[pt] A fisioterapia visa melhorar a funcionalidade física das pessoas, procurando atenuar as incapacidades causadas por alguma lesão, distúrbio ou doença. Nesse contexto, diversas tecnologias computacionais têm sido desenvolvidas com o intuito de apoiar o processo de reabilitação, como as tecnologias adaptáveis para o usuário final. Essas tecnologias possibilitam ao fisioterapeuta adequar aplicações e criarem atividades com características personalizadas de acordo com as preferências e necessidades de cada paciente. Nesta tese é proposta uma abordagem de baixo custo baseada no aprendizado de máquina interativo (iML - Interactive Machine Learning) que visa auxiliar os fisioterapeutas a criarem atividades personalizadas para seus pacientes de forma fácil e sem a necessidade de codificação de software, a partir de apenas alguns exemplos em vídeo RGB (capturadas por uma câmera de vídeo digital) Para tal, aproveitamos a estimativa de pose baseada em aprendizado profundo para rastrear, em tempo real, as articulações-chave do corpo humano a partir de dados da imagem. Esses dados são processados como séries temporais por meio do algoritmo Dynamic Time Warping em conjunto com com o algoritmo K-Nearest Neighbors para criar um modelo de aprendizado de máquina. Adicionalmente, usamos um algoritmo de detecção de anomalias com o intuito de avaliar automaticamente os movimentos. A arquitetura de nossa abordagem possui dois módulos: um para o fisioterapeuta apresentar exemplos personalizados a partir dos quais o sistema cria um modelo para reconhecer esses movimentos; outro para o paciente executar os movimentos personalizados enquanto o sistema avalia o paciente. Avaliamos a usabilidade de nosso sistema com fisioterapeutas de cinco clínicas de reabilitação. Além disso, especialistas avaliaram clinicamente nosso modelo de aprendizado de máquina. Os resultados indicam que a nossa abordagem contribui para avaliar automaticamente os movimentos dos pacientes sem monitoramento direto do fisioterapeuta, além de reduzir o tempo necessário do especialista para treinar um sistema adaptável. / [en] Physiotherapy aims to improve the physical functionality of people, seeking to mitigate the disabilities caused by any injury, disorder or disease. In this context, several computational technologies have been developed in order to support the rehabilitation process, such as the end-user adaptable technologies. These technologies allow the physiotherapist to adapt applications and create activities with personalized characteristics according to the preferences and needs of each patient. This thesis proposes a low-cost approach based on interactive machine learning (iML) that aims to help physiotherapists to create personalized activities for their patients easily and without the need for software coding, from just a few examples in RGB video (captured by a digital video camera). To this end, we take advantage of pose estimation based on deep learning to track, in real time, the key joints of the human body from image data. This data is processed as time series using the Dynamic Time Warping algorithm in conjunction with the K-Nearest Neighbors algorithm to create a machine learning model. Additionally, we use an anomaly detection algorithm in order to automatically assess movements. The architecture of our approach has two modules: one for the physiotherapist to present personalized examples from which the system creates a model to recognize these movements; another to the patient performs personalized movements while the system evaluates the patient. We assessed the usability of our system with physiotherapists from five rehabilitation clinics. In addition, experts have clinically evaluated our machine learning model. The results indicate that our approach contributes to automatically assessing patients movements without direct monitoring by the physiotherapist, in addition to reducing the specialist s time required to train an adaptable system.

