internet of Things(IoT),smartphones,Artificial Intelligence(AI),wearables and so on make it easy to design fall detection mechanisms for smart homecare.The current study devel-ops an Automated Disabled People Fall Detection using Cuckoo Search Optimi-zation with Mobile Networks(ADPFD-CSOMN)model.The proposed model’s major aim is to detect and distinguish fall events from non-fall events automati-cally.To attain this,the presented ADPFD-CSOMN technique incorporates the design of the MobileNet model for the feature extraction process.Next,the CSO-based hyperparameter tuning process is executed for the MobileNet model,which shows the paper’s novelty.Finally,the Radial Basis Function(RBF)clas-sification model recognises and classifies the instances as either fall or non-fall.In order to validate the betterment of the proposed ADPFD-CSOMN model,a com-prehensive experimental analysis was conducted.The results confirmed the enhanced fall classification outcomes of the ADPFD-CSOMN model over other approaches with an accuracy of 99.17%.

从随班到适宜:残疾儿童融合教育的历史嬗变、发展困境与优化策略

2021年教育部出台政策明确提出要以“适宜融合”为目标办好特殊教育,全面推动我国特殊教育质量提升及随班就读模式转型升级。二战后以美国为代表的西方国家在残疾儿童融合教育方面先后开展了“去机构化”“回归主流”“一体化教育”“全纳教育”等实践探索,呈现了从单向推动到双向参与再到追求公平而优质发展的阶段性特征。长期以来,我国开展随班就读工作以提高残疾儿童义务教育入学率为主要目标,粗放式发展模式导致残疾儿童随班就读“合而不融”。新时代背景下,融合教育发展回归“适异而育”的核心本义是全面提升特殊教育质量的内在要求。借鉴美国经验,我国应进一步完善普通学校残疾儿童随班就读教学支持机制、加强对残疾儿童接受普通教育的能力表现评估、提高普通学校教师的融合教育教学专业素养、营造残疾儿童适宜发展的融合教育生态场域。

残疾儿童家庭教育的挑战与对策研究

高质量教育对残疾人实现共同富裕具有奠基作用,需要家庭与学校协同。较之残疾儿童的学校教育,其家庭教育是当前研究的薄弱环节。残疾儿童家庭教育的特殊性和挑战性均较突出,主要面临经济状况、父母心理健康、教育新要求应对能力不足等困境,其成因既有个体因素,也有环境因素。鉴于此,应通过精准支持减轻残疾儿童父母的经济压力与心理压力,提升其教育效能感,为实现共同富裕目标提供重要支撑。