Science education research continues to demonstrate improved learning with active-learning techniques compared to lectures. However, the question of which active-learning methods are the most effective for learning complex scientific principles in various context still remains. Models are commonly used in activities that allow students to simplify complex systems and understand how components interact. I investigated the outcomes for student learning and engagement of two model-based activities - concept models and game simulations. The activities were conducted in an introductory biology course in sixteen discussion sections. Eight sections were assigned to the concept model activity and eight to the simulation activity. To assess engagement, students filled out a Likert-scale questionnaire on enjoyment and usefulness of activity (concept model: 130 students for food web activity and 131 for carbon cycle activity; game simulation: 131 students for food web activity and 126 game simulation students during the carbon cycle activity). To assess student learning, 152 students completed pre-post homework assignment based on conservation and transformation of matter. Over 80% of students enjoyed both the concept-mapping and simulation activities. Students reported that the hands-on nature of the concept activity was helpful for understanding the connections in food webs. For the homework assessment, all students significantly increased in their scores from pre to post on the MC (paired t-test, meanpre = 4.86±1.6; meanpost = 5.23±1.6;p<.05) and TF assessments (paired t-test; meanpre = 2.06±1.0 meanpost = 2.32± 1.0; p<0.05). For the TF assessments, we observed the trend that students in the simulation group showed a greater improvement in their scores than students in the concept-mapping group (t-test; meanΔconcept = 0.11±1.4; meanΔsimulation =0 .43±1.0 p=.059). There was no difference between student improvement for the two groups on the MC assessment ( t-test meanΔconcept = 0.27±2.1; meanΔsimulation = 0.51±1.8 p=.474). Students’ responses to short answer questions showed those students’ ideas about the concept of matter conservation varied from naive to scientific. For example, students failed to conserve matter during nutrient cycling. More scientific responses demonstrated principled reasoning such as references to conservation of matter. The students within the two activities did not demonstrate large differences between their text responses for the short answer. Overall, students in both activity type demonstrated learning gains, though there was no significant difference between the activity types.

