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.

EVM: An Educational Virtual Reality Modeling Tool; Evaluation Study with Freshman Engineering Students

Educational Virtual Modeling (EVM) is a novel VR-based application for sketching and modeling in an immersive environment designed to introduce freshman engineering students to modeling concepts and reinforce their understanding of the spatial connection between an object and its 2D projections. It was built on the Unity 3D game engine and Microsoft’s Mixed Reality Toolkit (MRTK). EVM was designed to support the creation of the typical parts used in exercises in basic engineering graphics courses with a special emphasis on a fast learning curve and a simple way to provide exercises and tutorials to students. To analyze the feasibility of using EVM for this purpose, a user study was conducted with 23 freshmen and sophomore engineering students that used both EVM and Trimble SketchUp to model six parts using an axonometric view as the input. Students had no previous experience in any of the two systems. Each participant went through a brief training session and was allowed to use each tool freely for 20 min. At the end of the modeling exercises with each system, the participants rated its usability by answering the System Usability Scale (SUS) questionnaire. Additionally, they filled out a questionnaire for assessment of the system functionality. The results demonstrated a very high SUS score for EVM (M = 92.93, SD = 6.15), whereas Trimble SketchUp obtained only a mean score of 76.30 (SD = 6.69). The completion time for the modeling tasks with EVM showed its suitability for regular class use, despite the fact that it usually takes longer to complete the exercises in the system than in Trimble SketchUp. There were no statistically significant differences regarding functionality assessment. At the end of the experimental session, participants were asked to express their opinion about the systems and provide suggestions for the improvement of EVM. All participants showed a preference for EVM as a potential tool to perform exercises in the engineering graphics course.

Uncertainty Quantification and Optimization of Deep Learning for Fracture Recognition

Automatic fracture recognition from borehole images or outcrops is applicable for the construction of fractured reservoir models. Deep learning for fracture recognition is subject to uncertainty due to sparse and imbalanced training set, and random initialization. We present a new workflow to optimize a deep learning model under uncertainty using U-Net. We consider both epistemic and aleatoric uncertainty of the model. We propose a U-Net architecture by inserting dropout layer after every "weighting" layer. We vary the dropout probability to investigate its impact on the uncertainty response. We build the training set and assign uniform distribution for each training parameter, such as the number of epochs, batch size, and learning rate. We then perform uncertainty quantification by running the model multiple times for each realization, where we capture the aleatoric response. In this approach, which is based on Monte Carlo Dropout, the variance map and F1-scores are utilized to evaluate the need to craft additional augmentations or stop the process. This work demonstrates the existence of uncertainty within the deep learning caused by sparse and imbalanced training sets. This issue leads to unstable predictions. The overall responses are accommodated in the form of aleatoric uncertainty. Our workflow utilizes the uncertainty response (variance map) as a measure to craft additional augmentations in the training set. High variance in certain features denotes the need to add new augmented images containing the features, either through affine transformation (rotation, translation, and scaling) or utilizing similar images. The augmentation improves the accuracy of the prediction, reduces the variance prediction, and stabilizes the output. Architecture, number of epochs, batch size, and learning rate are optimized under a fixed-uncertain training set. We perform the optimization by searching the global maximum of accuracy after running multiple realizations. Besides the quality of the training set, the learning rate is the heavy-hitter in the optimization process. The selected learning rate controls the diffusion of information in the model. Under the imbalanced condition, fast learning rates cause the model to miss the main features. The other challenge in fracture recognition on a real outcrop is to optimally pick the parental images to generate the initial training set. We suggest picking images from multiple sides of the outcrop, which shows significant variations of the features. This technique is needed to avoid long iteration within the workflow. We introduce a new approach to address the uncertainties associated with the training process and with the physical problem. The proposed approach is general in concept and can be applied to various deep-learning problems in geoscience.

