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The sudden spread of novel coronavirus COVID-19 across the world has been leading to the drastic changes in complete structural, organizational and social aspects of every sector, including the education system. The quick closure of universities and schools for public health safety during COVID-19 pandemic has become a catalyst for searching innovative solutions within a short span of time. In the context of this new and challenging situation, e-learning tools have become the new educational policy and practice for virtual classrooms. This chapter presents an analysis of various e-learning tools for synchronous and asynchronous learning. It also focuses on the various health issues arising due to the excessive exposure of everyone to screens with the growing adoption of online learning tools and technologies.
E-learning has become an important part of our educational life with the development of e-learning systems and platforms and the need for online and remote learning. ICT and computational intelligence techniques are being used to design more intelligent and adaptive systems. However, the art of designing good real-time e-learning systems is difficult as different aspects of learning need to be considered including challenges such as learning rates, involvement, knowledge, qualifications, as well as networking and security issues. The earlier concepts of standalone integrated virtual e-learning systems have been greatly enhanced with emerging technologies such as cloud computing, mobile computing, big data, Internet of Things (IoT), AI and machine learning, and AR/VT technologies. With this book, the editors and authors wish to help researchers, scholars, professionals, lecturers, instructors, developers, and designers understand the fundamental concepts, challenges, methodologies and technologies for the design of performant and reliable intelligent and adaptive real time e-learning systems and platforms. This edited volume covers state of the art topics including user modeling for e-learning systems and cloud, IOT, and mobile-based frameworks. It also considers security challenges and ethical conduct using Blockchain technology.
The proposed training system helps the examined people to generate motor images based on the example maps of the activity of neuronal cell fractions presented to them. The study involved 16 students at the Laboratory of Neuroinformatics and Decision Systems of the Technical University of Opole. The group was divided into two equal subgroups, one of which was acquainted with the operation of the system, while the other – considered as a control – was not. Electroencephalographic signals were recorded when users were imagining the upper limb movement for two subgroups before and after the imagery training in order to verify the introduction of the proposed training system. The area used for data acquisition as part of the monitoring session implemented with the use of the Emotiv EPOC Flex device is a sensorimotor cortex. As it results from the carried-out literature analysis, it was the first attempt to use the 32-channel Emotive EPOC Flex device in the scope of the training system construction in the field of motor imagery.
To improve the robustness and discrimination power of the triangle-area representation, a novel shape matching method based on multi-scale angle representation is proposed in this study. By analysing the configurations of different sample points from each shape contour, shape descriptors are constructed by using space angles at different scale levels. With the proposed shape representation, the multi-scale information of shape contours is efficiently described, and the dynamic programming is further used to determine the correspondence between samples from different shapes and calculate the shape distance in the feature matching step. Moreover, to improve the shape retrieval results based on pairwise shape distances, the dynamic label propagation is introduced as the post-processing step. Unlike previous distance learning methods learning the database manifold implicitly, the authors method retrieves relative objects on the shortest paths from near to far explicitly, and the underlying structure can be effectively captured. The proposed method tested on different shape databases provides the performances superior to many other methods, and it can be applied to visual data processing and understanding of the internet of things.
Student performance prediction plays an important role in improving education quality. Noticing that students' exercise-answering processes exhibit different characteristics according to their different performance levels, this paper aims to mine the performance-related information from students' exercising logs and to explore the possibility of predicting students' performance using such process-characteristic information. A formal model of student-shared exercising processes and its discovery method from students' exercising logs are presented. Several similarity measures between students' individual exercising behavior and student-shared exercising processes are presented. A prediction method of students' performance level considering these similarity measures is explored based on classification algorithms. An experiment on real-life exercise-answering event logs shows the effectiveness of the proposed prediction method.
As the world continuously faces humanitarian disasters, there is an ever-increasing need to find ICT-based solutions to overcome complex problems. In other words, though ICTs have changed the lives of people at the operative level, many ICTs' contributions are yet to be uncovered in humanitarian service. Therefore, this book provides new developments, innovations, and research outcomes; case studies and lessons learnt; and other considerations for the creation and deployment of effective ICTs to provide humanitarian services for the resource-constrained and vulnerable populations in the world in order to improve their lives.
The objective of this chapter is to analyse the profound technologies in our contemporary society, the process of humanization and inclusion as well as life forms based on coexistence, solidarity and the distributed participation of those who teach and learn through technologies.
