Recent adaptations of machine learning models in many businesses has underscored the need for quality training data. Typically, training supervised machine learning systems involves using large amounts of human-annotated data. Labeling data is expensive and can be a limiting factor in using machine learning models. To enable continued integration of machine learning systems in businesses and also easy access by users, researchers have proposed several alternatives to supervised learning. Weak supervision is one such alternative. Weak supervision or weakly supervised learning involves using noisy labels (weak signals of the data) from multiple sources to train machine learning systems. A weak supervision model aggregates multiple noisy label sources called weak signals in order to produce probabilistic labels for the data. The main allure of weak supervision is that it provides a cheap yet effective substitute for supervised learning without need for labeled data. The key challenge in training weakly supervised machine learning models is that the weak supervision leaves ambiguity about the possible true labelings of the data. In this dissertation, we aim to address the challenge associated with training weakly supervised learning models by developing new weak supervision methods. Our work focuses on learning with constraint-based weak supervision algorithms. Firstly, we will propose an adversarial labeling approach for weak supervision. In this method, the adversary chooses the labels for the data and a model learns by minimising the error made by the adversarial model. Secondly, we will propose a simple constrained based approach that minimises a quadratic objective function in order to solve for the labels of the data. Next we explain the notion of data consistency for weak supervision and propose a data consistent method for weakly supervised learning. This approach combines weak supervision labels with features of the training data to make the learned labels consistent with the data. Lastly, we use this data consistent approach to propose a general approach for improving the performance of weak supervision models. In this method, we combine weak supervision with active learning in order to generate a model that outperforms each individual approach using only a handful of labeled data. For each algorithm we propose, we report extensive empirical validation of it by testing it on standard text and image classification datasets. We compare each approach against baseline and state-of-the-art methods and show that in most cases we match or outperform the methods we compare against. We report significant gains of our method on both binary and multi-class classification tasks. / Doctor of Philosophy / Machine learning models learn to make predictions from data. In supervised learning, a machine learning model is fed data and corresponding labels for the data so that the model can learn to predict labels for new unseen test data. Curation of large fully supervised datasets is expensive and time consuming since it involves subject matter experts providing labels for each individual data example. The cost of collecting labels has become one of the major roadblocks for training machine learning models. An alternative to supervised training of machine learning models is weak supervision. Weak supervision or weakly supervised learning trains with cheap, and easy to define signals that noisily label the data. We refer to these signals as weak signals. A weak supervision model combines various weak signals to produce training labels for the data. The key challenge in weak supervision is how to combine the different weak signals while navigating misleading correlations in their errors. In this dissertation, we propose several algorithms for weakly supervised learning. We classify our methods as constraint-based weak supervision since weak supervision is provided as constraints to our algorithms. We use experiments on different text and image classification datasets to show that our methods are effective and outperform competing methods that we compare against. Lastly, we propose a general framework for improving the performance of weak supervision models by incorporating a few labeled data. With this method we are able to close the gap to supervised learning without the need for labeling all the data examples.

Actively engaging students in the classroom and promoting their interaction, both amongst themselves and with the instructor, is an important aspect to student learning. Research has demonstrated that student learning improves when instructors make use of pedagogical techniques which promote active learning. Equally important is instructor feedback from activities such as in-class assessments. Studies have shown that when instructor feedback is given at the time a new topic is introduced, student performance is improved. The focus of this thesis is the creation of a software program, Virtual Clicker, which addresses the need for active engagement, in-class feedback, and classroom interaction, even in large classrooms. When properly used it will allow for multi-directional feedback; teacher to student, student to teacher, and student to student. It also supports the use of digital ink for Tablet PCs in this interaction environment. / Master of Science