Advancements in creative artificial intelligence (AI) are leading to systems that can actively work together with designers in tasks such as ideation, i.e. the creation, development, and communication of ideas. In human group work, making suggestions and explaining the reasoning behind them as well as comprehending other group member’s explanations aids reflection, trust, alignment of goals and inspiration through diverse perspectives. Despite their ability to inspire through independent suggestions, state-of-the-art creative AI systems do not leverage these advantages of group work due to missing or one-sided explanations. For other use cases, AI systems that explain their reasoning are already gathering wide research interest. However, there is a knowledge gap on the effects of explanations on creativity. Furthermore, it is unknown whether a user can benefit from also explaining their contributions to an AI system. This thesis investigates whether reciprocal explanations, a novel technique which combines explanations from and to an AI system, improve the designers’ and AI’s joint exploration of ideas. I integrated reciprocal explanations into an AI aided tool for mood board design, a common method for ideation. In our implementation, the AI system uses text to explain which features of its suggestions match or complement the current mood board. Occasionally, it asks for user explanations providing several options for answers that it reacts to by aligning its strategy. A study was conducted with 16 professional designers who used the tool to create mood boards followed by presentations and semi-structured interviews. The study emphasized a need for explanations that make the principles of the system transparent and showed that alignment of goals motivated participants to provide explanations to the system. Also, enabling users to explain their contributions to the AI system facilitated reflection on their own reasons. / Framsteg inom kreativ artificiell intelligens (AI) har lett till system som aktivt kan samarbeta med designers under idéutformningsprocessen, dvs vid skapande, utveckling och kommunikation av idéer. I grupparbete är det viktigt att kunna göra förslag och förklara resonemanget bakom dem, samt förstå de andra gruppmedlemmarnas resonemang. Detta ökar reflektionsförmågan och förtroende hos medlemmarna, samt underlättar sammanjämkning av mål och ger inspiration genom att höra olika perspektiv. Trots att system, baserade på kreativ artificiell intelligens, har förmågan att inspirera genom sina oberoende förslag, utnyttjar de allra senaste kreativa AI-systemen inte dessa fördelar för att facilitera grupparbete. Detta är på grund av AI-systemens bristfälliga förmåga att resonera över sina förslag. Resonemangen är ofta ensidiga, eller saknas totalt. AI-system som kan förklara sina resonemang är redan ett stort forskningsintresse inom många användningsområden. Dock finns det brist på kunskap om AI-systemens påverkan på den kreativa processen. Dessutom är det okänt om en användare verkligen kan dra nytta av möjligheten att kunna förklara sina designbeslut till ett AI-system. Denna avhandling undersöker om ömsesidiga förklaringar, en ny teknik som kombinerar förklaringar från och till ett AI system, kan förbättra designerns och AI:s samarbete under utforskningen av idéer. Jag integrerade ömsesidiga förklaringar i ett AI-hjälpmedel som underlättar skapandet av stämningsplank (eng. mood board), som är en vanlig metod för konceptutveckling. I vår implementering använder AI-systemet textbeskrivningar för att förklara vilka delar av dess förslag som matchar eller kompletterar det nuvarande stämningsplanket. Ibland ber den användaren ge förklaringar, så den kan anpassa sin förslagsstrategi efter användarens önskemål. Vi genomförde en studie med 16 professionella designers som använde verktyget för att skapa stämningsplank. Feedback samlades genom presentationer och semistrukturerade intervjuer. Studien betonade behovet av förklaringar och resonemang som gör principerna bakom AI-systemet transparenta för användaren. Höjd sammanjämkning mellan användarens och systemets mål motiverade deltagarna att ge förklaringar till systemet. Genom att göra det möjligt för användare att förklara sina designbeslut för AI-systemet, förbättrades också användarens reflektionsförmåga över sina val.

Not only is Quantum Mechanics (QM) conceptually rich, it is also a theory that physics students have found abstract and technically formidable. Nevertheless, compared to other classical topics of physics, university students’ understanding of QM has received minimal attention in the physics education literature. The principal purpose of this study was to characterize the variation in the ways that undergraduate physics students depict the basic concepts of QM and to extrapolate the results to scaffold possible changes to instructional practices at the university that provided the context for the study. In so doing, an adaptation of a developmental phenomenographic perspective was chosen. Empirically, the study was approached through in-depth interviews with 35 physics students from two Ethiopian governmental universities after they had been exposed to the traditional QM course for one-third of a semester. Interview responses were analyzed using phenomenographic approach where a picture of students’ depictions was established for each quantum concept by expounding the given responses. For each basic quantum concept addressed, the structure of the description categories was separately constructed, and overall, it was found that naive, quasi-classical ontology and/or variants of classical ways of visualization are dominant in students’ responses. For example, it was found that students’ depictions of the photon concept could be described with three distinct categories of description, which are (a) classical intuitive description, (b) mixed model description and (c) quasi-quantum model description. Similarly, the findings revealed that it is possible to establish three qualitatively different categories of description to picture students’ depictions of matter waves, namely, (a) classical and trajectory-based description, (b) an intricate blend of classical and quantum description and (c) incipient quantum model description. Likewise, it was found that students’ depictions of uncertainty principle can be described as: (a) uncertainty as classical ignorance, (b) uncertainty as measurement disturbance and (c) uncertainty as a quasi-quantum principle. With regard to learning QM, the categories of description made clear several issues: most students did not have enough knowledge to depict the basic concepts of QM properly; they were influenced by the perspective of classical physics and their perceptions in making explanations about QM; and they also applied mixed ideas, one based on their classical model and the other from newly introduced QM. These results are also supported by the findings of previous studies in similar domains. Findings from the study were used to guide the design of multiple representations-based instructions and interactive learning tutorials on the conceptual aspects of QM that has been shown to address specific difficulties identified in the study. Theoretical and practical implications of the study, as well as potential future considerations are drawn. / Mathematics, Science and Technology Education / D. Phil. (Mathematics, Science and Technology Education)

