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.

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

Yes / A large majority of primary school pupils fail to achieve 30-min of daily, in-school moderate-to-vigorous physical activity (MVPA). The aim of this study was to investigate MVPA accumulation and subject frequency during academic lesson segments and the broader segmented school day. Methods: 122 children (42.6% boys; 9.9 ± 0.3 years) from six primary schools in North East England, wore uniaxial accelerometers for eight consecutive days. Subject frequency was assessed by teacher diaries. Multilevel models (children nested within schools) examined significant predictors of MVPA across each school-day segment (lesson one, break, lesson two, lunch, lesson three). Results: Pupils averaged 18.33 ± 8.34 min of in-school MVPA, and 90.2% failed to achieve the in-school 30-min MVPA threshold. Across all school-day segments, MVPA accumulation was typically influenced at the individual level. Lessons one and two—dominated by maths and English—were less active than lesson three. Break and lunch were the most active segments. Conclusion: This study breaks new ground, revealing that MVPA accumulation and subject frequency varies greatly during different academic lessons. Morning lessons were dominated by the inactive delivery of maths and English, whereas afternoon lessons involved a greater array of subject delivery that resulted in marginally higher levels of MVPA. / This research was funded by Redcar and Cleveland Borough Council.

Yes / This paper explores teachers’ educational values and how they shape their judgements about physically active learning (PAL). Twenty one teachers from four primary schools in Norway participated in focus groups. By conceptualising PAL as a didaktikk approach, the findings indicated that teachers engaged with PAL in a way that reflected their professional identity and previous experiences with the curriculum. Teachers valued PAL as a way of getting to know pupils in educational situations that were different from those when sedentary. These insights illustrate how PAL, as a didaktikk approach to teaching, can shift teachers’ perceptions of pupils’ knowledge, learning, and identity formation in ways that reflect the wider purposes of education. The paper gives support to a classroom discourse that moves beyond the traditional, sedentary one-way transfer of knowledge towards a more collaborative effort for pupils’ development. / This work was supported by Norwegian Directorate for Higher Education and Skills: [Grant Number 2019-1-NO01-KA203-060324]. The authors of this manuscript were supported and funded by the European Union ERASMUS+Strategic Partnership Fund as part of the Activating Classroom Teachers (ACTivate) project.

Yes / The adoption of physically active learning (PAL) in schools is becoming more widespread. To understand how PAL is being used in different countries and explore if and how methods and strategies differ, this paper draws cross-national comparisons in primary school teachers' use of PAL. Thirteen focus groups were conducted with 54 teachers from Norway, the Netherlands and the UK. Four themes were identified using thematic analysis: 1) teachers' values and beliefs about PAL; 2) influence of school context; 3) influence of the national policy context and; 4) managing teacher dissonance when using PAL. Use of PAL was related to teachers' values and beliefs and the degree to which these aligned with the context of the school and the wider educational system. The findings underline the importance of addressing teachers’ competence, opportunity and agency to use PAL in different contexts. / The authors of this manuscript were supported and funded by the European Union ERASMUS + Strategic Partnership Fund as part of the Activating Classroom Teachers project, ACTivate (Grant no 2019-1-N001-KA203-063024).

Yes / Physically active learning (PAL) has emerged as a promising way of eliciting health and education-based outcomes for pupils. Concurrently, research suggests large variability in how PAL is perceived, operationalized, and prioritized in practice across Europe. Therefore, this study aimed to co-develop a framework for action to support the adoption and implementation of PAL. Adopting a design thinking approach, 40 international stakeholders representing 13 countries engaged in an idea generation workshop during a two-day PAL international conference. Participants included professionals from research (n = 20), practice (n = 4) and policy (n = 1) or a combination (n = 15). Their experience with PAL ranged from none to 19 years (with an average of 3.9 years). Participants were allocated into one of six heterogeneous and multidisciplinary groups and led through interactive tasks to identify: the landscape for PAL across Europe, barriers to the adoption and implementation of PAL, and key objectives for research, policy and practice to improve the adoption and implementation of PAL. All discussions were audio recorded and prioritized objectives were transcribed verbatim and analysed using inductive qualitative content analysis. Five interlinked and mutually reinforcing themes were identified: (1) Integration of the health and education paradigms (2) Coherent national policy and decision making (3) Building confident and competent teachers (4) Adopting a whole school approach for PAL (5) Strengthening the evidence base for PAL. The priority action framework identifies five key areas for action to facilitate PAL adoption and implementation across Europe. Central to the success of border uptake of PAL is the integration of the health and education paradigms. To achieve this aim, reframing PAL as movement-centered pedagogy would provide a more holistic and inclusive perspective. / The authors of this manuscript were supported and funded by the European Union ERASMUS + Strategic Partnership Fund as part of the Activating Classroom Teachers project, ACTivate (Grant no 2019-1-N001-KA203-063024).

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 / 新制||人||258(附属図書館) / 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