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

Understanding Game-based Approaches for Improving Sustainable Water Governance : The Potential of Serious Games to Solve Water Problems

The sustainable governance of water resources relies on processes of multi-stakeholder collaborations and interactions that facilitate knowledge co-creation and social learning. Governance systems are often fragmented, forming a barrier to adequately addressing the myriad of challenges affecting water resources, including climate change, increased urbanized populations, and pollution. Transitions towards sustainable water governance will likely require innovative learning partnerships between public, private, and civil society stakeholders. It is essential that such partnerships involve vertical and horizontal communication of ideas and knowledge, and an enabling and democratic environment characterized by informal and open discourse. There is increasing interest in learning-based transitions. Thus far, much scholarly thinking and, to a lesser degree, empirical research has gone into understanding the potential impact of social learning on multi-stakeholder settings. The question of whether such learning can be supported by forms of serious gaming has hardly been asked. This Special Issue critically explores the potential of serious games to support multi-stakeholder social learning and collaborations in the context of water governance. Serious games may involve simulations of real-world events and processes and are challenge players to solve contemporary societal problems; they, therefore, have a purpose beyond entertainment. They offer a largely untapped potential to support social learning and collaboration by facilitating access to and the exchange of knowledge and information, enhancing stakeholder interactions, empowering a wider audience to participate in decision making, and providing opportunities to test and analyze the outcomes of policies and management solutions. Little is known about how game-based approaches can be used in the context of collaborative water governance to maximize their potential for social learning. While several studies have reported examples of serious games, there is comparably less research about how to assess the impacts of serious games on social learning and transformative change

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

Low-resource learning in complex games

This project is concerned with learning to take decisions in complex domains, in games in particular. Previous work assumes that massive data resources are available for training, but aside from a few very popular games, this is generally not the case, and the state of the art in such circumstances is to rely extensively on hand-crafted heuristics. On the other hand, human players are able to quickly learn from only a handful of examples, exploiting specific characteristics of the learning problem to accelerate their learning process. Designing algorithms that function in a similar way is an open area of research and has many applications in today’s complex decision problems. One solution presented in this work is design learning algorithms that exploit the inherent structure of the game. Specifically, we take into account how the action space can be clustered into sets called types and exploit this characteristic to improve planning at decision time. Action types can also be leveraged to extract high-level strategies from a sparse corpus of human play, and this generates more realistic trajectories during planning, further improving performance. Another approach that proved successful is using an accurate model of the environment to reduce the complexity of the learning problem. Similar to how human players have an internal model of the world that allows them to focus on the relevant parts of the problem, we decouple learning to win from learning the rules of the game, thereby making supervised learning more data efficient. Finally, in order to handle partial observability that is usually encountered in complex games, we propose an extension to Monte Carlo Tree Search that plans in the Belief Markov Decision Process. We found that this algorithm doesn’t outperform the state of the art models on our chosen domain. Our error analysis indicates that the method struggles to handle the high uncertainty of the conditions required for the game to end. Furthermore, our relaxed belief model can cause rollouts in the belief space to be inaccurate, especially in complex games. We assess the proposed methods in an agent playing the highly complex board game Settlers of Catan. Building on previous research, our strongest agent combines planning at decision time with prior knowledge extracted from an available corpus of general human play; but unlike this prior work, our human corpus consists of only 60 games, as opposed to many thousands. Our agent defeats the current state of the art agent by a large margin, showing that the proposed modifications aid in exploiting general human play in highly complex games