Not only is Quantum Mechanics (QM) conceptually rich, it is also a theory that physics students have found abstract and technically formidable. Nevertheless, compared to other classical topics of physics, university students’ understanding of QM has received minimal attention in the physics education literature. The principal purpose of this study was to characterize the variation in the ways that undergraduate physics students depict the basic concepts of QM and to extrapolate the results to scaffold possible changes to instructional practices at the university that provided the context for the study. In so doing, an adaptation of a developmental phenomenographic perspective was chosen. Empirically, the study was approached through in-depth interviews with 35 physics students from two Ethiopian governmental universities after they had been exposed to the traditional QM course for one-third of a semester. Interview responses were analyzed using phenomenographic approach where a picture of students’ depictions was established for each quantum concept by expounding the given responses. For each basic quantum concept addressed, the structure of the description categories was separately constructed, and overall, it was found that naive, quasi-classical ontology and/or variants of classical ways of visualization are dominant in students’ responses. For example, it was found that students’ depictions of the photon concept could be described with three distinct categories of description, which are (a) classical intuitive description, (b) mixed model description and (c) quasi-quantum model description. Similarly, the findings revealed that it is possible to establish three qualitatively different categories of description to picture students’ depictions of matter waves, namely, (a) classical and trajectory-based description, (b) an intricate blend of classical and quantum description and (c) incipient quantum model description. Likewise, it was found that students’ depictions of uncertainty principle can be described as: (a) uncertainty as classical ignorance, (b) uncertainty as measurement disturbance and (c) uncertainty as a quasi-quantum principle. With regard to learning QM, the categories of description made clear several issues: most students did not have enough knowledge to depict the basic concepts of QM properly; they were influenced by the perspective of classical physics and their perceptions in making explanations about QM; and they also applied mixed ideas, one based on their classical model and the other from newly introduced QM. These results are also supported by the findings of previous studies in similar domains. Findings from the study were used to guide the design of multiple representations-based instructions and interactive learning tutorials on the conceptual aspects of QM that has been shown to address specific difficulties identified in the study. Theoretical and practical implications of the study, as well as potential future considerations are drawn. / Mathematics, Science and Technology Education / D. Phil. (Mathematics, Science and Technology Education)

The thesis deals with the problematics of interactive books and their influence on the education of preschool children, which is in the Czech Republic still one of the unexplored areas. The aim of the thesis is to compare the paper books with the interactive books in terms of their impact on the child's learning and to see if interactivity influences the better memorization of information than interaction with the kindergarten teacher and also if the children are able to work with the interactive book. There are two hypotheses H1: A child of pre-school age working with an interactive book can remember less information than a child listening to a narrative from a classical book, and H2: A pre-school child who working with an interactive book will remember better the visual rendering of the content. The examined group was composed of preschool children from two differently different kindergartens. For the research, the method of participated target group observation was used when working with interactive books, supplemented by interviews with nursery teachers who participated in the research. The development of the pre-school child's cognitive functions and the development of its interaction with the technologies is briefly described in the theoretical part one. The technologies and their role in the...

В настоящее время силлогистика, благодаря ее связи с естественным языком, занимает значимую позицию в широком спектре дисциплин: от когнитивных наук до исследований в области искусственного интеллекта. Ряд особенностей восприятия и обработки человеком силлогистических рассуждений часто приводит к ошибкам при решении данного рода задач. Применение интерактивной визуализации символьных контекстов с применением компьютерных моделей снижает вероятность возникновения подобных проблем. Кроме того, повышению эффективности на этапе обучения способствует введение игровых элементов. В силлогистике самыми распространенными вариантами графических представлений являются круговые диаграммы Эйлера и Венна. Первый подход, согласно ряду эмпирических результатов, оказывает более положительный эффект на результаты решения силлогистических задач. В качестве иллюстрации идеи визуальной поддержки освоения базовых принципов силлогистического вывода предложен проект программного решения, предоставляющего возможности интерактивного построения силлогизмов с использованием диаграмм Эйлера в качестве графической репрезентации. / Today syllogistic, due to its connection with natural language, finds broad application in a wide range of research fields: from cognitive sciences to AI. Some specific features of human perception and processing of syllogistic reasoning makes such kind of tasks prone to error. To diminish the impact of these cognitive biases visual interactive computer models are used as a representation of symbolic contexts. Moreover, to increase the effectiveness of learning various game elements could be applied. Euler and Venn diagrams are the most common methods of the syllogistic reasoning visual representation. There are some empirical proofs that Euler diagrams have better effect on the results of the syllogistic tasks solving. To illustrate the idea of a visual support system for syllogistic learning, we have proposed a project of software application that represents the environment for interactive syllogism construction, which relies on Euler diagrams as a visual representation.