867 resultados para Concurrent Feedback


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It is a big challenge to clearly identify the boundary between positive and negative streams. Several attempts have used negative feedback to solve this challenge; however, there are two issues for using negative relevance feedback to improve the effectiveness of information filtering. The first one is how to select constructive negative samples in order to reduce the space of negative documents. The second issue is how to decide noisy extracted features that should be updated based on the selected negative samples. This paper proposes a pattern mining based approach to select some offenders from the negative documents, where an offender can be used to reduce the side effects of noisy features. It also classifies extracted features (i.e., terms) into three categories: positive specific terms, general terms, and negative specific terms. In this way, multiple revising strategies can be used to update extracted features. An iterative learning algorithm is also proposed to implement this approach on RCV1, and substantial experiments show that the proposed approach achieves encouraging performance.

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Despite an ostensibly technology-driven society, the ability to communicate orally continues to feature as an essential ability for students at school and university, as it is for graduates in the workplace. Pedagogically, one rationale is that the need to develop effective oral communication skills is tied to life-long learning which includes successful participation in future work-related tasks. One tangible way that educators have assessed proficiency in the area of communication is through prepared oral presentations. While much of the literature uses the terms 'oral communication' and 'oral presentation' interchangeably, some writers question the role more formal presentations play in the overall development of oral communication skills. However, such formal speaking tasks continue to be a recognised assessment practice in both the secondary school and academy, and, therefore, worthy of further investigation. Adding to the discussion, this thesis explores the knowledge and skills students bring into the academy from previous educational experiences. It examines some of the teaching and assessment methods used in secondary schools to develop oral communication skills through the use of formal oral presentations. Specifically, it investigates criterion-referenced assessment sheets and how these tools are used as a form of instruction, as well as their role and effectiveness in the evaluation of student ability. The focus is on the student's perspective and includes 12 semi-structured interviews with school students. The purpose of this thesis is to explore key thematics underpinning oral communication and to identify tensions between expectations and practice. While acknowledging the breadth and depth of material available under the heading of 'communication theory', this study specifically draws on an expanded view of the rhetorical tradition to fully interrogate the assumptions supporting the practice of assessing oral presentations. Finally, this thesis recommends reconnecting with an updated understanding of rhetoric as a way of assisting in the development of expressive, articulate and discerning communicators.

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The use of mobile devices such as smart phones and tablets in classrooms has been met with mixed sentiments. Some instructors and teachers see them as a distraction and regularly ban their usage. Others who see their potential to enhance learning have started to explore ways to integrate them into their teaching in an attempt to improve student engagement. In this paper we report on a pilot study that forms part of a university-wide project reconceptualising its approach to the student evaluation of learning and teaching. In a progressive decision to embrace mobile technology, the university decided to trial a smart phone app designed for students to check-in to class and leave feedback on the spot. Our preliminary findings from trialling the app indicate that the application establishes a more immediate feedback loop between students and teachers. However, the app’s impact depends on how feedback is shared with students and how the teaching team responds.

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In recent times, higher education institutions have paid increasing attention to the views of students to obtain feedback on their experience of learning and teaching through internal surveys. This article reviews research in the field and reports on practices in other Australian universities. Findings demonstrate that while student feedback is valued and used by all Australian universities, survey practices are idiosyncratic and in the majority of cases, questionnaires lack validity and reliability; data are used inadequately or inappropriately; and they offer limited potential for cross-sector benchmarking. The study confirms the need for institutions to develop an overarching framework for evaluation in which a valid, reliable, multidimensional and useful student feedback survey constitutes just one part. Given external expectations and internal requirements to collect feedback from students on their experience of learning and teaching, the pursuit of sound evaluation practices will continue to be of interest at local, national and international levels.

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Concurrent and longitudinal links between children’s own and their nominated best friends’ antisocial and prosocial behavior were studied in a normative sample of 3–5-year-olds (N = 203). Moderating effects of age and gender were also explored. Subscales of the Strength and Difficulties Questionnaire (SDQ) were used to obtain teacher ratings of behavior for each target child and his/her nominated best friends. Nomination of best friends with higher levels of antisocial behavior and lower levels of prosocial behavior was concurrently linked to more antisocial behavior in boys. Nomination of highly prosocial best friends was concurrently linked to more prosocial behavior in both boys and girls. However, the study found no longitudinal effects of best friends’ behavior on target child’s behavior over a one-year period. A group of children who nominated no best friends at T1 were generally perceived as less prosocial, but not more antisocial, than other children. © 2011 Elsevier Inc. All rights reserved.