Many grand societal challenges are rooted in the need for new materials, such as those related to energy, health, and the environment. However, the traditional way of discovering new materials is basically trial and error. This time-consuming and expensive method can't meet the quickly growing requirements for material discovery. To meet this challenge, the government of the United States started the Materials Genome Initiative (MGI) in 2011. MGI aims at accelerating the pace and reducing the cost of discovering new materials. The success of MGI needs materials innovation infrastructure including data tools, computation tools, and experiment tools. The last decade has witnessed significant progress for MGI, especially with respect to hard materials. However, relatively less attention has been paid to soft materials. One important reason is the lack of experimental tools, especially characterization tools for high-throughput experimentation. This dissertation aims to enrich the toolbox by trying new sensor tools for high-throughput characterization of hydrogels. Piezoelectric-excited millimeter-sized cantilever (PEMC) sensors were used in this dissertation to characterize hydrogels. Their capability to investigate hydrogels was first demonstrated by monitoring the synthesis and stimuli-response of composite hydrogels. The PEMC sensors enabled in-situ study of how the manufacturing process, i.e. bulk vs. layer-by-layer, affects the structure and properties of hydrogels. Afterwards, the PEMC sensors were integrated with robots to develop a method of high-throughput experimentation. Various hydrogels were formulated in a well-plate format and characterized by the sensor tools in an automated manner. High-throughput characterization, especially multi-property characterization enabled optimizing the formulation to achieve tradeoff between different properties. Finally, the sensor-based high-throughput experimentation was combined with active learning for accelerated material discovery. A collaborative learning was used to guide the high-throughput formulation and characterization of hydrogels, which demonstrated rapid discovery of mechanically optimized composite glycogels. Through this dissertation, we hope to provide a new tool for high-throughput characterization of soft materials to accelerate the discovery and optimization of materials. / Doctor of Philosophy / Many grand societal challenges, including those associated with energy and healthcare, are driven by the need for new materials. However, the traditional way of discovering new materials is based on trial and error using low throughput computational and experimental methods. For example, it often takes several years, even decades, to discover and commercialize new materials. The lithium-ion battery is a good example. Traditional time-consuming and expensive methods cannot meet the fast-growing requirements of modern material discovery. With the development of computer science and automation, the idea of using data, artificial intelligence, and robots for accelerated materials discovery has attracted more and more attention. Significant progress has been made in metals and inorganic non-metal materials (e.g., semiconductors) in the past decade under the guidance of machine learning and the assistance of automated robots. However, relatively less progress has been made in materials having complex structures and dynamic properties, such as hydrogels. Hydrogels have wide applications in our daily lives, such as drugs and biomedical devices. One significant barrier to accelerated discovery and engineering of hydrogels is the lack of tools that can rapidly characterize the material's properties. In this dissertation, a sensor-based approach was created to characterize the mechanical properties and stimuli-response of soft materials using low sample volumes. The sensor was integrated with a robot to test materials in high-throughput formats in a rapid and automated measurement format. In combination with machine learning, the high-throughput characterization method was demonstrated to accelerate the engineering and optimization of several hydrogels. Through this dissertation, we hope to provide new tools and methods for rapid engineering of soft materials.

There is a significant body of literature showing improved student outcomes in higher education STEM courses when evidence-based instructional practices (EBIPs) are used. Despite this, traditional, lecture-style instruction remains the primary means of instruction in these courses. However, given the situation of the sudden shift to online teaching as a result of the COVID-19 pandemic, faculty were participating in training programs with greater frequency, and thus learning more about the use of EBIPs than ever before. Through the lens of Kurt Lewin’s theory of organizational change in the three stages of unfreezing, change, and refreezing, this explanatory mixed methods study sought to explore through a survey and interviews whether this shift to online teaching and the resulting increase in training participation did, in fact, result in changes in instructional practices, implementation, and perceptions of EBIPs, and whether any changes were sustained upon the return to in-person instruction.The survey tool used in this study was a subset of the Teaching Practices Inventory, developed by the Carl Wieman Science Education Initiative from the University of British Columbia. This generated a modified “extent of use of research-based teaching practices” (METP) score, as well as METP sub-scores in five subcategories of the survey. These results, as well as data obtained from demographic questions and questions about teaching responsibilities and training participation, informed the selection of twelve participants for semi-structured interviews. Through one-way ANOVA testing, the quantitative analysis showed a statistically significant increase in METP (p < .001) from Pre-Covid to Post-Covid scores. Statistical significance was also found in the subcategories of In Class Features (p = .003) and Collaboration (p = .005). Two-way ANOVA testing was also done to explore statistical significance for demographic subcategories, which was found to exist for gender, tenure status, and various categories relating to participation in training and professional development. Interview data supported the quantitative data analysis, and offered further insight and context for the changes that have been made and sustained, including changes regarding the use of educational technology tools, introduction of authentic learning experiences, streamlining of content, and intentional alignment of activities and assessments with course goals. Additional analysis showed faculty relied on virtual collaboration to develop community with other instructors, and realized the importance of student feedback to inform their instruction and of fostering a classroom community. Most significantly, the ability to see first-hand the effect of the pandemic on students and to have a window into their personal lives caused faculty to make sweeping changes with respect to their beliefs in the affective domains of learning, emphasizing the need for empathy, flexibility, and equity-mindedness in their classrooms. This study showed that faculty became convinced of the need for change, consistent with Lewin’s unfreezing stage, not solely through training and professional development, but largely through the realizations about the individuality of students that faculty experienced during the pandemic. This occurred simultaneously with an increase in virtual collaboration as well as the influence of changes peers had made and suggested upon the return to in-person instruction. The recognition of the need to center students in learning combined with these outside influences resulted in the increased use of EBIPs upon the return to in-person instruction, therefore creating the desired change. Lastly, these practices have been maintained as of two years after the return to in-person, thus indicating refreezing, and further data showed that faculty continue to adapt their practices to create more inclusive and student-centered learning environments. / Policy, Organizational and Leadership Studies

