2,469 research outputs found

    Developing great teachers through professional development: a comparative international case study in England, Israel, South Korea, and Turkey

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    This comparative international case study explores teacher quality, that is, how teachers, who are regarded as great, train and develop. In particular, the thesis investigates ways in which participation in professional development programmes contributes to teachers’ professional knowledge and the personal virtues involved in teaching chemistry at secondary school level in England, Israel, South Korea, and Turkey as case study nations. The study employs a comparative case study approach. Empirical data collection was preceded by a document analysis and a comprehensive literature review which revealed three themes, namely community of practice, pedagogical content knowledge, and professional beliefs and virtues as impacting teachers in becoming great teachers. These themes were explored in practice utilising qualitative data collection methods, namely semi structured interviews with science teachers (mainly chemistry) who participated in professional development programmes and through observing lessons and professional development activities of teachers teaching science to 14-18-year-olds. Data was collected in South Korea, Israel, Turkey, and the United Kingdom (England) over a 1-year period. A volunteer sample of 40 science teachers (10 teachers for each country) were interviewed. Ten professional development activities were observed. The total length of observed PD activities was 1500 minutes. Nine science teachers were observed in four countries. The total length of observed lessons was 525 minutes. Four focus group interviews with the participation of 18 teachers were conducted. Thematic analysis was used to analyse the data. The data shows that great teacher appears differently in the four nations. A great teacher is identified variously as an amalgamation of a lifelong learner (South Korea), a moral exemplar (Turkey), a reflective practitioner (England), and an educator (Israel). Great teachers as lifelong learners promote students’ practical wisdom and wise decision-making ability, skills which are required to live a good life. Moral exemplars transmit their personal moral values to their students. Reflective practitioner teachers demonstrate intellectual and performance virtues in practice. As educators, great teachers motivate their students to be good human beings. The results of the study reveal that practical wisdom is an essential lens for making teachers educationally wise people. Great teacher is perceived to empower practical wisdom, which helps teachers establish mutual understanding and let them have more space to draw ii upon intellectual, social, moral and performance virtues through collaboration, mutual engagement and sharing in community of practice. The teachers in the study who participated in community-based professional development programmes enhanced the intellectual, moral, performance, and social virtues, pedagogical content knowledge associated with being a great teacher. The study finds that nations whose educational systems build strong connections between teachers through development and application of learning communities tend to generate a higher proportion of great teachers and that those teachers have positive and extensive influences on each other’s intellectual and personal development. This research also found that one of the most important dispositions that enable teachers to become responsible for students' learning is passion in science teaching. The teachers' passion, motivation, and love for teaching helped them to expand their professional knowledge and techniques of instruction in distinctive manners. The character traits that a great teacher must possess should receive a lot of consideration. Emphasise also should be on developing character strengths in the professional development. Community of practice has potential to achieve this through mutual engagement, shared repertoire and joint enterprise. The research emphasizes the vital role of teachers' passion for science teaching in enabling them to take responsibility for their students' learning. It advocates for the development of character strengths in teacher professional development, particularly through the cultivation of community of practice, characterized by mutual engagement, shared repertoire, and joint enterprise. This comparative study offers valuable insights into the dynamic interplay of teacher development, enhancing the quality of education across diverse contexts

    Musculoskeletal Modeling and Control of the Human Upper Limb during Manual Wheelchair Propulsion: Application in Functional Electrical Stimulation Rehabilitation Therapy

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    Manual wheelchair users rely on their upper limbs for independence and daily activities. The high incidence of upper limb injuries can be attributed to the significant muscular demands imposed by propulsion as a repetitive movement. People with spinal cord injury are at high risk for upper limb injuries, including neuromusculoskeletal pathologies and nociceptive pain, as human upper limbs are poorly designed to facilitate chronic weight-bearing activities, such as manual wheelchair propulsion. Comprehending the underlying biomechanical mechanisms of motor control and developing appropriate rehabilitation tasks are essential to deal with the effects of poor motor control on the performance of manual wheelchair users and prevent long-term upper limb disability, which can interrupt electrical signals between the brain and muscles. Functional electrical stimulation utilizes low-intensity electrical signals to artificially generate body movements by stimulating the damaged peripheral nerves of patients with impaired motor control. Therefore, this study investigates the central nervous system strategy to control human movements, which can be used for task-specific functional electrical stimulation rehabilitation therapy. To this aim, two degrees of freedom musculoskeletal model of the upper limb, including six muscles, is developed, and an optimal controller consisting of two separate optimal parts is proposed to track the desired trajectories in the joint space and estimate the optimal muscle activations regarding physiological constraints. The simulation results are validated with electromyography datasets collected from twelve participants. This study's primary advantages are generating optimal joint torques, accurate trajectory tracking, and good similarities between estimated and measured muscle activations

    Sociotechnical Imaginaries, the Future and the Third Offset Strategy

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    Subjective Excess: Aesthetics, Character, and Non-Normative Perspectives in Serial Television After 2000

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    This dissertation aims to fill gaps in contemporary television scholarship with regards to aesthetics and character subjectivity. By analyzing eight series that have all aired after 2000, there is a marked trend in series that use an excessive visual and aural style to not only differentiate themselves from other programming, but also to explore non-normative perspectives. Now more willing to explore previously taboo topics such as mental health, addiction, illness, and trauma, the shows featured in this dissertation show how a seemingly excessive televisual aesthetic works with television’s seriality to create narrative complexity and generate character development. Chapters are arranged by mode of production with the first chapter focusing on the series Grey’s Anatomy and Hannibal as a means of exploring the production and distribution practices surrounding network TV. The second chapter examines the basic cable series Crazy Ex-Girlfriend and Legion and posits how the narrowcasting of cable allows for more nuanced character representations through aesthetics. In the third chapter, the impact HBO has had on the television medium is explored through Carnivàle and Euphoria. The final chapter looks at contemporary series The Boys and Unbreakable Kimmy Schmidt as a way to better understand how the medium’s production and distribution has shifted during the convergence era. Ultimately, this dissertation will argue that in addition to further explorations of aesthetics, television studies is in need of a medium specific vernacular for creating meaningful textual analyses that avoid an overreliance on cinematic terminology

    Microcredentials to support PBL

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    On the Utility of Representation Learning Algorithms for Myoelectric Interfacing

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    Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steer—a gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden

    Requirements for Explainability and Acceptance of Artificial Intelligence in Collaborative Work

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    The increasing prevalence of Artificial Intelligence (AI) in safety-critical contexts such as air-traffic control leads to systems that are practical and efficient, and to some extent explainable to humans to be trusted and accepted. The present structured literature analysis examines n = 236 articles on the requirements for the explainability and acceptance of AI. Results include a comprehensive review of n = 48 articles on information people need to perceive an AI as explainable, the information needed to accept an AI, and representation and interaction methods promoting trust in an AI. Results indicate that the two main groups of users are developers who require information about the internal operations of the model and end users who require information about AI results or behavior. Users' information needs vary in specificity, complexity, and urgency and must consider context, domain knowledge, and the user's cognitive resources. The acceptance of AI systems depends on information about the system's functions and performance, privacy and ethical considerations, as well as goal-supporting information tailored to individual preferences and information to establish trust in the system. Information about the system's limitations and potential failures can increase acceptance and trust. Trusted interaction methods are human-like, including natural language, speech, text, and visual representations such as graphs, charts, and animations. Our results have significant implications for future human-centric AI systems being developed. Thus, they are suitable as input for further application-specific investigations of user needs
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