Parental Perspectives on the Excellence of Computer Learning Media in Early Childhood Education

The introduction of basic computer media for early childhood is very important because it is one of the skills that children need in this century. Need to support parents and teachers in developing the implementation of the use of computer technology at home or at school. This study aims to determine and understand the state of learning conducted based on technology. This research uses a qualitative approach with a case study model. This study involved 15 children and 5 parents. Data obtained through interviews (children and parents) and questionnaires for parents. The results showed that children who were introduced to and taught basic computers earlier became more skilled in learning activities. Suggestions for further research to be more in-depth both qualitatively and quantitatively explore the use of the latest technology to prepare future generations who have 21st century skills. 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Holistic Approach for Authoring Immersive and Smart Environments for the Integration in Engineering Education

Die vierte industrielle Revolution und der rasante technologische Fortschritt stellen die etablierten Bildungsstrukturen und traditionellen Bildungspraktiken in Frage. Besonders in der Ingenieurausbildung erfordert das lebenslange Lernen, dass man sein Wissen und seine Fähigkeiten ständig verbessern muss, um auf dem Arbeitsmarkt wettbewerbsfähig zu sein. Es besteht die Notwendigkeit eines Paradigmenwechsels in der Bildung und Ausbildung hin zu neuen Technologien wie virtueller Realität und künstlicher Intelligenz. Die Einbeziehung dieser Technologien in ein Bildungsprogramm ist jedoch nicht so einfach wie die Investition in neue Geräte oder Software. Es müssen neue Bildungsprogramme geschaffen oder alte von Grund auf umgestaltet werden. Dabei handelt es sich um komplexe und umfangreiche Prozesse, die Entscheidungsfindung, Design und Entwicklung umfassen. Diese sind mit erheblichen Herausforderungen verbunden, die die Überwindung vieler Hindernisse erfordert. Diese Arbeit stellt eine Methodologie vor, die sich mit den Herausforderungen der Nutzung von Virtueller Realität und Künstlicher Intelligenz als Schlüsseltechnologien in der Ingenieurausbildung befasst. Die Methodologie hat zum Ziel, die Hauptakteure anzuleiten, um den Lernprozess zu verbessern, sowie neuartige und effiziente Lernerfahrungen zu ermöglichen. Da jedes Bildungsprogramm einzigartig ist, folgt die Methodik einem ganzheitlichen Ansatz, um die Erstellung maßgeschneiderter Kurse oder Ausbildungen zu unterstützen. Zu diesem Zweck werden die Wechselwirkung zwischen verschiedenen Aspekten berücksichtigt. Diese werden in den drei Ebenen - Bildung, Technologie und Management zusammengefasst. Die Methodik betont den Einfluss der Technologien auf die Unterrichtsgestaltung und die Managementprozesse. Sie liefert Methoden zur Entscheidungsfindung auf der Grundlage einer umfassenden pädagogischen, technologischen und wirtschaftlichen Analyse. Darüber hinaus unterstützt sie den Prozess der didaktischen Gestaltung durch eine umfassende Kategorisierung der Vor- und Nachteile immersiver Lernumgebungen und zeigt auf, welche ihrer Eigenschaften den Lernprozess verbessern können. Ein besonderer Schwerpunkt liegt auf der systematischen Gestaltung immersiver Systeme und der effizienten Erstellung immersiver Anwendungen unter Verwendung von Methoden aus dem Bereich der künstlichen Intelligenz. Es werden vier Anwendungsfälle mit verschiedenen Ausbildungsprogrammen vorgestellt, um die Methodik zu validieren. Jedes Bildungsprogramm hat seine eigenen Ziele und in Kombination decken sie die Validierung aller Ebenen der Methodik ab. Die Methodik wurde iterativ mit jedem Validierungsprojekt weiterentwickelt und verbessert. Die Ergebnisse zeigen, dass die Methodik zuverlässig und auf viele Szenarien sowie auf die meisten Bildungsstufen und Bereiche übertragbar ist. Durch die Anwendung der in dieser Arbeit vorgestellten Methoden können Interessengruppen immersiven Technologien effektiv und effizient in ihre Unterrichtspraxis integrieren. Darüber hinaus können sie auf der Grundlage der vorgeschlagenen Ansätze Aufwand, Zeit und Kosten für die Planung, Entwicklung und Wartung der immersiven Systeme sparen. Die Technologie verlagert die Rolle des Lehrenden in eine Moderatorrolle. Außerdem bekommen die Lehrkräfte die Möglichkeit die Lernenden individuell zu unterstützen und sich auf deren kognitive Fähigkeiten höherer Ordnung zu konzentrieren. Als Hauptergebnis erhalten die Lernenden eine angemessene, qualitativ hochwertige und zeitgemäße Ausbildung, die sie qualifizierter, erfolgreicher und zufriedener macht