京都大学 / 新制・課程博士 / 博士(工学) / 甲第24146号 / 工博第5033号 / 新制||工||1786(附属図書館) / 京都大学大学院工学研究科航空宇宙工学専攻 / (主査)教授 藤本 健治, 教授 松野 文俊, 教授 森本 淳 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

京都大学 / 新制・課程博士 / 博士(人間・環境学) / 甲第25359号 / 人博第1101号 / 京都大学大学院人間・環境学研究科共生人間学専攻 / (主査)教授 STEWARTTimothy William, 教授 柳瀬 陽介, 准教授 笹尾 洋介, 准教授 DALSKYDavid Jerome, 教授 Lori Zenuk-Nishide / 学位規則第4条第1項該当 / Doctor of Human and Environmental Studies / Kyoto University / DFAM

The government of the Federal Democratic Republic of Ethiopia proclaimed a new curriculum for reconstructing the education system. The programme aimed at changing the predominantly-used teacher-centred instructional strategies to student-centred, active learning methods. This motivated the main research question of this study namely What are Ethiopian students’ learning styles and attitudes towards active learning approaches? The specific research questions that were investigated were: • What are the learning styles of students in Grade 10 public and private schools and at second year university level, and do these students prefer certain learning styles? • What are the attitudes of students at Grade 10 public and private schools, and at second year university level in respect of active learning approaches? • Do significant relationships exist between the students’ learning styles and their attitudes towards active learning as regards the four dimensions of the Index of Learning Styles (ILS), namely active-reflective, sensing-intuitive, visual-reflective and sequential-global? • Are there significant differences in the students’ learning styles and their attitudes towards active learning in respect of gender, different education levels and types of schools? In order to answer these questions, the study made use of an exploratory, descriptive design. By means of questionnaires data were collected from a purposefully and a conveniently selected sample of 920 students from Grade 10 government and private schools and second year university students in Hawassa, Ethiopia. The sample comprised of 506 males and 414 females, 400 students from Government schools and 249 from private schools, and 271 from the university. The data were analysed by means of descriptive statistics (means and correlations) and inferential statistics (analysis of variance). The results indicated that the majority of the students’ learning styles were balanced between the two dimensions of the ILS scales. As secondary preference, they tended towards moderate categories, and a small section of the students preferred the strong categories of the scales. Secondly, the study determined that the sampled students in general, demonstrated a positive attitude towards active learning. Thirdly, by means of the study a significant relationship was ascertained between the students’ attitudes towards active learning and the active-reflective dimension of the ILS. Fourthly, significant differences were indicated in the students’ learning styles and attitudes towards active learning in respect of their gender, their education level and the types of schools. / Psychology of Education / D. Ed. (Psychology of Education)

La compréhension automatique de la parole est au confluent des deux grands domaines que sont la reconnaissance automatique de la parole et l'apprentissage automatique. Un des problèmes majeurs dans ce domaine est l'obtention d'un corpus de données conséquent afin d'obtenir des modèles statistiques performants. Les corpus de parole pour entraîner des modèles de compréhension nécessitent une intervention humaine importante, notamment dans les tâches de transcription et d'annotation sémantique. Leur coût de production est élevé et c'est la raison pour laquelle ils sont disponibles en quantité limitée.Cette thèse vise principalement à réduire ce besoin d'intervention humaine de deux façons : d'une part en réduisant la quantité de corpus annoté nécessaire à l'obtention d'un modèle grâce à des techniques d'apprentissage semi-supervisé (Self-Training, Co-Training et Active-Learning) ; et d'autre part en tirant parti des réponses de l'utilisateur du système pour améliorer le modèle de compréhension.Ce dernier point touche à un second problème rencontré par les systèmes de compréhension automatique de la parole et adressé par cette thèse : le besoin d'adapter régulièrement leurs modèles aux variations de comportement des utilisateurs ou aux modifications de l'offre de services du système / Two wide research fields named Speech Recognition and Machine Learning meet with the Automatic Speech Language Understanding. One of the main problems in this domain is to obtain a sufficient corpus to train an efficient statistical model. Such speech corpora need a lot of human involvement to transcript and semantically annotate them. Their production cost is therefore quite high and they are difficultly available.This thesis mainly aims at reducing the need of human intervention in two ways: firstly, reducing the amount of corpus needed to build a model thanks to some semi-supervised learning methods (Self-Training, Co-Training and Active-Learning); And lastly, using the answers of the system end-user to improve the comprehension model.This last point addresses another problem related to automatic speech understanding systems: the need to adapt their models to the fluctuation of end-user habits or to the modification of the services list offered by the system

by Yuen Pong-yiu. / Thesis (Ph.D.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (p. 310-325 (2nd gp.)). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese; questionnaires in Chinese.

published_or_final_version / Education / Master / Master of Education