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Objective: Substance use is common in first-episode psychosis, and complicates the accurate diagnosis and treatment of the disorder. The differentiation of substance-induced psychotic disorders (SIPD) from primary psychotic disorders (PPD) is particularly challenging. This cross-sectional study compares the clinical, substance use and functional characteristics of substance using first episode psychosis patients diagnosed with a SIPD and PPD. Method: Participants were 61 young people (15-24 years) admitted to a psychiatric inpatient service with first episode psychosis, reporting substance use in the past month. Diagnosis was determined using the Psychiatric Research Interview for DSM-IV Substance and Mental disorders (PRISM-IV). Measures of clinical (severity of psychotic symptoms, level of insight, history of trauma), substance use (frequency/quantity, severity) and social and occupational functioning were also administered. Results: The PRISM-IV differentially diagnosed 56% of first episode patients with a SIPD and 44% with a PPD. Those with a SIPD had higher rates of substance use and disorders, higher levels of insight, were more likely to have a forensic and trauma history and had more severe hostility and anxious symptoms than those with a PPD. Logistic regression analysis indicated a family history of psychosis, trauma history and current cannabis dependence were the strongest predictors of a SIPD. Almost 80% of diagnostic predictions of a SIPD were accurate using this model. Conclusions: This clinical profile of SIPD could help to facilitate the accurate diagnosis and treatment of SIPD versus PPD in young people with first episode psychosis admitted to an inpatient psychiatric service.

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Substantial research efforts have been expended to deal with the complexity of concurrent systems that is inherent to their analysis, e.g., works that tackle the well-known state space explosion problem. Approaches differ in the classes of properties that they are able to suitably check and this is largely a result of the way they balance the trade-off between analysis time and space employed to describe a concurrent system. One interesting class of properties is concerned with behavioral characteristics. These properties are conveniently expressed in terms of computations, or runs, in concurrent systems. This article introduces the theory of untanglings that exploits a particular representation of a collection of runs in a concurrent system. It is shown that a representative untangling of a bounded concurrent system can be constructed that captures all and only the behavior of the system. Representative untanglings strike a unique balance between time and space, yet provide a single model for the convenient extraction of various behavioral properties. Performance measurements in terms of construction time and size of representative untanglings with respect to the original specifications of concurrent systems, conducted on a collection of models from practice, confirm the scalability of the approach. Finally, this article demonstrates practical benefits of using representative untanglings when checking various behavioral properties of concurrent systems.

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This paper details a team-based feedback approach for reducing resource consumption. The approach uses paper printing within office environments as a case study. It communicates the print usage of each participant’s team rather than the participant’s individual print usage. Feedback is provided weekly via emails and contains normative information, along with eco-metrics and team-based comparative statistics. The approach was empirically evaluated to study the effectiveness of the feedback method. The experiment comprised of 16 people belonging to 4 teams with data on their print usage gathered over 58 weeks, using the first 30-35 weeks as a baseline. The study showed a significant reduction in individual printing with an average of 28%. The experiment confirms the underlying hypothesis that participants are persuaded to reduce their print usage in order to improve the overall printing behaviour of their teams. The research provides clear pathways for future research to qualitatively investigate our findings.

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Retrieving information from Twitter is always challenging due to its large volume, inconsistent writing and noise. Most existing information retrieval (IR) and text mining methods focus on term-based approach, but suffers from the problems of terms variation such as polysemy and synonymy. This problem deteriorates when such methods are applied on Twitter due to the length limit. Over the years, people have held the hypothesis that pattern-based methods should perform better than term-based methods as it provides more context, but limited studies have been conducted to support such hypothesis especially in Twitter. This paper presents an innovative framework to address the issue of performing IR in microblog. The proposed framework discover patterns in tweets as higher level feature to assign weight for low-level features (i.e. terms) based on their distributions in higher level features. We present the experiment results based on TREC11 microblog dataset and shows that our proposed approach significantly outperforms term-based methods Okapi BM25, TF-IDF and pattern based methods, using precision, recall and F measures.