Humanitarian services seek to promote welfare to save lives, maintain human dignity, alleviate suffering, strengthen preparedness, and provide material and logistical assistance in response to humanitarian crises. They are thus different from development aids that address underlying socioeconomic factors and provides support for the social, economic and political developments of developing nations. Information and Communication Technologies (ICTs) are becoming the backbone technologies for providing quality and efficient services and are playing an increasingly important and sophisticated role in humanitarian-service activities. Many ICT-based solutions exist such as tools to support the work of humanitarian organizations, mobile applications and solutions to provide health services, open source web portals for disaster management systems, and mobile and autonomous devices to provide assistance. This edited book covers new developments, innovations, and research outcomes for the creation and deployment of effective ICT technologies and solutions to support humanitarian services. Coverage includes theories as well as information, mobile and networking applications that foster information exchange and cooperation in the humanitarian and emergency management fields. Aimed at ICT and computer engineers, professionals, and researchers working on practical and lasting ICT solutions to support humanitarian services, this will also be a useful reference for advanced students and lecturers in the field, entrepreneurs and researchers from government, non-government and industry organizations, as well as professionals in NGO organizations, ethics committees, and policymakers.
Telemedicine can be classified into two major categories, namely telemedicine that includes doctors and patients and telemedicine that includes only doctors. The former includes remote consultation with patients, which is becoming increasingly popular worldwide. It also includes remote surgery, where the patient is operated on by a remote surgeon. The latter includes remote consultation by a doctor with a specialist in fields such as radiology and pathology. It also includes remote medical education, where medical students remotely attend classes, and joint case discussions between doctors at multiple locations. This chapter focuses on remote medical education.
Gamification refers to the use of rules and game design techniques to involve and motivate people to achieve their goals. This work proposes an application development architecture that can generate game applications with gamification techniques. As proof of concept, the authors introduce Zeus, a platform for developing ruled-based serious game applications with gamification techniques. Zeus aims at generating gamified applications that can meet the learning goals set by users. These goals will be reflected through both learning and game attributes that users can personally select. To assess the functionality of the authors tool, they conduct a qualitative evaluation of four rule-based serious game applications developed with Zeus to help students learn about arithmetic. They make use of the Fun Toolkit to perform this evaluation in terms of the delivery of both fun and learning experiences. Their findings are encouraging in the context of learning basic arithmetic operations with Zeus game applications.
Every year software development industry requires a higher number of trained software engineers who are not only skilled programmers but also talented software projects managers. To deliver high-quality software projects, engineers require of the application of sound engineering competencies along with discipline. Obtaining those practices usually require years of experience. Companies are not prepared to invest this time on engineers resulting in a high percentage of deficient projects. Here, the authors present a bachelor-level competency-based approach that develops and evaluates such competencies during a challenge-based learning experience. In this way, the rate of successful projects where software engineers are involved will be higher, as they have obtained the appropriate competencies to deliver such projects.
The Defence Wargaming Centre (DWC), located on Dstl's Portsdown West site near Portsmouth, was created to host wargames for all three UK services, responding to the increasing demand for wargaming as a tool both to support decisions and to develop insight into complex issues faced by defence and security. By delivering a wide variety of wargames it represents a significant step-up in capability and signals our intent to keep developing in response to growing MoD and wider government demand for wargaming.
Autonomous Vehicles (AVs) are becoming prevalent in our society, from self-driving cars and autonomous shuttle buses on urban roads to delivery robots on the pavements and within buildings. These emerging applications have generated a huge interest in the concept of Mobility-on-Demand (MoD) and specifically, Autonomous MoD (AMoD). In this work, we highlight the initiatives of the Singapore-MIT Alliance for Research and Technology (SMART) in the area of AMoD. We discuss the fundamental building blocks of AMoD systems, the solutions and algorithms that we have developed and successfully deployed for public trials since late 2014, and the challenges that we have encountered during the process.
Adaptive metacognitive scaffolding is developed to provide learning assistance on an as-needed basis; thus, advances the effectiveness of computer-based learning systems. Metacognitive scaffoldings have been developed for some science subjects; however, not for algorithm-learning. The learning algorithm is different from learning science as it is more oriented to problem-solving; therefore, this study is aimed to describe the modelling, development, and evaluation of the adaptive metacognitive scaffolding which is dedicated for encouraging algorithm-learning. In addition, the authors present a new approach for learner modelling to find students’ metacognitive state. Adaptivity of the scaffolding is based on the learner modelling. To evaluate the effectiveness of the developed system, it is deployed in a real algorithm-learning classroom of 38 students. The class is randomly divided into two groups: experiment and control. Two parameters are measured from both groups, i.e. academic success and academic satisfaction. Non-parametric statistical test, i.e. Mann–Whitney U-test (significance level 0.01) rejects the null hypothesis (U-value = 86.5 and U-critical = 101). This result verifies that the academic success of the experiment group is significantly higher than that of the control group. In addition, an academic satisfaction survey shows that adaptive scaffolding is valid in assisting students while learning with the system.
