1,813 research outputs found

    A pedagogical model for effective online teacher professional development—findings from the Teacher Academy initiative of the European Commission

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    During their careers, teachers experience change in education policy, societal trends, and cultural shifts in pedagogical thought, requiring continual adaptation and innovation of their practices. Coupled with this is an assumed intrinsic desire to progress, whether as part of their own subject expertise, or with a view to taking on a role as leader in school management or a specialist area. Effective support and opportunities for teachers to develop and apply their competences is crucial for maintaining both motivation and high standards in the profession. However, many teachers across Europe claim to struggle to have access to effective forms of continued professional development coupled with the numerous demands already made on their work. On-site courses with opportunities for peer learning remain popular but demand time and are not financially cost-effective in reaching a large number of teachers, nor are they viable during pandemic restrictions. By exploring the pedagogical model of the online courses of the European Commission's Teacher Academy in the context of these challenges, this article discusses how an effective, collaborative approach to online continued professional development can be developed as a way of addressing both teacher and education system needs

    Parareal with a Learned Coarse Model for Robotic Manipulation

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    A key component of many robotics model-based planning and control algorithms is physics predictions, that is, forecasting a sequence of states given an initial state and a sequence of controls. This process is slow and a major computational bottleneck for robotics planning algorithms. Parallel-in-time integration methods can help to leverage parallel computing to accelerate physics predictions and thus planning. The Parareal algorithm iterates between a coarse serial integrator and a fine parallel integrator. A key challenge is to devise a coarse model that is computationally cheap but accurate enough for Parareal to converge quickly. Here, we investigate the use of a deep neural network physics model as a coarse model for Parareal in the context of robotic manipulation. In simulated experiments using the physics engine Mujoco as fine propagator we show that the learned coarse model leads to faster Parareal convergence than a coarse physics-based model. We further show that the learned coarse model allows to apply Parareal to scenarios with multiple objects, where the physics-based coarse model is not applicable. Finally, we conduct experiments on a real robot and show that Parareal predictions are close to real-world physics predictions for robotic pushing of multiple objects. Code (https://doi.org/10.5281/zenodo.3779085) and videos (https://youtu. be/wCh2o1rf-gA) are publicly available

    The use of a synthetic shoulder patch for large and massive rotator cuff tears – a feasibility study

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    Background The aim of this study was to explore the feasibility of using a non-absorbable biocompatible polyester patch to augment open repair of massive rotator cuff tears (Patch group) and compare outcomes with other treatment options (Non-patch group). Methods Participants referred to orthopaedic clinics for rotator cuff surgery were recruited. Choice of intervention (Patch or Non-patch) was based on patient preference and intra-operative findings. Oxford Shoulder Score (OSS), Shoulder Pain and Disability Index (SPADI), and Constant score were completed at baseline and 6 months. Shoulder MRI was performed at baseline and 6 months to assess fat fraction and Goutallier classification pre- and post- treatment. Feasibility outcomes (including retention, consent and missing data) were assessed. Results Sixty-eight participants (29 in the Patch group, 39 in Non-patch group) were included (mean age 65.3 years). Conversion to consent (92.6%), missing data (0% at baseline), and attrition rate (16%) were deemed successful feasibility endpoints. There was significant improvement in the Patch group compared to Non-patch at 6 months in OSS (difference in medians 9.76 (95% CI 2.25, 17.29) and SPADI: 22.97 (95% CI 3.02, 42.92), with no substantive differences in Constant score. The patch group had a higher proportion of participants improving greater than MCID for OSS (78% vs 62%) and SPADI (63% vs 50%) respectively. Analysis of the 48 paired MRIs demonstrated a slight increase in the fat fraction for supraspinatus (53 to 55%), and infraspinatus (26 to 29%) at 6 months. These differences were similar and in the same direction when the participants were analysed by treatment group. The Goutallier score remained the same or worsened one grade in both groups equally. Conclusions This study indicates that a definitive clinical trial investigating the use of a non-absorbable patch to augment repair of massive rotator cuff tears is feasible. In such patients, the patch has the potential to improve shoulder symptoms at 6 months

    On the role of the upper part of words in lexical access : evidence with masked priming

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    More than 100 years ago, Huey (1908) indicated that the upper part of words was more relevant for perception than the lower part. Here we examined whether mutilated words, in their upper/lower portions (e.g., , , , ), can automatically access their word units in the mental lexicon. To that end, we conducted four masked repetition priming experiments with the lexical decision task. Results showed that mutilated primes produced a sizeable masked repetition priming effect. Furthermore, the magnitude of the masked repetition priming effect was greater when the upper part of the primes was preserved than when the lower portion was preserved –this was the case not only when the mutilated words were presented in lowercase but also when the mutilated words were presented in uppercase. Taken together, these findings suggest that the front-end of computational models of visual-word recognition should be modified to provide a more realistic account at the level of letter features.The research reported in this article has been partially supported by Grant PSI2008-04069/PSIC and CONSOLIDER-INGENIO2010 CSD2008-00048 from the Spanish Ministry of Science and Innovation and by Grant PTDC/PSI-PCO/104671/2008 from the Portuguese Foundation for Science and Technology

    Transposed-letter priming effects in reading aloud words and nonwords

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    A masked nonword prime generated by transposing adjacent inner letters in a word (e.g., jugde) facilitates the recognition of the target word (JUDGE) more than a prime in which the relevant letters are replaced by different letters (e.g., junpe). This transposed-letter (TL) priming effect has been widely interpreted as evidence that the coding of letter position is flexible, rather than precise. Although the TL priming effect has been extensively investigated in the domain of visual word recognition using the lexical decision task, very few studies have investigated this empirical phenomenon in reading aloud. In the present study, we investigated TL priming effects in reading aloud words and nonwords and found that these effects are of equal magnitude for the two types of items. We take this result as support for the view that the TL priming effect arises from noisy perception of letter order within the prime prior to the mapping of orthography to phonology.6 page(s
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