Apprendre de nouvelles connaissances et savoir-faire sensorimoteurs dans des environnements réels entraine un grand nombre de défis majeurs pour les robots d'aujourd'hui. Pour acquérir de nouveaux comportements, ceux-ci ont besoin d'explorer des espaces sensorimoteurs qui possèdent généralement les caractéristiques d'être de grande dimensionnalité, de grands volumes, redondants, et de comporter des zones de complexités différentes. Dans cette thèse qui entre dans le cadre de la robotique développementale, nous proposons différents processus permettant de guider et contraindre une acquisition autonome de comportements sensorimoteurs nouveaux dans de tels espaces. Nous proposons une approche unifiée de résolution de ces problèmes qui prend inspiration des phénomènes de contraintes développementales présentés en biologie et psychologie, et plus particulièrement des motivations intrinsèques et des contraintes maturationnelles. Après la formalisation de cadres computationnels basés sur ces notions, nous présentons trois architectures algorithmiques différentes, chacune réutilisée de manière intégrée dans la suivante:La première, appelée RIAC, pour Robust-Intelligent Adaptive Curiosity, correspond à l'implémentation d'un algorithme d'apprentissage actif développemental permettant d'orienter l'exploration dans des espaces bornés et de dimensionnalité connue, possédant des régions de différents niveaux de complexités. Ce système, qui utilise des heuristiques prenant inspiration des mécanismes de motivations intrinsèques basées sur les connaissances, permet de diriger efficacement une exploration progressive de nouvelles connaissances sensorimotrices, qui correspondent à l'apprentissage de modèles directs. Il entraine aussi l'émergence de trajectoires développementales auto-organisées relatives à l'orientation de l'exploration sensorimotrice vers des activités de complexités intermédiaires.Ensuite, nous proposons l'algorithme SAGG-RIAC, pour Self-Adaptive Goal Generation - RIAC, en tant que mécanisme d'exploration intrinsèquement motivée basée sur les compétences, qui permet à des robots dont les espaces sensorimoteurs sont de grandes dimensions, hautement redondants, et possédant des schémas corporels différents, d'apprendre efficacement et activement de nouveaux comportements moteurs dans leurs espaces de tâches. L'idée principale de cet algorithme est d'orienter le robot à effectuer un babillage actif dans un espace des tâches de faible dimensionnalité, en opposition à un babillage moteur effectué dans un espace de contrôle de plus grande dimension, en auto-générant activement et adaptivement des objectifs dans les régions de l'espace des tâches qui fournissent les meilleures améliorations de compétences, pour l'atteinte d'objectifs précédemment tentés. Enfin, nous introduisons l'algorithme McSAGG-RIAC, pour Maturationally-Constrained SAGG-RIAC, qui repose sur le couplage de modèles computationnels de motivations intrinsèques et de contraintes maturationnelles physiologiques. Nous argumentons que ces mécanismes peuvent avoir des interactions bidirectionnelles complexes permettant le contrôle actif de l'augmentation de la complexité du développement sensorimoteur, afin de diriger une exploration et un apprentissage efficaces. Nous introduisons plus particulièrement un modèle fonctionnel des contraintes maturationnelles inspiré par le processus de myélinisation chez les humains, et montrons comment celui-ci peut être couplé avec l'algorithme SAGG-RIAC. Nous montrons qualitativement et quantitativement que cette approche intégrée des trois architectures présentées pendant cette thèse permet de répondre à certaines des problématiques des environnements réels, en contrôlant la complexité, le volume, la dimensionnalité et la redondance des comportements explorés de manière intrinsèque au robot, diminuant de manière importante la nécessité de contraindre et préparer l'environnement de manière externe. / Learning new sensorimotor knowledge and know-how in real environments leads to an important number of challenges for today's robots. In order to learn new skills, they need to explore sensorimotor spaces which are generally high-dimensional, high-volume, redundant, and possess areas of heterogenous levels of complexity. In this thesis, introduced within the developmental robotics domain, we propose different processes in order to guide and constrain the autonomous acquisition of new sensorimotor skills in such spaces. We propose an unified approach in order to resolve these problems which takes inspiration from phenomenon of developmental constraints introduced in biology and psychology, and more particularly intrinsic motivations and maturational constraints. After formalizing a computational framework based on these notions, we present three different algorithmic architectures, each one reused in an integrated manner within the next one:The first one, called RIAC, for Robust-Intelligent Adaptive Curiosity, corresponds to the implementation of an active learning algorithm which orients the exploration in bounded spaces whose dimensionality is known and which possess regions of different levels of complexity. This system, which uses heuristics taking inspiration from knowledge based intrinsic motivations mechanisms, effectively directs a progressive exploration of new sensorimotor knowledge, which corresponds to the learning of forward models. It also leads to the emergence of self-organized developmental trajectories related to the orientation of the sensorimotor exploration toward activities of intermediate complexity. Then, we propose the SAGG-RIAC algorithm for Self-Adaptive Goal Generation - RIAC, as a competence based intrinsic motivations exploration mechanism, which allows highly-redundant robots whose sensorimotor spaces are high-dimensional to learn effectively and actively new motor skills in their task spaces. The main idea of this algorithm is to guide the robot to do active babbling in a low-dimensional task space, in contrast with a motor babbling carried out in a higher-dimensional control space, by actively and adaptively self-generating goals in regions of the task space which bring the highest improvement of competences for reaching previously attempted goals.Finally, we introduce the McSAGG-RIAC algorithm for Maturationally-Constrained SAGG-RIAC, which is based on a coupling of computational models of intrinsic motivation and physiological maturational constraints. We argue that these mechanisms may have complex bidirectional interactions allowing the active control of the increase of complexity in the sensorimotor development, in order to direct efficient learning and exploration processes. We introduce more particularly a functional model of maturational constraints inspired by the biological process of myelination, and show how this can be coupled with the SAGG-RIAC algorithm. We show qualitatively and quantitatively that this integrated approach of the three architectures introduced in this thesis answers some problematics raised by real environments, by controlling the complexity, volume, dimensionality and redundancy of skills explored in a manner intrinsic to the robot, thus decreasing in an important extent the necessity of constraining and preparing the environment in en external manner.

A journey in metaxis explores the facilitation of drama workshops using an adaptation of Theatre of the Oppressed, a participatory drama process used with high school students, teachers and others in the community. New possibilities of engagement open up as knowing emerges through a variety o f forms of dramatic action which are simultaneously the medium, subject and re-presentation of research. As a theatre pedagogue I explore how knowing and meaning emerge through theatre and in the interplay between my life and my work. Writing, then reading, narratives of my practice engages me in a conversation that helps me draw attention to my practice. Diverse roles and points of view of the drama facilitator begin to become apparent as these narratives speak through a spiralling process of shared experiences. Commentaries on these experiences lead to discussions of the implications of this inquiry for other forms of reflective leadership practice in drama and in education. Particular attention is placed on the role of the body and mind (bodymind) of facilitator and participants as they journey into an increasing awareness of senses, histories, the landscapes worked in, and the relationships that intertwine through the constant ebb and flow of the drama workshop. Using a framework that parallels the drama workshop I facilitate, I play with forms of texts, languages and styles to enter into the text(ure) of the worlds of facilitation so that we may come face to face with kinaesthetic and discursive experiences remembered and reconsidered. Writing my body into this exploration enables me to become mindfully aware of, and extends and transforms, my practice. I re-awaken the memory of my senses and re-connect with them in the moments of "performing" my teaching. Such poetic and expressive writing enables an evocation of the world of drama. Writing from and through a sensing body means that reflection on practice becomes not merely reporting experiences, but also celebrating and expressing the multi-vocal, multi-layered events that develop drama facilitation skills. Writing, then reading, about this process of coming to know my identity-in-process as a drama facilitator enables the interpretation, interrogation and transformation of how one becomes facilitator, "making the way as we go," (re)writing/performing our presence. / Education, Faculty of / Language and Literacy Education (LLED), Department of / Graduate

