Literatura académica sobre el tema "Progressive learning"

This study examines the learning of ensemble skills by young musicians in progressive ensembles. Data collection took place in three music centres which form part of an LEA music service's ensemble provision. This study uniquely approaches ensembles as'socio-musical' phenomena. It finds description and explanation in the constantly changing and developing socio-musical interactions that form ensembles. Using an ethnographic approach it examines the practice of ensembles that are part of musical learning pyramids, through the eyes and actions of the ensemble members. There is an examination of ensemble membership and the social structures and interactions that form ensembles. This study explores, and for the first time identifies, a set of ensemble specific skills. These are the skills that a musician uses to negotiate, integrate and cooperate with other participants in the production of ensemble performance. It goes on to examine how ensemble specific skills are acquired and suggests that the learning process is one of serial performative responsibility transfer created within stratified centripetal progression. New, or novice, members of an ensemble start by participating at a peripheral level leaving it to others to take performative responsibility for the production of a negotiated collaborative realization of the musical intentions of the composer. This study has been influenced by the work of Lave and Wenger and social theories of learning. However, it departs from these theories by suggesting that the learning process within an ensemble is responsibility led and stratified.

<p> There is a large body of research that suggests the concept of a professional learning community (PLC) can promote improved student learning by increasing collective teacher capacity to meet the diverse learning needs of students (Reeves, 2016; Battersby &amp; Verdi, 2013; Marzano &amp; DuFour, 2011; Fullan 2010, Senge, 2006; Morrissey, 2000; Hord, 1997). Many schools have implemented PLCs around the world. Despite the well-documented benefits of PLCs, there is inconsistency to the extent with which PLCs are implemented within schools and districts. The purpose of this study was to (a) understand how the elementary division at Progressive Academy of Southeast Asia (PASA), an independent private school, has implemented PLCs; (b) learn about promising practices utilized by high performing PLCs to promote teacher collaboration and high productivity; and (c) understand barriers confronted by PLCs during the PLC process. A qualitative research design was used to understand the implementation process through focus group discussions. Focus group discussions were conducted with all grade levels, kindergarten to grade 5 in the elementary division. Ten themes emerged from the study (1) an alignment of belief in the mission and vision and purpose of PLCs by faculty and administration, (2) the elementary school has implemented systems and structures to support the PLC process, (3) there is a collaborative culture for the PLC process, (4) collective responsibility for student learning is still at its infancy stage, (5) PLCs are results oriented and have clearly established SMART goals that align with the elementary school&rsquo;s strategic plan, (6) instructional assistants are a strong system of support for learning for students, (7) trust is a key to high performing PLCs, (8) there is a low level of trust in some PLCs, (9) there is a transient faculty, and (10) there are an overwhelming number of initiatives that require the time of PLCs at the school.</p><p>

With the rapid development of deep learning techniques, the application of Convolutional Neural Network (CNN) has benefited the task of target object recognition. Several state-of-the-art object detectors have achieved excellent performance on the precision for object recognition. When it comes to applying the detection results for the real world application of collaborative robots, the reliability and robustness of the target object detection stage is essential to support efficient task allocation. In this work, collaborative robots task allocation is based on the assumption that each individual robotic agent possesses specialized capabilities to be matched with detected targets representing tasks to be performed in the surrounding environment which impose specific requirements. The goal is to reach a specialized labor distribution among the individual robots based on best matching their specialized capabilities with the corresponding requirements imposed by the tasks. In order to further improve task recognition with convolutional neural networks in the context of robotic task allocation, this thesis proposes an innovative approach for progressively refining the target detection process by taking advantage of the fact that additional images can be collected by mobile cameras installed on robotic vehicles. The proposed methodology combines a CNN-based object detection module with a refinement module. For the detection module, a two-stage object detector, Mask RCNN, for which some adaptations on region proposal generation are introduced, and a one-stage object detector, YOLO, are experimentally investigated in the context considered. The generated recognition scores serve as input for the refinement module. In the latter, the current detection result is considered as the a priori evidence to enhance the next detection for the same target with the goal to iteratively improve the target recognition scores. Both the Bayesian method and the Dempster-Shafer theory are experimentally investigated to achieve the data fusion process involved in the refinement process. The experimental validation is conducted on indoor search-and-rescue (SAR) scenarios and the results presented in this work demonstrate the feasibility and reliability of the proposed progressive refinement framework, especially when the combination of adapted Mask RCNN and D-S theory data fusion is exploited.

An assessment procedure to measure the learning potential of mildly and moderately mentally handicapped adults was investigated in this study. The procedure was developed to overcome the limitations of previously established learning potential assessment procedures by meeting the specific orientation necessary when assessing the mentally handicapped, by avoiding practice and mechanical memory effects, and by requiring the subjects to show the ability to retain and apply new cognitive skills. Thirty institutionalized mentally handicapped adults were matched on IG and then randomly assigned to an assessment or control group. For the assessment group the intervention procedure involved the Coloured Progressive Matrices test as a pretest measure, in four treatment sessions each two to three days apart, and as a posttest measure. Verbal and/or figural aids were provided when an incorrect response was given on a test item during the treatment sessions. The control group was provided the same number of administrations of the test but without the treatment intervention. Significant differences were found between the pre and post test performance levels of the assessment group. The control group showed no significant gains. The learning potential assessment procedure used in this study provided a measure of the learning potential of mentally handicapped institutionalized adults.The results indicate that the mentally handicapped can perform at much higher levels than those assessed by standardly used intellectual measures. Implications are that through the use of this approach a clearer discrimination of intellectual ability within the broad classifications of the mentally handicappped can be gained. Such information could be used to aid in the selection of peers, developmental programming decisions, and provide prescriptive information about the most efficacious modes of learning for an individual.

L'addestramento di algotritmi di deep learning (DL) richiede una grande quantità di dati, che però spesso non sono disponibili in ambito medico. In questa tesi viene proposto un modello per la generazione di dataset sintetici etichettati nell'ambito dell'imaging medico ad alta risoluzione. Dopo aver presentato vantaggi e limiti dell'uso delle tecniche di DL in radiologia, vengono proposte le Generative Adversarial Networks (GANs) come possibile soluzione per superare tali limiti. Illustrando lo stato dell'arte relativo alle GAN, viene focalizzata l'attenzione sulle Progressive Growing GAN, capaci di generare immagini ad alta risoluzione, e sulle Auxiliary Classifier GAN (ACGAN), capaci di generare immagini target. Sulla base di questi modelli, vengono proposte le innovative Progressive ACGAN (PACGAN), progettate per generare immagini target ad elevata risoluzione. L'obiettivo di questo lavoro di tesi è sfruttare la capacità delle GAN di creare una rappresentazione nello spazio latente dei dati del training set, sia per generare immagini target ad alta risoluzione (256 x 256), che per effettuare una classificazione. Il modello proposto viene testato su un dataset contenente 200 immagini di risonanza magnetica (RM) cerebrale di soggetti sani e pazienti con malattia di Alzheimer. I risultati del modello sono molto promettenti. La qualità delle immagini generate è stata valutata sia visivamente che quantitativamente, tramite FID (Fréchet Inception Distance) e MS-SSIM (Multi-Scale Structural Similarity Index), evidenziando una maggiore capacità delle PACGAN di rappresentare immagini target ad alta risoluzione rispetto alle ACGAN. Le performance di classificazione risultano ottime nel training set, con discreta capacità di generalizzare su nuovi dati. Il modello proposto consente quindi di generare immagini target ad alta risoluzione che possono essere usate per ottenere dataset sintetici.