Existing person re-identification (re-id) models mainly focus on still-image-based module, namely matching person images across non-overlapping camera views. Since video sequence contains much more information than still images and can be easily achieved by tracking algorithms in practical applications, the video re-id has attracted increasing attention in recent years. Distance learning is crucial for a re-id system. However, the computed distances in traditional video-based methods are easily distracted by the randomness of data distribution, especially with small sample size for training. To preferably distinguish different people, a novel regularised hull distance learning video-based person re-id method is proposed. It is advantageous in two aspects: robustness is guaranteed due to expanded video samples by regularised affine hull with limited ones, discriminability is ensured due to penalised hard negative samples more severely. Hence, the discriminability and robustness of the learnt metric are strengthened. Comparisons with the state-of-the-art video-based methods as well as related methods on PRID 2011, iLIDS-VID and MARS datasets demonstrate the superiority of the authors’ method.
Synthetic biology is a relatively young field, although it builds upon disciplines whose roots go back centuries. Recently, its practitioners have tended to move into the field out of interest or by chance, and come from a wide variety of backgrounds. It is also a very fast-moving field; new protocols, laboratory equipment, computational facilities and algorithms are being developed at a rapid pace. Students who start studying synthetic biology at an undergraduate or postgraduate level will, in the course of their careers, work with technologies as yet undreamt of, and will do so mostly in the context of highly interdisciplinary teams. In this study, the authors identify some of the key areas required for the education of new synthetic biologists to equip them with both adequate background and sufficient flexibility to tackle these challenges and therefore to future-proof synthetic biology.
Academic emotions can produce a great impact on the learning effect. Normally, emotions are expressed externally in the students' facial expressions, speech and behaviour. In this paper, the focus is on automatic academic emotion inference based on facial expressions in online learning. Considering the lack of training samples for the inference algorithm, a spontaneous facial expression database is established. It includes the facial expressions of five common academic emotions and consists of two subsets: a video clip database and an image database. A total of 1,274 video clips and 30,184 images from 82 students are included in the database. The samples are labelled by both the participants and external coders. An extensive analysis is carried out on the image database using a convolutional neural network (CNN)-based algorithm to infer self-annotation. Some data augmentation algorithms are applied to improve the algorithm performance. Additionally, an adaptive data augmentation algorithm based on spatial transformer network is introduced, which can remove some confounding factors in the original images. The algorithm can obviously improve the inference performance, which has been proven by comparing some evaluation indicators before and after adoption. Such a database will certainly accelerate the application of affective computing in the educational field.
Numerical cognition is a distinctive component of human intelligence such that the observation of its practice provides a window in high-level brain function. The modelling of numerical abilities in artificial cognitive systems can help to confirm existing child development hypotheses and define new ones by means of computational simulations. Meanwhile, new research will help to discover innovative principles for the design of artificial agents with advanced reasoning capabilities and clarify the underlying algorithms (e.g. deep learning) that can be highly effective but difficult to understand for humans. This study promotes new investigation by providing a common resource for researchers with different backgrounds, including computer science, robotics, neuroscience, psychology, and education, who are interested in pursuing scientific collaboration on mutually stimulating research on this topic. The study emphasises the fundamental role of embodiment in the initial development of numerical cognition in children. This strong relationship with the body motivates the cognitive developmental robotics (CDR) approach for new research that can (among others) help standardise data collection and provide open databases for benchmarking computational models. Furthermore, the authors discuss the potential application of robots in classrooms and argue that the CDR approach can be extended to assist educators and favour mathematical education.
This study aims to develop a robotic system human–machine interaction (HMI) to facilitate surgical training through visual and kinaesthetic feedbacks. This is motivated by the pressing need for effective surgical training and the unaddressed gaps in existing surgical training simulator for minimally invasive procedures. This study establishes the design concept and scope for development to facilitate the required HMI training model. Subsequently, implementation and demonstration of the model is carried out with analytical experiments to assess the feasibility of the proposed concept. The design concept of the robotic system for training is demonstrated through an user experiment. Results suggest viability and observable benefits in the authors’ proposed kinaesthetic HMI guidance for the trainee. Potential impact of this study includes the development of a novel training paradigm that engages trainees through collaborative training facilitated by human trainers and active kinaesthetic simulation. Although motivated by surgical training applications, the concept developed in this study can potentially be extended for general motor skill learning.
With the rapid development of network information technology and the wide application of smart phones, tablet PCs and other mobile terminals, online education plays an increasingly important role in social life. This article focuses on mining useful data from the massive online education data, by using transfer learning, relying on Hadoop, to construct Online education data classification framework (OEDCF), and design an algorithm Tr_MAdaBoost. This algorithm overcomes the traditional classification algorithms in which the required data must be restricted to independent and identically distributed data, since online education using this new algorithm can achieve the correct classification even it has different data distribution. At the same time, with the help of Hadoop's parallel processing architecture, OEDCF can greatly enhance the efficiency of data processing, create favorable conditions for learning analysis, and promote personalized learning and other activities of big data era.