京都大学 / 新制・課程博士 / 博士(理学) / 甲第24420号 / 理博第4919号 / 新制||理||1703(附属図書館) / 京都大学大学院理学研究科物理学・宇宙物理学専攻 / (主査)教授 前田 啓一, 講師 LEE Shiu Hang, 教授 太田 耕司 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DFAM

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京都大学 / 新制・課程博士 / 博士(理学) / 甲第23705号 / 理博第4795号 / 新制||理||1686(附属図書館) / 京都大学大学院理学研究科物理学・宇宙物理学専攻 / (主査)教授 嶺重 慎, 准教授 前田 啓一, 准教授 野上 大作 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DFAM

Recently, distance education has become popular and has gotten much attention. Information and Communication Technology advances fostered distance learning creation and enabled individuals to participate in the education process via various web-based platforms and study entirely online. Thus, the notion of e-learning and distance learning emerged. Massive Open Online Courses (MOOCs) appeared as part of e-learning in 2008 and attracted great interest, especially during the COVID-19 pandemic. It was anticipated that this kind of study also could be integrated into higher education and revolutionize the learning approach. However, several issues related to MOOCs limit their full potential. One of the most significant problems is substantial rate of learners’ attrition. It was discovered that only 5-10 percent of MOOC learners complete a course. This thesis aims to examine what influences individuals’ decision to leave MOOCs and how learners perceive various course components to get ideas regarding how MOOCs could be enhanced. To do this, the mixed-method study was undertaken where quantitative data analysis of learners’ reviews from discussion forums and qualitative interviews were adopted. It allowed to get two perspectives and broaden the thesis out- come. For the current research, data was collected from six courses in three different subjects-«Health», «Art and Humanity/Design» and «Computer/Data Science». In the first part of the work, sentiment analysis and topic modeling using Python packages were carried out, and then the results were used to construct an interview questionnaire. Lexicon-based sentiment analysis technique and LDA topic modeling algorithm were utilized and proved to be robust methods to extract texts’ polarity and peoples’ opinions. In the qualitative part, 19 topics of discussion were identified, which were consolidated into eight topics with higher abstraction – materials, instructor, content, time, assignment, feedback, program(course), and algorithms. Then during the qualitative part, participants expressed their opinions regarding these topics, and analysis codes were predefined, and new topics did not emerge. The results showed learners’ perceptions related to presented topics and how these aspects influence experience with MOOCs. The outcome also showed a slight disparity between different subject learners, in both qualitative and quantitative studies identified topics of discussion were not exactly the same, showing that learners from different educational domains tend to discuss different themes.

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Real-time rendering of massive 3D models has been recognized as a challenging task due to the limited computational power and memory available in a workstation. Most existing acceleration techniques, such as mesh simplification algorithms with hierarchical data structures, suffer from the nature of sequential executions. As data complexity increases due to the fundamental advances in modeling and simulation technologies, 3D models become complex and require gigabytes in storage. Consequently, visualizing such large datasets becomes a computationally intensive process where sequential solutions are unable to satisfy the demands of real-time rendering. Recently, the Graphics Processing Unit (GPU) has been praised as a massively parallel architecture not only for its significant improvements in performance but also because of its programmability for general-purpose computation. Today's GPUs allow researchers to solve problems by delivering fine-grained parallel implementations. In this dissertation, I concentrate on the design of parallel algorithms for real-time rendering of massive 3D polygonal models towards modern GPU architectures. As a result, the delivered rendering system supports high-performance visualization of 3D models composed of hundreds of millions of polygons on a single commodity workstation. / Ph. D.

Yes / The need to achieve high data rates in modern telecommunication systems, such as 5G standard, motivates the study and development of large antenna and multiple-input multiple-output (MIMO) systems. This study introduces a large antenna-order design of MIMO quasi-orthogonal space-time block code (QO-STBC) system that achieves better signal-to-noise ratio (SNR) and bit-error ratio (BER) performances than the conventional QO-STBCs with the potential for massive MIMO (mMIMO) configurations. Although some earlier MIMO standards were built on orthogonal space-time block codes (O-STBCs), which are limited to two transmit antennas and data rates, the need for higher data rates motivates the exploration of higher antenna configurations using different QO-STBC schemes. The standard QO-STBC offers a higher number of antennas than the O-STBC with the full spatial rate. Unfortunately, also, the standard QO-STBCs are not able to achieve full diversity due to self-interference within their detection matrices; this diminishes the BER performance of the QO-STBC scheme. The detection also involves nonlinear processing, which further complicates the system. To solve these problems, we propose a linear processing design technique (which eliminates the system complexity) for constructing interference-free QO-STBCs and that also achieves full diversity using Hadamard modal matrices with the potential for mMIMO design. Since the modal matrices that orthogonalize QO-STBC are not sparse, our proposal also supports O-STBCs with a well-behaved peak-to-average power ratio (PAPR) and better BER. The results of the proposed QO-STBC outperform other full diversity techniques including Givens-rotation and the eigenvalue decomposition (EVD) techniques by 15 dB for both MIMO and multiple-input single-output (MISO) antenna configurations at 10−3 BER. The proposed interference-free QO-STBC is also implemented for 16×NR and 32×NR MIMO systems, where NR≤2. We demonstrate 8 x 16 and 32 transmit antenna-enabled MIMO systems with the potential for mMIMO design applications with attractive BER and PAPR performance characteristics.