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The INEX 2011 Relevance Feedback track offered a refined approach to the evaluation of Focused Relevance Feedback algorithms through simulated exhaustive user feedback. Run in largely identical fashion to the Relevance Feedback track in INEX 2010[2], we simulated a user-in-the loop by re-using the assessments of ad-hoc retrieval obtained from real users who assess focused ad-hoc retrieval submissions. We present the evaluation methodology, its implementation, and experimental results obtained for four submissions from two participating organisations. As the task and evaluation methods did not change between INEX 2010 and now, explanations of these details from the INEX 2010 version of the track have been repeated verbatim where appropriate.

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This paper reviews electricity consumption feedback literature to explore the potential of electricity feedback to affect residential consumers’ electricity usage patterns. The review highlights a substantial amount of literature covering the debate over the effectiveness of different feedback criteria to residential customer acceptance and overall conservation and peak demand reduction. Researchers studying the effects of feedback on everyday energy use have observed substantial variation in effect size, both within and between studies. Although researchers still continue to question the types of feedback that are most effective in encouraging conservation and peak load reduction, some trends have emerged. These include that feedback be received as quickly as possible to the time of consumption; be related to a standard; be clear and meaningful and where possible both direct and indirect feedback be customised to the customer. In general, the literature finds that feedback can reduce electricity consumption in homes by 5 to 20 per cent, but that significant gaps remain in our knowledge of the effectiveness and cost benefit of feedback.

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In practical cases for active noise control (ANC), the secondary path has usually a time varying behavior. For these cases, an online secondary path modeling method that uses a white noise as a training signal is required to ensure convergence of the system. The modeling accuracy and the convergence rate are increased when a white noise with a larger variance is used. However, the larger variance increases the residual noise, which decreases performance of the system and additionally causes instability problem to feedback structures. A sudden change in the secondary path leads to divergence of the online secondary path modeling filter. To overcome these problems, this paper proposes a new approach for online secondary path modeling in feedback ANC systems. The proposed algorithm uses the advantages of white noise with larger variance to model the secondary path, but the injection is stopped at the optimum point to increase performance of the algorithm and to prevent the instability effect of the white noise. In this approach, instead of continuous injection of the white noise, a sudden change in secondary path during the operation makes the algorithm to reactivate injection of the white noise to correct the secondary path estimation. In addition, the proposed method models the secondary path without the need of using off-line estimation of the secondary path. Considering the above features increases the convergence rate and modeling accuracy, which results in a high system performance. Computer simulation results shown in this paper indicate effectiveness of the proposed method.

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Feedback, both formative and summative, enables students to reflect on their understandings and to restructure their thinking to develop their capabilities. It can also encourage positive motivation and help boost self-esteem. Online multiple choice questions can be an efficient and effective means of providing timely formative feedback. At the same time, locating learning in a narrative environment can facilitate engaging and effective learning experiences. Narratives can help learners to navigate through information and support cognitive and imaginative engagement. This article will discuss The 00 Files, an online suite of modules containing multiple choice questions situated in the narrative of a fictional law firm. It notes student responses to the program and discusses lessons that may be learnt from its development which may be of assistance to academics considering the development of similar programs for their courses.

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This paper proposes a new method for online secondary path modeling in feedback active noise control (ANC) systems. In practical cases, the secondary path is usually time varying. For these cases, online modeling of secondary path is required to ensure convergence of the system. In literature the secondary path estimation is usually performed offline, prior to online modeling, where in the proposed system there is no need for using offline estimation. The proposed method consists of two steps: a noise controller which is based on an FxLMS algorithm, and a variable step size (VSS) LMS algorithm which is used to adapt the modeling filter with the secondary path. In order to increase performance of the algorithm in a faster convergence and accurate performance, we stop the VSS-LMS algorithm at the optimum point. The results of computer simulation shown in this paper indicate effectiveness of the proposed method.