Sound based social networks

The sound environment is an eco of the activity and character of each place, often carrying additional information to that made available to the eyes (both new and redundant). It is, therefore, an intangible and volatile acoustic fingerprint of the place, or simply an acoustic snapshot of a single event. Such rich resource, full of meaning and subtleness, Schaeffer called Soundscape. The exploratory research project presented here addresses the Soundscape in the context of Mobile Online Social Networking, aiming at determining the extent of its applicability regarding the establishment and/or strengthening of new and existing social links. Such research goal demanded an interdisciplinary approach, which we have anchored in three main stems: Soundscapes, Mobile Sound and Social Networking. These three areas pave the scientific ground for this study and are introduced during the first part of the thesis. An extensive survey of the state-of-the-arte projects related with this research is also presented, gathering examples from different but adjacent areas such as mobile sensing, wearable computing, sonification, social media and contextaware computing. This survey validates that our approach is scientifically opportune and unique, at the same time. Furthermore, in order to assess the role of Soundscapes in the context of Social Networking, an experimental procedure has been implemented based on an Online Social Networking mobile application, enriched with environmental sensing mechanisms, able to capture and analyze the surrounding Soundscape and users' movements. Two main goals guided this prototypal research tool: collecting data regarding users' activity (both sonic and kinetic) and providing users with a real experience using a Sound-Based Social Network, in order to collect informed opinions about this unique type of Social Networking. The application – Hurly-Burly – senses the surrounding Soundscape and analyzes it using machine audition techniques, classifying it according to four categories: speech, music, environmental sounds and silence. Additionally, it determines the sound pressure level of the sensed Soundscape in dB(A)eq. This information is then broadcasted to the entire online social network of the user, allowing each element to visualize and audition a representation of the collected data. An individual record for each user is kept available in a webserver and can be accessed through an online application, displaying the continuous acoustic profile of each user along a timeline graph. The experimental procedure included three different test groups, forming each one a social network with a cluster coefficient equal to one. After the implementation and result analysis stages we concluded that Soundscapes can have a role in the Online Social Networking paradigm, specially when concerning mobile applications. Has been proven that current offthe- shelf mobile technology is a promising opportunity for accomplishing this kind of tasks (such as continuous monitoring, life logging and environment sensing) but battery limitations and multitasking's constraints are still the bottleneck, hindering the massification of successful applications. Additionally, online privacy is something that users are not enthusiastic in letting go: using captured sound instead of representations of the sound would abstain users from utilizing such applications. We also demonstrated that users who are more aware of the Soundscape concept are also more inclined to assume it as playing an important role in OSN. This means that more pedagogy towards the acoustic phenomenon is needed and this type of research gives a step further in that direction.O ambiente sonoro de um lugar é um eco da sua atividade e carácter, transportando, na maior parte da vezes, informação adicional àquela que é proporcionada à visão (quer seja redundante ou complementar). É, portanto, uma impressão digital acústica - tangível e volátil - do lugar a que pertence, ou simplesmente uma fotografia acústica de um evento pontual. A este opulento recurso, carregado de significados e subtilezas, Schafer chamou de Paisagem-Sonora. O projeto de investigação de carácter exploratório que aqui apresentamos visa o estudo da Paisagem-Sonora no contexto das Redes Sociais Móveis Em-Linha, procurando entender os moldes e limites da sua aplicação, tendo em vista o estabelecimento e/ou reforço de novos ou existente laços sociais, respectivamente. Para satisfazer este objectivo foi necessária uma abordagem multidisciplinar, ancorada em três pilares principais: a Paisagem-Sonora, o Som Móvel e as Redes Sociais. Estas três áreas determinaram a moldura científica de referência em que se enquadrou esta investigação, sendo explanadas na primeira parte da tese. Um extenso levantamento do estado-da-arte referente a projetos relacionados com este estudo é também apresentado, compilando exemplos de áreas distintas mas adjacentes, tais como: Computação Sensorial Móvel, Computação Vestível, Sonificação, Média Social e Computação Contexto-Dependente. Este levantamento veio confirmar quer a originalidade quer a pertinência científica do projeto apresentado. Posteriormente, a fim de avaliar o papel da Paisagem-Sonora no contexto das Redes Sociais, foi posto em prática um procedimento experimental baseado numa Rede Social Sonora Em-Linha, desenvolvida de raiz para dispositivos móveis e acrescida de mecanismos sensoriais para estímulos ambientais, capazes de analisar a Paisagem-Sonora envolvente e os movimentos do utilizador. Dois objectivos principais guiaram a produção desta ferramenta de investigação: recolher dados relativos à atividade cinética e sonora dos utilizadores e proporcionar a estes uma experiência real de utilização uma Rede Social Sonora, de modo a recolher opiniões fundamentadas sobre esta tipologia específica de socialização. A aplicação – Hurly-Burly – analisa a Paisagem-Sonora através de algoritmos de Audição Computacional, classificando- a de acordo com quatro categorias: diálogo (voz), música, sons ambientais (“ruídos”) e silêncio. Adicionalmente, determina o seu nível de pressão sonora em dB(A)eq. Esta informação é então distribuída pela rede social dos utilizadores, permitindo a cada elemento visualizar e ouvir uma representação do som analisado. É mantido num servidor Web um registo individual da informação sonora e cinética captada, o qual pode ser acedido através de uma aplicação Web que mostra o perfil sonoro de cada utilizador ao longo do tempo, numa visualização ao estilo linha-temporal. O procedimento experimental incluiu três grupos de teste distintos, formando cada um a sua própria rede social com coeficiente de aglomeração igual a um. Após a implementação da experiência e análise de resultados, concluímos que a Paisagem- Sonora pode desempenhar um papel no paradigma das Redes Sociais Em- Linha, em particular no que diz respeito à sua presença nos dispositivos móveis. Ficou provado que os dispositivos móveis comerciais da atualidade apresentam-se com uma oportunidade promissora para desempenhar este tipo de tarefas (tais como: monitorização contínua, registo quotidiano e análise sensorial ambiental), mas as limitações relacionadas com a autonomia energética e funcionamento em multitarefa representam ainda um constrangimento que impede a sua massificação. Além disso, a privacidade no mundo virtual é algo que os utilizadores atuais não estão dispostos a abdicar: partilhar continuamente a Paisagem-Sonora real em detrimento de uma representação de alto nível é algo que refrearia os utilizadores de usar a aplicação. Também demonstrámos que os utilizadores que mais conhecedores do fenómeno da Paisagem-Sonora são também os que consideram esta como importante no contexto das Redes Sociais Em-Linha. Isso significa que uma atitude pedagógica em relação ao fenómeno sonoro é essencial para obter dele o maior ganho possível. Esta investigação propõe-se a dar um passo em frente nessa direção