AI in Learning: Designing the Future

AI (Artificial Intelligence) is predicted to radically change teaching and learning in both schools and industry causing radical disruption of work. AI can support well-being initiatives and lifelong learning but educational institutions and companies need to take the changing technology into account. Moving towards AI supported by digital tools requires a dramatic shift in the concept of learning, expertise and the businesses built off of it. Based on the latest research on AI and how it is changing learning and education, this book will focus on the enormous opportunities to expand educational settings with AI for learning in and beyond the traditional classroom. This open access book also introduces ethical challenges related to learning and education, while connecting human learning and machine learning. This book will be of use to a variety of readers, including researchers, AI users, companies and policy makers

Psychophysiological analysis of a pedagogical agent and robotic peer for individuals with autism spectrum disorders.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by ongoing problems in social interaction and communication, and engagement in repetitive behaviors. According to Centers for Disease Control and Prevention, an estimated 1 in 68 children in the United States has ASD. Mounting evidence shows that many of these individuals display an interest in social interaction with computers and robots and, in general, feel comfortable spending time in such environments. It is known that the subtlety and unpredictability of people’s social behavior are intimidating and confusing for many individuals with ASD. Computerized learning environments and robots, however, prepare a predictable, dependable, and less complicated environment, where the interaction complexity can be adjusted so as to account for these individuals’ needs. The first phase of this dissertation presents an artificial-intelligence-based tutoring system which uses an interactive computer character as a pedagogical agent (PA) that simulates a human tutor teaching sight word reading to individuals with ASD. This phase examines the efficacy of an instructional package comprised of an autonomous pedagogical agent, automatic speech recognition, and an evidence-based instructional procedure referred to as constant time delay (CTD). A concurrent multiple-baseline across-participants design is used to evaluate the efficacy of intervention. Additionally, post-treatment probes are conducted to assess maintenance and generalization. The results suggest that all three participants acquired and maintained new sight words and demonstrated generalized responding. The second phase of this dissertation describes the augmentation of the tutoring system developed in the first phase with an autonomous humanoid robot which serves the instructional role of a peer for the student. In this tutoring paradigm, the robot adopts a peer metaphor, where its function is to act as a peer. With the introduction of the robotic peer (RP), the traditional dyadic interaction in tutoring systems is augmented to a novel triadic interaction in order to enhance the social richness of the tutoring system, and to facilitate learning through peer observation. This phase evaluates the feasibility and effects of using PA-delivered sight word instruction, based on a CTD procedure, within a small-group arrangement including a student with ASD and the robotic peer. A multiple-probe design across word sets, replicated across three participants, is used to evaluate the efficacy of intervention. The findings illustrate that all three participants acquired, maintained, and generalized all the words targeted for instruction. Furthermore, they learned a high percentage (94.44% on average) of the non-target words exclusively instructed to the RP. The data show that not only did the participants learn nontargeted words by observing the instruction to the RP but they also acquired their target words more efficiently and with less errors by the addition of an observational component to the direct instruction. The third and fourth phases of this dissertation focus on physiology-based modeling of the participants’ affective experiences during naturalistic interaction with the developed tutoring system. While computers and robots have begun to co-exist with humans and cooperatively share various tasks; they are still deficient in interpreting and responding to humans as emotional beings. Wearable biosensors that can be used for computerized emotion recognition offer great potential for addressing this issue. The third phase presents a Bluetooth-enabled eyewear – EmotiGO – for unobtrusive acquisition of a set of physiological signals, i.e., skin conductivity, photoplethysmography, and skin temperature, which can be used as autonomic readouts of emotions. EmotiGO is unobtrusive and sufficiently lightweight to be worn comfortably without interfering with the users’ usual activities. This phase presents the architecture of the device and results from testing that verify its effectiveness against an FDA-approved system for physiological measurement. The fourth and final phase attempts to model the students’ engagement levels using their physiological signals collected with EmotiGO during naturalistic interaction with the tutoring system developed in the second phase. Several physiological indices are extracted from each of the signals. The students’ engagement levels during the interaction with the tutoring system are rated by two trained coders using the video recordings of the instructional sessions. Supervised pattern recognition algorithms are subsequently used to map the physiological indices to the engagement scores. The results indicate that the trained models are successful at classifying participants’ engagement levels with the mean classification accuracy of 86.50%. These models are an important step toward an intelligent tutoring system that can dynamically adapt its pedagogical strategies to the affective needs of learners with ASD

