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.

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.

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.

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.

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.

IMS-LD标准在E-learning中的应用

随着互联网的飞速发展,网上学习的平台和标准建设日趋完善。通过对国际主流的E-learning学习标准的研究和分析,笔者认为基于IMS的LD标准规范设计的在线学习模式可以对我国高等教育形成有益的补充,并就这种标准的方法设计进行了探讨。

IMS-LD标准在E-learning中的应用

互联网的飞速发展使得在线学习拥有了越来越多的用户,而网上学习的平台和标准建设也日趋完善。通过对国际主流的E-learning学习标准的研究和分析,笔者认为基于IMS的LD标准规范设计的在线学习模式可以对我国高等教育形成有益的补充,并就这种标准的方法设计进行了探讨。

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.

一种基于语义网格的E-Learning系统框架

通过分析知识社会学习方式的特征以及建构学习环境的技术趋势,提出一种基于语义网格的E-Learning系统框架,阐述了在该框架下实现IMS LD和SCORM学习规范的模型.最后指出相关工作及进一步的研究方向.

IMS LD规范视域下的“学习单元”设计

怎样基于教育建模语言(Educational Modeling Language,简称EML)开展形式化层次的教学设计,是教学设计自动化领域中一个值得探讨的问题。IMS LD规范是一种经过改造的EML,具有目标、性质、基础、体系、提出者等五个方面的特点。基于IMS LD规范的学习(单元)设计有四条原理:指导思想原理、要素结构原理、设计层次原理、创设流程原理。从九个维度进一步对国际上已经推出的20多个IMS LD工具进行了归类扣比较,并以《教育技术原理》教改课程为例,阐述了基于IMS LD规范的"学习单元"设计的具体过程,简介了"学习单元"设计制品的四条应用途径。

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%.

A Requirement Driven Learning Management Architecture Based on BPEL

With the prosperity of the Intemet, e-learning has been greatly improved. By supporting multiple learners and multiple roles in a learning activity, the IMS Leaming Design （LD） specification provides a collaborative scenario for participants. However, IMS LD provides insufficient support for interaction among learning activities and can not dynamically integrate learning resources to meet the continually changing service requirements. In this paper, a Business Process Execution Language （BPEL） enhanced requirement driven learning management architecture to address the issues of personalize adaptive learning was proposed. It models the learning activity by combining IMS LD with BPEL and matches optimal learning sequence based on Case-based reasoning （CBR） method. By providing expandable secure learning sequences flexibly, it satisfies the different actual demands for personalize learning

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.

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%.

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.