Developing Learning System in Pesantren The Role of ICT

According to Krashen's affective filter hypothesis, students who are highly motivated have a strong sense of self, enter a learning context with a low level of anxiety, and are much more likely to become successful language acquirers than those who do not. Affective factors, such as motivation, attitude, and anxiety, have a direct impact on foreign language acquisition. Horwitz et al. (1986) mentioned that many language learners feel anxious when learning foreign languages. Thus, this study recruits 100 college students to fill out the Foreign Language Classroom Anxiety Scale (FLCAS) to investigate language learning anxiety. Then, this study designs and develops an affective tutoring system (ATS) to conduct an empirical study. The study aims to improve students’ learning interest by recognizing their emotional states during their learning processes and provide adequate feedback. It is expected to enhance learners' motivation and interest via affective instructional design and then improve their learning performance

Psychological Engagement in Choice and Judgment Under Risk and Uncertainty

Theories of choice and judgment assume that agents behave rationally, choose the higher expected value option, and evaluate the choice consistently (Expected Utility Theory, Von Neumann, & Morgenstern, 1947). However, researchers in decision-making showed that human behaviour is different in choice and judgement tasks (Slovic & Lichtenstein, 1968; 1971; 1973). In this research, we propose that psychological engagement and control deprivation predict behavioural inconsistencies and utilitarian performance with judgment and choice. Moreover, we explore the influences of engagement and control deprivation on agent’s behaviours, while manipulating content of utility (Kusev et al., 2011, Hertwig & Gigerenzer 1999, Tversky & Khaneman, 1996) and decision reward (Kusev et al, 2013, Shafir et al., 2002)