926 resultados para Task-Based Instruction (TBI)


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Perspective taking is a crucial ability that guides our social interactions. In this study, we show how the specific patterns of errors of brain-damaged patients in perspective taking tasks can help us further understand the factors contributing to perspective taking abilities. Previous work (e.g., Samson, Apperly, Chiavarino, & Humphreys, 2004; Samson, Apperly, Kathirgamanathan, & Humphreys, 2005) distinguished two components of perspective taking: the ability to inhibit our own perspective and the ability to infer someone else’s perspective. We assessed these components using a new nonverbal false belief task which provided different response options to detect three types of response strategies that participants might be using: a complete and spared belief reasoning strategy, a reality-based response selection strategy in which participants respond from their own perspective, and a simplified mentalising strategy in which participants avoid responding from their own perspective but rely on inaccurate cues to infer the other person’s belief. One patient, with a self-perspective inhibition deficit, almost always used the reality-based response strategy; in contrast, the other patient, with a deficit in taking other perspectives, tended to use the simplified mentalising strategy without necessarily transposing her own perspective. We discuss the extent to which the pattern of performance of both patients could relate to their executive function deficit and how it can inform us on the cognitive and neural components involved in belief reasoning.

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Les enfants d’âge préscolaire (≤ 5 ans) sont plus à risque de subir un traumatisme crânio-cérébral (TCC) que les enfants plus agés, et 90% de ces TCC sont de sévérité légère (TCCL). De nombreuses études publiées dans les deux dernières décennies démontrent que le TCCL pédiatrique peut engendrer des difficultés cognitives, comportementales et psychiatriques en phase aigüe qui, chez certains enfants, peuvent perdurer à long terme. Il existe une littérature florissante concernant l'impact du TCCL sur le fonctionnement social et sur la cognition sociale (les processus cognitifs qui sous-tendent la socialisation) chez les enfants d'âge scolaire et les adolescents. Or, seulement deux études ont examiné l'impact d'un TCCL à l'âge préscolaire sur le développement social et aucune étude ne s'est penchée sur les répercussions socio-cognitives d'un TCCL précoce (à l’âge préscolaire). L'objectif de la présente thèse était donc d'étudier les conséquences du TCCL en bas âge sur la cognition sociale. Pour ce faire, nous avons examiné un aspect de la cognition sociale qui est en plein essor à cet âge, soit la théorie de l'esprit (TE), qui réfère à la capacité de se mettre à la place d'autrui et de comprendre sa perspective. Le premier article avait pour but d'étudier deux sous-composantes de la TE, soit la compréhension des fausses croyances et le raisonnement des désirs et des émotions d'autrui, six mois post-TCCL. Les résultats indiquent que les enfants d'âge préscolaire (18 à 60 mois) qui subissent un TCCL ont une TE significativement moins bonne 6 mois post-TCCL comparativement à un groupe contrôle d'enfants n'ayant subi aucune blessure. Le deuxième article visait à éclaircir l'origine de la diminution de la TE suite à un TCCL précoce. Cet objectif découle du débat qui existe actuellement dans la littérature. En effet, plusieurs scientifiques sont d'avis que l'on peut conclure à un effet découlant de la blessure au cerveau seulement lorsque les enfants ayant subi un TCCL sont comparés à des enfants ayant subi une blessure n'impliquant pas la tête (p.ex., une blessure orthopédique). Cet argument est fondé sur des études qui démontrent qu'en général, les enfants qui sont plus susceptibles de subir une blessure, peu importe la nature de celle-ci, ont des caractéristiques cognitives pré-existantes (p.ex. impulsivité, difficultés attentionnelles). Il s'avère donc possible que les difficultés que nous croyons attribuables à la blessure cérébrale étaient présentes avant même que l'enfant ne subisse un TCCL. Dans cette deuxième étude, nous avons donc comparé les performances aux tâches de TE d'enfants ayant subi un TCCL à ceux d'enfants appartenant à deux groupes contrôles, soit des enfants n'ayant subi aucune blessure et à des pairs ayant subi une blessure orthopédique. De façon générale, les enfants ayant subi un TCCL ont obtenu des performances significativement plus faibles à la tâche évaluant le raisonnement des désirs et des émotions d'autrui, 6 mois post-blessure, comparativement aux deux groupes contrôles. Cette étude visait également à examiner l'évolution de la TE suite à un TCCL, soit de 6 mois à 18 mois post-blessure. Les résultats démontrent que les moindres performances sont maintenues 18 mois post-TCCL. Enfin, le troisième but de cette étude était d’investiguer s’il existe un lien en la performance aux tâches de TE et les habiletés sociales, telles qu’évaluées à l’aide d’un questionnaire rempli par le parent. De façon intéressante, la TE est associée aux habiletés sociales seulement chez les enfants ayant subi un TCCL. Dans l'ensemble, ces deux études mettent en évidence des répercussions spécifiques du TCCL précoce sur la TE qui persistent à long terme, et une TE amoindrie seraient associée à de moins bonnes habiletés sociales. Cette thèse démontre qu'un TCCL en bas âge peut faire obstacle au développement sociocognitif, par le biais de répercussions sur la TE. Ces résultats appuient la théorie selon laquelle le jeune cerveau immature présente une vulnérabilité accrue aux blessures cérébrales. Enfin, ces études mettent en lumière la nécessité d'étudier ce groupe d'âge, plutôt que d'extrapoler à partir de résultats obtenus avec des enfants plus âgés, puisque les enjeux développementaux s'avèrent différents, et que ceux-ci ont potentiellement une influence majeure sur les répercussions d'une blessure cérébrale sur le fonctionnement sociocognitif.

