ICTs and Speed Learning in Special Education: High-Consciousness Training Strategies for High-Capacity Learners through Metacognition Lens

Education in the 21st century is called upon to prepare students with disabilities to enter a high-consciousness society where people can learn, think and react fast. The current review paper aims at investigating the role of fast learning in special education. We trace the essential indicators of speed learning with a special focus on those factors that are most relevant to learning disabilities. Afterward, we present evidence-based training techniques and strategies that speed up learning. In addition, we examine the role of ICTs as essential training tools in speed learning. Finally, we discuss the role of metacognition in training fast and conscious learners. The results of this review showed that speed learning training techniques improve all those factors that accelerate learning such as spatial attention, visual span, processing speed, speed reaction, executive functions, metacognition and consciousness. Most important, fast learning strategies meliorate control processes and spatial intelligence which is extremely fast and powerful. This study also points to the option of including high-speed training techniques in schools to help children with or without disabilities to become conscious and high-capacity learners.

Ebook Features in Mathematics Education

This article presents a theoretical study of the ebook for teaching mathematics in a mixed environment, which became ubiquitous as a result of the COVID-19 pandemic. The paper shows the typical features of ebooks, which reveal the advantages and disadvantages of their use. The main opportunities for their development are presented, such as training techniques and additional support materials to improve the maths lesson. In literature, the ebook in mathematics education is considered in various forms and aspects, with the main advantage being their ability to illustrate dynamic mathematical learning content with the help of ICT, while respecting the principles of multimedia and the requirements for accessibility, ergonomics and visual design.

Neuro-Linguistic Programming & VR via the 8 Pillars of Metacognition X 8 Layers of Consciousness X 8 Intelligences

Neuro-linguistic programming (NLP) has already achieved great popularity as a method for personal development and excellence. It is already used by successful educators, managers, trainers, salespeople, market researchers, counselors, consultants, medics, top athletes and lawyers. However, there is a lack of understanding about the secrets behind the success of neuro-linguistic training in various areas of human life. What are the pillars of its success? What is the role of metacognition? The specific aim of the present review is to explore the relationship between neuro-linguistic programming and metacognition as well as their role in building human excellence. In addition, we investigate, for the first time to our knowledge, the effectiveness of NLP in virtual reality in order to promote metacognitive development in terms of behavior change, subconscious reshaping and consciousness-raising. The results of this review showed that there is a mutually reinforcing relationship between Neuro-linguistic programming and Metacognition. Research has also shown that virtual reality provides the ideal environment for the application of subconscious training techniques like those of NLP. We conclude with a new layered model of NLP based on the principles of metacognition. This model aims to condition people to become awake, transcend their limitations, and enter a higher state of consciousness.

Pneumonia detection based on transfer learning and a combination of VGG19 and a CNN Built from scratch

In this paper, to categorize and detect pneumonia from a collection of chest X-ray picture samples, we propose a deep learning technique based on object detection, convolutional neural networks, and transfer learning. The proposed model is a combination of the pre-trained model (VGG19) and our designed architecture. The Guangzhou Women and Children's Medical Center in Guangzhou, China provided the chest X-ray dataset used in this study. There are 5,000 samples in the data set, with 1,583 healthy samples and 4,273 pneumonia samples. Preprocessing techniques such as contrast limited adaptive histogram equalization (CLAHE) and brightness preserving bi-histogram equalization was also used (BBHE) to improve accuracy. Due to the imbalance of the data set, we adopted some training techniques to improve the learning process of the samples. This network achieved over 99% accuracy due to the proposed architecture that is based on a combination of two models. The pre-trained VGG19 as feature extractor and our designed convolutional neural network (CNN).

Promise and performance of gender mainstreaming at a Zimbabwean agricultural training college

Gender equality in education and training can be achieved only if curricula at every level of the system become gender-sensitive. The present study examines the extent to which the milieu at one agricultural training college in Zimbabwe promotes the implementation of gender-sensitive training. The main investigative question posed was as follows: To what extent is the agricultural education and training curriculum used at the college gender-sensitive? By responding to this question, the study provided some response to the performance, challenges and prospects for gender mainstreaming in the college’s agricultural education curriculum. Data for this study were generated by document analysis of policy, curricular and instructional documents, interviews with 12 college lecturers, four college administrators and selected final year students, and by lesson observations. The study revealed that while government, and to a lesser extent college policies, articulate the need for gender equality, little attention is paid to these invocations in practice. Likewise, agricultural education and training curricula, training techniques, learning-support materials and out-of-class activities reflect minimal attention to issues of gender equality. The article concludes by discussing possible interventions that correspond to these findings.

Study on a visual coder acceleration algorithm for image classification applying dynamic scaling training techniques

Nowadays, image classification techniques are used in the field of autonomous vehicles, and Convolutional Neural Network (CNN) is used extensively, and Vision Transformer (ViT) networks are used instead of deep convolutional networks in order to compress the network size and improve the model accuracy. The ViT network is used to replace the deep convolutional network. Since training ViT requires a large dataset to have sufficient accuracy, a variant of ViT, Data-Efficient Image Transformers (DEIT), is used in this paper. In addition, in order to greatly reduce the computing memory and shorten the computing time in practical use, the network is flexibly scaled in size and training speed by both adaptive width and adaptive depth. In this paper, we introduce DEIT, width adaptive techniques and depth adaptive techniques and combine them to be applied to image classification examples. Experiments are conducted on the Cifar100 dataset, and the experiments demonstrate the superiority of the algorithm on image classification scenarios.

Optimizing Small BERTs Trained for German NER

Currently, the most widespread neural network architecture for training language models is the so-called BERT, which led to improvements in various NLP tasks. In general, the larger the number of parameters in a BERT model, the better the results obtained in these NLP tasks. Unfortunately, the memory consumption and the training duration drastically increases with the size of these models. In this article, we investigate various training techniques of smaller BERT models: We combine different methods from other BERT variants, such as ALBERT, RoBERTa, and relative positional encoding. In addition, we propose two new fine-tuning modifications leading to better performance: CSE tagging and a modified form of LCRF. Furthermore, we introduce WWA, which reduces BERT memory usage and leads to a small increase in performance compared to classical Multi-Head-Attention. We evaluate these techniques on five public German NER tasks, of which two are introduced by this article.

Application of the WNN-Based SCG Optimization Algorithm for Predicting Soft Soil Foundation Engineering Settlement

Settlement prediction in soft soil foundation engineering is a newer technique. Predicting soft soil settling has long been one of the most challenging techniques due to difficulties in soft soil engineering. To overcome these challenges, the wavelet neural network (WNN) is mostly used. So, after assessing its estimate performance, two elements, early parameter selection and system training techniques, are chosen to optimize the traditional WNN difficulties of readily convergence to the local infinitesimal point, low speed, and poor approximation performance. The number of hidden layer nodes is determined using a self-adaptive adjustment technique. The wavelet neural network (WNN) is coupled with the scaled conjugate gradient (SCG) to increase the feasibility and accuracy of the soft fundamental engineering settlement prediction model, and a better wavelet network for the soft ground engineering settlement prediction is suggested in this paper. Furthermore, we have proposed the technique of locating the early parameters based on autocorrelation. The settlement of three types of traditional soft foundation engineering, including metro tunnels, highways, and high-rise building foundations, has been predicted using our proposed model. The findings revealed that the model is superior to the backpropagation neural network and the standard WNN for solving problems of approximation performance. As a result, the model is acceptable for soft foundation engineering settlement prediction and has substantial project referential value.