AI in Learning: Designing the Future

AI (Artificial Intelligence) is predicted to radically change teaching and learning in both schools and industry causing radical disruption of work. AI can support well-being initiatives and lifelong learning but educational institutions and companies need to take the changing technology into account. Moving towards AI supported by digital tools requires a dramatic shift in the concept of learning, expertise and the businesses built off of it. Based on the latest research on AI and how it is changing learning and education, this book will focus on the enormous opportunities to expand educational settings with AI for learning in and beyond the traditional classroom. This open access book also introduces ethical challenges related to learning and education, while connecting human learning and machine learning. This book will be of use to a variety of readers, including researchers, AI users, companies and policy makers

Rethinking Pedagogy: Exploring the Potential of Digital Technology in Achieving Quality Education

(First Paragraph) The Mahatma Gandhi Institute of Education for Peace and Sustainable Development (MGIEP) is UNESCO’s Category 1 education Institute in the Asia-Pacific region devoted to education for peace and sustainable development, as enshrined in SDG Target 4.7. UNESCO MGIEP promotes the use of digital learning platforms where teachers and students can co-create and share a highly interactive learning experience. With the rise of the internet, there has been a proliferation of online content and digital resources intended to support teaching and learning, albeit widely varying in quality. Digital education media and resources, if carefully designed and implemented, have a significant potential to be mobilized on a massive scale to support transformative learning for building sustainable, flourishing societies

A tablet computer-assisted motor and language skills training programme to promote communication development in children with autism: development and pilot study

Autism is a heterogenous condition, encompassing many different subtypes and presentations. Of those people with autism who lack communicative speech, some are more skilled at receptive language than their expressive difficulty might suggest. This disparity between what can be spoken and what can be understood correlates with motor and especially oral motor abilities, and thus may be a consequence of limits to oral motor skill. Point OutWords, tablet-based software targeted for this subgroup, builds on autistic perceptual and cognitive strengths to develop manual motor and oral motor skills prerequisite to communication by pointing or speaking. Although typical implementations of user-centred design rely on communicative speech, Point OutWords users were involved as co-creators both directly via their own nonverbal behavioural choices and indirectly via their communication therapists’ reports; resulting features include vectorised, high-contrast graphics, exogenous cues to help capture and maintain attention, customisable reinforcement prompts, and accommodation of open-loop visuomotor control

An Intelligent Robot and Augmented Reality Instruction System

Human-Centered Robotics (HCR) is a research area that focuses on how robots can empower people to live safer, simpler, and more independent lives. In this dissertation, I present a combination of two technologies to deliver human-centric solutions to an important population. The first nascent area that I investigate is the creation of an Intelligent Robot Instructor (IRI) as a learning and instruction tool for human pupils. The second technology is the use of augmented reality (AR) to create an Augmented Reality Instruction (ARI) system to provide instruction via a wearable interface. To function in an intelligent and context-aware manner, both systems require the ability to reason about their perception of the environment and make appropriate decisions. In this work, I construct a novel formulation of several education methodologies, particularly those known as response prompting, as part of a cognitive framework to create a system for intelligent instruction, and compare these methodologies in the context of intelligent decision making using both technologies. The IRI system is demonstrated through experiments with a humanoid robot that uses object recognition and localization for perception and interacts with students through speech, gestures, and object interaction. The ARI system uses augmented reality, computer vision, and machine learning methods to create an intelligent, contextually aware instructional system. By using AR to teach prerequisite skills that lend themselves well to visual, augmented reality instruction prior to a robot instructor teaching skills that lend themselves to embodied interaction, I am able to demonstrate the potential of each system independently as well as in combination to facilitate students\u27 learning. I identify people with intellectual and developmental disabilities (I/DD) as a particularly significant use case and show that IRI and ARI systems can help fulfill the compelling need to develop tools and strategies for people with I/DD. I present results that demonstrate both systems can be used independently by students with I/DD to quickly and easily acquire the skills required for performance of relevant vocational tasks. This is the first successful real-world application of response-prompting for decision making in a robotic and augmented reality intelligent instruction system