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When a task must be executed in a remote or dangerous environment, teleoperation systems may be employed to extend the influence of the human operator. In the case of manipulation tasks, haptic feedback of the forces experienced by the remote (slave) system is often highly useful in improving an operator's ability to perform effectively. In many of these cases (especially teleoperation over the internet and ground-to-space teleoperation), substantial communication latency exists in the control loop and has the strong tendency to cause instability of the system. The first viable solution to this problem in the literature was based on a scattering/wave transformation from transmission line theory. This wave transformation requires the designer to select a wave impedance parameter appropriate to the teleoperation system. It is widely recognized that a small value of wave impedance is well suited to free motion and a large value is preferable for contact tasks. Beyond this basic observation, however, very little guidance exists in the literature regarding the selection of an appropriate value. Moreover, prior research on impedance selection generally fails to account for the fact that in any realistic contact task there will simultaneously exist contact considerations (perpendicular to the surface of contact) and quasi-free-motion considerations (parallel to the surface of contact). The primary contribution of the present work is to introduce an approximate linearized optimum for the choice of wave impedance and to apply this quasi-optimal choice to the Cartesian reality of such a contact task, in which it cannot be expected that a given joint will be either perfectly normal to or perfectly parallel to the motion constraint. The proposed scheme selects a wave impedance matrix that is appropriate to the conditions encountered by the manipulator. This choice may be implemented as a static wave impedance value or as a time-varying choice updated according to the instantaneous conditions encountered. A Lyapunov-like analysis is presented demonstrating that time variation in wave impedance will not violate the passivity of the system. Experimental trials, both in simulation and on a haptic feedback device, are presented validating the technique. Consideration is also given to the case of an uncertain environment, in which an a priori impedance choice may not be possible.

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The relevance of explicit instruction has been well documented in SLA research. Despite numerous positive findings, however, the issue continues to engage scholars worldwide. One issue that was largely neglected in previous empirical studies - and one that may be crucial for the effectiveness of explicit instruction - is the timing and integration of rules and practice. The present study investigated the extent to which grammar explanation (GE) before practice, grammar explanation during practice, and individual differences impact the acquisition of L2 declarative and procedural knowledge of two grammatical structures in Spanish. In this experiment, 128 English-speaking learners of Spanish were randomly assigned to four experimental treatments and completed comprehension-based task-essential practice for interpreting object-verb (OV) and ser/estar (SER) sentences in Spanish. Results confirmed the predicted importance of timing of GE: participants who received GE during practice were more likely to develop and retain their knowledge successfully. Results further revealed that the various combinations of rules and practice posed differential task demands on the learners and consequently drew on language aptitude and WM to a different extent. Since these correlations between individual differences and learning outcomes were the least observed in the conditions that received GE during practice, we argue that the suitable integration of rules and practice ameliorated task demands, reducing the burden on the learner, and accordingly mitigated the role of participants’ individual differences. Finally, some evidence also showed that the comprehension practice that participants received for the two structures was not sufficient for the formation of solid productive knowledge, but was more effective for the OV than for the SER construction.

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The new generation of artificial satellites is providing a huge amount of Earth observation images whose exploitation can report invaluable benefits, both economical and environmental. However, only a small fraction of this data volume has been analyzed, mainly due to the large human resources needed for that task. In this sense, the development of unsupervised methodologies for the analysis of these images is a priority. In this work, a new unsupervised segmentation algorithm for satellite images is proposed. This algorithm is based on the rough-set theory, and it is inspired by a previous segmentation algorithm defined in the RGB color domain. The main contributions of the new algorithm are: (i) extending the original algorithm to four spectral bands; (ii) the concept of the superpixel is used in order to define the neighborhood similarity of a pixel adapted to the local characteristics of each image; (iii) and two new region merged strategies are proposed and evaluated in order to establish the final number of regions in the segmented image. The experimental results show that the proposed approach improves the results provided by the original method when both are applied to satellite images with different spectral and spatial resolutions.

