Designing the content of religious education learning in creating sustainability among children with learning disabilities: A fuzzy delphi analysis.

Teachers found it is hard to figure out what are the best approach and strategies shall be employed to create an effective learning activity that can benefit the children. Children with learning disabilities have distinctive learning difficulties, depending on each individual. Therefore, this requires modification and adaptation in the learning activities to make sure they can learn effectively. Teachers need to make adjustment to the instructions, learning materials, assessments, and activities to accommodate the children with learning disabilities. Therefore, the objective of this research is to develop the content of religious education for children with learning disabilities using fuzzy delphi. This research used method of design and developmental research approach which have three phases. In this research, the researchers focus on the second phase of fuzzy delphi. There were 20 panel experts involved in this research to rank the elements in developing religious education model. Findings showed that, all the elements such as learning style, rights of people with disabilities manners and universal design were above 70% that considered suitable and applicable. It is hoped that this model can assist and guide teachers in teaching religious education for children with learning disabilities.

Challenges and Elements Needed for Children with Learning Disabilities in Teaching and Learning the Quran.

People with disabilities have the same right to access education and, therefore, the space and opportunity to study the Quran as other groups. However, there are some issues regarding the teaching of the Quran to students with special learning needs, such as from the aspects of the level of readiness of teachers, the mastery of Islamic Education among teachers who teach Special Education, and the use of teaching aids to teach the Quran. Therefore, this study aimed to identify the challenges, the need to develop a model of teaching and learning the Quran, elements to be included in the development of Quranic teaching, and a learning model for children with learning disabilities. A qualitative methodology was adopted using a case study design. The sample consisted of eight informants who volunteered to be involved in this research. The results show that there are seven main challenges in the teaching and learning of children with learning disabilities, i.e., a lack of stimulus materials, a lack of knowledge, limited time, uncontrolled behavior, traditional teaching, disabilities, and a lack of parental commitment. Thereafter, three themes arose in terms of identifying why it is necessary to develop Quran teaching and learning models for children with learning disabilities, such as the lack of an up-to-date model and the right to education. In addition, there were four themes concerning the elements that need to be included in the development of teaching and learning models for the Quran for children with learning disabilities, i.e., digital teaching aids, visual, audio, and kinesthetic learning styles, activities graded by the level of ability, and sensory support. It is hoped that this study will provide guidance to teachers to further strengthen the teaching profession pertaining to educating children with learning disabilities.

The Influence of Sociodemographic Factors on Mobile Device Use among Young Children in Putrajaya, Malaysia.

Technology is evolving rapidly around the world, and the use of mobile devices is increasing every day. Today, everyone owns a mobile device, including young children. Parents provide and allow young children to use mobile devices for various purposes. Due to the fact of these circumstances, children begin to become comfortable with the use of mobile devices, and they are prone to excessive use. Therefore, the purpose of this study was to examine the influence of sociodemographic factors on excessive mobile device use among young children. Sociodemographic variables, including the child's gender, the child's age when starting to use a mobile device, the parent's educational level, household income, type of application used, and the purpose of giving a mobile device to the child, were selected as predictive factors. A cross-sectional survey study design with a quantitative approach was conducted. A simple random sampling technique was employed, and a total of 364 parents completed the adapted questionnaire, namely, the Problematic Mobile Phone Use Scale (PMPUS). Data were statistically analyzed using descriptive and binary logistic regression analysis. The findings revealed that gender, age of the child when starting to use mobile devices, and purpose of parents providing mobile devices significantly contributed to 77.7% of the variance to make children users with a problem. However, the parent's educational level, household income, and type of application did not significantly contribute to the problem of mobile device use. Later, this study discusses the research implication, limitation, and recommendation for future research based on the finding.

A Review of Machine Learning Methods of Feature Selection and Classification for Autism Spectrum Disorder.

Autism Spectrum Disorder (ASD), according to DSM-5 in the American Psychiatric Association, is a neurodevelopmental disorder that includes deficits of social communication and social interaction with the presence of restricted and repetitive behaviors. Children with ASD have difficulties in joint attention and social reciprocity, using non-verbal and verbal behavior for communication. Due to these deficits, children with autism are often socially isolated. Researchers have emphasized the importance of early identification and early intervention to improve the level of functioning in language, communication, and well-being of children with autism. However, due to limited local assessment tools to diagnose these children, limited speech-language therapy services in rural areas, etc., these children do not get the rehabilitation they need until they get into compulsory schooling at the age of seven years old. Hence, efficient approaches towards early identification and intervention through speedy diagnostic procedures for ASD are required. In recent years, advanced technologies like machine learning have been used to analyze and investigate ASD to improve diagnostic accuracy, time, and quality without complexity. These machine learning methods include artificial neural networks, support vector machines, a priori algorithms, and decision trees, most of which have been applied to datasets connected with autism to construct predictive models. Meanwhile, the selection of features remains an essential task before developing a predictive model for ASD classification. This review mainly investigates and analyzes up-to-date studies on machine learning methods for feature selection and classification of ASD. We recommend methods to enhance machine learning's speedy execution for processing complex data for conceptualization and implementation in ASD diagnostic research. This study can significantly benefit future research in autism using a machine learning approach for feature selection, classification, and processing imbalanced data.