Innate heuristics and fast learning support escape route selection in mice

When faced with imminent danger, animals must rapidly take defensive actions to reach safety. Mice can react to innately threatening stimuli in less than 250 milliseconds [1] and, in simple environments, use spatial memory to quickly escape to shelter [2,3]. Natural habitats, however, often offer multiple routes to safety which animals must rapidly identify and choose from to maximize the chances of survival [4]. This is challenging because while rodents can learn to navigate complex mazes to obtain rewards [5,6], learning the value of different routes through trial-and-error during escape from threat would likely be deadly. Here we have investigated how mice learn to choose between different escape routes to shelter. By using environments with paths to shelter of varying length and geometry we find that mice prefer options that minimize both path distance and path angle relative to the shelter. This choice strategy is already present during the first threat encounter and after only ~10 minutes of exploration in a novel environment, indicating that route selection does not require experience of escaping. Instead, an innate heuristic is used to assign threat survival value to alternative paths after rapidly learning the spatial environment. This route selection process is flexible and allows quick adaptation to arenas with dynamic geometries. Computational modelling of different classes of reinforcement learning agents shows that the observed behavior can be replicated by model-based agents acting in an environment where the shelter location is rewarding during exploration. These results show that mice combine fast spatial learning with innate heuristics to choose escape routes with the highest survival value. They further suggest that integrating priors acquired through evolution with knowledge learned from experience supports adaptation to changing environments while minimizing the need for trial-and-error when the errors are very costly.

PERBANDINGAN METODE SEASONAL ARIMA DAN EXTREME LEARNING MACHINE PADA PERAMALAN JUMLAH WISATAWAN MANCANEGARA KE BALI

Bali Island of the Gods is one of the wealth of very popular tourist destinations and has the highest number of foreign tourists in Indonesia. It is very necessary to do more in-depth learning related to the projections or forecasting of foreign tourist visits to Bali at a certain period of time. Forecasting analysis used is to compare two methods, namely the Seasonal ARIMA method (SARIMA) and Extreme Learning Machine (ELM). The SARIMA method is a statistical method commonly used in forecasting time series data that contains seasonality and has good accuracy. While the ELM method is a new learning method of artificial neural networks that has fast learning speed and good accuracy. The results obtained indicate that the Seasonal ARIMA method is a better method used to predict the number of tourists to Bali in this case, because it has a smaller forecasting MAPE value of 4.97%. While the ELM method has a forecasting MAPE value of 7.62%.

An optimized Parkinson's disorder identification through evolutionary fast learning network

PurposeParkinson's disease (PD) is a well-known complex neurodegenerative disease. Typically, its identification is based on motor disorders, while the computer estimation of its main symptoms with computational machine learning (ML) has a high exposure which is supported by researches conducted. Nevertheless, ML approaches required first to refine their parameters and then to work with the best model generated. This process often requires an expert user to oversee the performance of the algorithm. Therefore, an attention is required towards new approaches for better forecasting accuracy.Design/methodology/approachTo provide an available identification model for Parkinson disease as an auxiliary function for clinicians, the authors suggest a new evolutionary classification model. The core of the prediction model is a fast learning network (FLN) optimized by a genetic algorithm (GA). To get a better subset of features and parameters, a new coding architecture is introduced to improve GA for obtaining an optimal FLN model.FindingsThe proposed model is intensively evaluated through a series of experiments based on Speech and HandPD benchmark datasets. The very popular wrappers induction models such as support vector machine (SVM), K-nearest neighbors (KNN) have been tested in the same condition. The results support that the proposed model can achieve the best performances in terms of accuracy and g-mean.Originality/valueA novel efficient PD detection model is proposed, which is called A-W-FLN. The A-W-FLN utilizes FLN as the base classifier; in order to take its higher generalization ability, and identification capability is also embedded to discover the most suitable feature model in the detection process. Moreover, the proposed method automatically optimizes the FLN's architecture to a smaller number of hidden nodes and solid connecting weights. This helps the network to train on complex PD datasets with non-linear features and yields superior result.

Ways of organizing the educational process in elementary school based on the project-research form of educational activity

This article examines the ways of organizing the educational process in primary school on the basis of the project-research form of educational activity. The relevance of this topic lies in the search and application of the design and research form of educational activity in primary school, where the main task of teachers is to quickly adapt to modern projects and the ongoing changes in updated learning. The XXI century is called the century of high or digital technologies. Creative, active, fast-learning and inquisitive people are in demand in any industry. The modern system of updated education develops children's research and cognitive structure of activity. Therefore, in the educational program, in almost every country, adjustments are made for the development of students' abilities, based on research and cognitive activity, where one of the first steps is primary school.