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 Implementation of a care bundle for nursing management of patients with severe traumatic brain injury was feasible in the Thai context. Use of an evidence-based care bundle increased emergency nurses’ knowledge regarding severe TBI management and improved the care delivered during the initial emergency nursing management of these patients.

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BACKGROUND: There has been a recent proliferation in the development of smartphone applications (apps) aimed at modifying various health behaviours. While interventions that incorporate behaviour change techniques (BCTs) have been associated with greater effectiveness, it is not clear to what extent smartphone apps incorporate such techniques. The purpose of this study was to investigate the presence of BCTs in physical activity and dietary apps and determine how reliably the taxonomy checklist can be used to identify BCTs in smartphone apps.

METHODS: The top-20 paid and top-20 free physical activity and/or dietary behaviour apps from the New Zealand Apple App Store Health & Fitness category were downloaded to an iPhone. Four independent raters user-tested and coded each app for the presence/absence of BCTs using the taxonomy of behaviour change techniques (26 BCTs in total). The number of BCTs included in the 40 apps was calculated. Krippendorff's alpha was used to evaluate interrater reliability for each of the 26 BCTs.

RESULTS: Apps included an average of 8.1 (range 2-18) techniques, the number being slightly higher for paid (M = 9.7, range 2-18) than free apps (M = 6.6, range 3-14). The most frequently included BCTs were "provide instruction" (83% of the apps), "set graded tasks" (70%), and "prompt self-monitoring" (60%). Techniques such as "teach to use prompts/cues", "agree on behavioural contract", "relapse prevention" and "time management" were not present in the apps reviewed. Interrater reliability coefficients ranged from 0.1 to 0.9 (Mean 0.6, SD = 0.2).

CONCLUSIONS: Presence of BCTs varied by app type and price; however, BCTs associated with increased intervention effectiveness were in general more common in paid apps. The taxonomy checklist can be used by independent raters to reliably identify BCTs in physical activity and dietary behaviour smartphone apps.

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The popularity of mobile devices such as smart phones and tablets has led to a growing use of spatial crowdsourcing in recent years. However, current solution requires the workers send their locations to a centralized server, which leads to a privacy threat. One of the key challenges of spatial crowdsourcing is to maximize the number of assigned tasks with workers’ location privacy preserved. In this paper, we focus on the reward-based spatial crowdsourcing and propose a two-stage method which consists of constructing a differentially private contour plot followed by task assignment with optimized-reward allocation. Experiments on real dataset demonstrate the availability of the proposed method.

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Motor vehicle accidents are one of the main killers on the road. Modern vehicles have several safety features to improve the stability and controllability. The tire condition is critical to the proper function of the designed safety features. Under or over inflated tires adversely affects the stability of vehicles. It is generally the vehicle's user responsibility to ensure the tire inflation pressure is set and maintained to the required value using a tire inflator. In the tire inflator operation, the vehicle's user sets the desired value and the machine has to complete the task. During the inflation process, the pressure sensor does not read instantaneous static pressure to ensure the target value is reached. Hence, the inflator is designed to stop repetitively for pressure reading and avoid over inflation. This makes the inflation process slow, especially for large tires. This paper presents a novel approach using artificial neural network based technique to identify the tire size. Once the tire size is correctly identified, an optimized inflation cycle can be computed to improve performance, speed and accuracy of the inflation process. The developed neural network model was successfully simulated and tested for predicting tire size from the given sets of input parameters. The test results are analyzed and discussed in this paper.

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There is a growing interest in identifying inorganic material affinity classes for peptide sequences due to the development of bionanotechnology and its wide applications. In particular, a selective model capable of learning cross-material affinity patterns can help us design peptide sequences with desired binding selectivity for one inorganic material over another. However, as a newly emerging topic, there are several distinct challenges of it that limit the performance of many existing peptide sequence classification algorithms. In this paper, we propose a novel framework to identify affinity classes for peptide sequences across inorganic materials. After enlarging our dataset by simulating peptide sequences, we use a context learning based method to obtain the vector representation of each amino acid and each peptide sequence. By analyzing the structure and affinity class of each peptide sequence, we are able to capture the semantics of amino acids and peptide sequences in a vector space. At the last step we train our classifier based on these vector features and the heuristic rules. The construction of our models gives us the potential to overcome the challenges of this task and the empirical results show the effectiveness of our models.