付記する学位プログラム名: 充実した健康長寿社会を築く総合医療開発リーダー育成プログラム / 京都大学 / 新制・課程博士 / 博士(医学) / 甲第23092号 / 医博第4719号 / 新制||医||1050(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 黒田 知宏, 教授 上杉 志成, 教授 藤渕 航 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

Handwriting recognition systems have achieved remarkable performance over the past several years with the advent of deep neural networks. For high-quality recognition, these models require large amounts of labeled training data, which can be difficult to obtain. Various methods to reduce this effort have been proposed in the realms of active and transfer learning, but not in combination. We propose a framework for fitting new handwriting recognition models that joins active and transfer learning into a unified framework. Empirical results show the superiority of our method compared to traditional active learning, transfer learning, or standard supervised training schemes.

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This study provides a framework to guide educators and researchers within departments of mathematics at institutions of higher education involved in reform efforts for undergraduate mainstream calculus. It does so by using motivational constructs from Self-Determination Theory to define and measure student-centeredness within both traditional and reformed calculus learning environments within a large-scale, quasi-experimental study. Motivational inventories assessing students’ perceived satisfaction of basic psychological needs and self-determined motivation were analyzed together with demographic variables, course outcomes, and prior math knowledge within traditional and reformed conditions in both Calculus I and Calculus II courses. Results include 1) positive correlates among students’ perceptions of satisfaction of basic psychological needs, intrinsic motivation, and achievement; 2) overall increased student perception of BPN-satisfaction in the reformed condition; and 3) directional variation in achievement and perception of BPN-satisfaction between conditions across subpopulations. The results demonstrate how student-centered calculus learning environments operate through motivational processes to improve academic outcomes and how learning environments may differentially affect demographic categories at institutions of higher education. Specifically, learning environments are not culturally or socially neutral and may, despite good intentions, be centered about privileged populations to the detriment of historically disenfranchised groups.

Students at the college level need good instruction and active learning has been shown to improve student retention and learning. Science, Technology, Engineering, and Math Faculty Institution (STEMFI) aims to assist faculty in implementing active learning in the classroom. This qualitative case study sought to understand faculty perspectives two years after completion of STEMFI in the areas of active learning implementation, methodology and student attitudes and impact. Data collected reported that the faculty participants still use active learning strategies in their classrooms two years after exiting the STEMFI program. The faculty also felt the need for a refresher course and felt that overall students respond well to active learning techniques. Faculty suggested a few improvements to STEMFI involving scheduling and the relevance of workshops and seminars. Overall, faculty were pleased with STEMFI and it was encouraging that faculty still use certain techniques taught during the program which can engage students as well as improve student retention and learning. With the suggestion from the faculty to have a refresher course, it would be advantageous to narrow down where the STEMFI program has been the most effective so that refresher courses could be designed as well as similar programs instituted in other departments, colleges, or at other universities. Although this professional learning program is focused on STEM faculty, it would be important for a university to consider how the main tenants of this program could also be used with faculty in other disciplines. This could provide needed and important knowledge for faculty in humanities, arts, education, and business to make changes in their classroom practice as well as additional avenues of research.

Evolution is the central organizing theory of biology. Without evolutionary theory, biology becomes a somewhat tangential assemblage of facts about living organisms, which is precisely how it is viewed by many students. Many teachers teach evolution in a limited capacity or avoid it entirely due to fear of opposition, lack of confidence in their own understanding, or lack of acceptance of the theory themselves. When evolution is not taught, or is not accepted, it cannot be utilized to make sense of the field, and is quickly forgotten by students. While some studies have shown a correlation between instruction about evolution and acceptance of evolution, many have not. Understanding which instructional factors, both pedagogical and conceptual, contribute to increases in evolution acceptance are paramount if we are going to make biology education more cohesive and applicable beyond the context of the course itself. To better understand what these factors may be, I utilized curriculum that I developed previously to teach introductory biology to non-biology majors that incorporated evolution as the organizing structure and appeared to produce considerable increases in acceptance of evolution based on the lack of hostility and pushback from the students in the course. I verified that the curriculum as taught produced increases in acceptance of evolution using the Measure of Acceptance of the Theory of Evolution (MATE) instrument as a measure of acceptance, and by asking students on the final exam what their position had been before instruction and if it had changed as a result of the course. Both measures revealed a considerable increase in evolution acceptance. Using a full factorial experimental design, tested three major pedagogical approaches that have all been hypothesized to contribute to increasing evolution acceptance: Constructivist-inspired vs Behaviorist-inspired, active vs less active instruction, and reflexive journaling vs not journaling. While all possible combinations of treatments showed statistically significant increases in evolution acceptance, there was no statistically significant difference between any of the treatments or combinations of treatments. Also, using Thematic Analysis, we coded and analyzed the responses that students provided as to the concepts from class that played a role in their having changed or not changed their positions on evolution as reported on the final exams, and in their reflexive journals which provided a valuable window into the concepts that we might emphasize or choose to remove or deemphasize in the future to maximize the probability that student acceptance of evolution will increase following instruction.