The vehicular community is moving towards a whole new paradigm with the advancement of new technology. Vehicular communication not only supports safety services but also provides non-safety services like navigation support, toll collection, web browsing, media streaming, etc. The existing communication frameworks like Dedicated Short Range Communication (DSRC) and Cellular V2X (C-V2X) might not meet the required capacity in the coming days. So, the vehicular community needs to adopt new technologies and upgrade the existing communication frameworks so that it can fulfill the desired expectations. Therefore, an increment in reliability and data rate is required. Multiple Input Multiple Output (MIMO), 5G New Radio, Low Density Parity Check (LDPC) Code, and Massive MIMO signal detection and equalization algorithms are the latest addition to the 5G wireless communication domain. These technologies have the potential to make the existing V2X communication framework more robust. As a result, more reliability and throughput can be achieved. This work demonstrates these technologies' compatibility and positive impact on existing V2X communication standard.

Esta pesquisa analisa fatores que sustentam o uso das metodologias ativas para o aumento do desempenho dos estudantes para a aprendizagem significativa, levando-se em consideração que o ato de aprender é intransferível, só o indivíduo pode fazê-lo e ninguém pode aprender por outro, mas pode-se incentivar o interesse dos estudantes, explorando novas oportunidades de aprendizagem, bem mais centradas na atividade dos estudantes, utilizando a hibridização, que é a mescla de técnicas e ferramentas que auxiliam e dinamizam o aprendizado com a combinação entre ambientes presenciais e virtuais de ensino - usando a ferramenta dos MOOCs pela concepção da sala de aula invertida. Para avaliar a interferência da metodologia ativa de aprendizagem no desempenho, participaram da análise estudantes universitários, de duas instituições particulares, na área de Ciências Sociais Aplicadas, que após um semestre responderam a dois questionários de escalas validadas estatisticamente: Academic Motivation Scale e Escala Estratégica de Aprendizagem para ambientes on-line. Utilizou-se técnicas de análise multivariada, composta pela análise de componentes principais e análise de agrupamento, para avaliar a presença de grupos de motivação. Para a caracterização dos grupos de motivação obtidos foram calculadas as frequências dos grupos formados e médias das componentes principais, bem como as marcações de significância estatística para o teste-t de diferença de médias e técnicas de modelo de regressão para avaliar as médias finais (notas) dos estudantes de acordo com as covariáveis (estudantes que participaram do curso na plataforma MOOC, turma, grupo de motivação e gênero). Os principais resultados demonstram que a escolha de um método ativo de aprendizagem, utilizando a plataforma MOOC interfere em todos os grupos, no aumento da nota final em comparação aos estudantes que não acessaram a plataforma, portanto, não participaram de todo o processo, sendo esse efeito ainda mais expressivo para o grupo de motivação 1 (motivados pela excelência acadêmica), a IES-1 obteve um desempenho superior a IES-2. O êxito da introdução das metodologias ativas está diretamente relacionado a importância do envolvimento de todos os atores desse processo, com destaque as instituições de ensino e professores, para a formação de um indivíduo capaz de transformar a sua vida, o seu meio e a nossa sociedade. / This research analyzes the factors that support the use of active methods to increase the performance of the students towards meaningful learning, taking into account that the act of learning is not transferable, only the individual can do it and no one can learn on the other, but can stimulate the interest of students, exploring new learning opportunities and more focused on the activity of the students, using the hybridization, which is the mixture of techniques and tools to assist and streamline the learning with the combination of classroom and virtual teaching environments - using MOOCs tools for the design of the inverted classroom. In order to evaluate the interference of active learning methodology over performance, students from two private universities in the area of Applied Social Sciences took part in this research and, after one semester answered two statistically validated scales questionnaires: Academic Motivation Scale and Strategic Learning Scale, suitable for online environments. Multivariate analysis techniques, composed by Principal Component Analysis and Cluster Analysis, have been used to assess the presence of motivation groups. In order to characterize obtained motivation groups, frequencies of formed groups and main components averages were calculated as well as markings of statistical significance for the t-test mean difference and regression techniques to assess students final average (grades) according to the covariates (students who took the course in MOOC platform, classroom, motivation group and gender). Main results show that the choice of an active learning method, using MOOC platform interferes in all groups, in the final score increase comparatively to students who had not accessed the platform, thus not participating in the process; this effect was even more significant on motivation of group 1 (motivated by academic excellence), PU-1 achieved a superior performance comparatively to PU-2. The active methods introduction success is directly related to the importance of the involvement of all actors in the process, especially educational institutions and teachers, for the formation of an individual able to transform his life, his environment and our society .