32 resultados para Training analysis


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This article examines the negotiation of face in post observation feedback conferences on an initial teacher training programme. The conferences were held in groups with one trainer and up to four trainees and followed a set of generic norms. These norms include the right to offer advice and to criticise, speech acts which are often considered to be face threatening in more normal contexts. However, as the data analysis shows, participants also interact in ways that challenge the generic norms, some of which might be considered more conventionally face attacking. The article argues that face should be analysed at the level of interaction (Haugh and Bargiela-Chiappini, 2010) and that situated and contextual detail is relevant to its analysis. It suggests that linguistic ethnography, which 'marries' (Wetherell, 2007) linguistics and ethnography, provides a useful theoretical framework for doing so. To this end the study draws on real-life talk-in-interaction (from transcribed recordings), the participants' perspectives (from focus groups and interviews) and situated detail (from fieldnotes) to produce a contextualised and nuanced analysis. © 2011 Elsevier B.V.

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The thesis is concerned with cross-cultural distance learning in two countries: Great Britain and France. Taking the example of in-house sales training, it argues that it is possible to develop courses for use in two or more countries of differing culture and language. Two courses were developed by the researcher. Both were essentially print-based distance-learning courses designed to help salespeople achieve a better understanding of their customers. One used a quantitative, the other qualitative approach. One considered the concept of the return on investment and the other, for which a video support was also developed, considered the analysis of a customer's needs. Part 1 of the thesis considers differences in the training context between France and Britain followed by a review of the learning process with reference to distance learning. Part 2 looks at the choice of training medium course design and evaluation and sets out the methodology adopted, including problems encountered in this type of fieldwork. Part 3 analyses the data and draws conclusions from the findings, before offering a series of guidelines for those concerned with the development of cross-cultural in-house training courses. The results of the field tests on the two courses were analysed in relation to the socio-cultural, educational and experiential background of the learners as well as their preferred learning styles. The thesis argues that it is possible to develop effective in-house sales training courses to be used in two cultures and identifies key considerations which need to be taken into account when carrying out this type of work.

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The thesis examines Kuhn's (1962, 1970) concept of paradigm, assesses how it is employed for mapping intellectual terrain in the social sciences, and evaluates it's use in research based on multiple theory positions. In so doing it rejects both the theses of total paradigm 'incommensurability' (Kuhn, 1962), and also of liberal 'translation' (Popper, 1970), in favour of a middle ground through the 'language-game of everyday life' (Wittgenstein, 1953). The thesis ultimately argues for the possibility of being 'trained-into' new paradigms, given the premise that 'unorganised experience cannot order perception' (Phillips, 1977). In conducting multiple paradigm research the analysis uses the Burrell and Morgan (1979) model for examining the work organisation of a large provincial fire Service. This analysis accounts for firstly, a 'functionalist' assessment of work design, demonstrating inter alia the decrease in reported motivation with length of service; secondly, an 'interpretive' portrayal of the daily accomplishment of task routines, highlighting the discretionary and negotiated nature of the day's events; thirdly, a 'radical humanist' analysis of workplace ideology, demonstrating the hegemonic role of officer training practices; and finally, a 'radical structuralist' description of the labour process, focusing on the establishment of a 'normal working day'. Although the argument is made for the possibility of conducting multiple paradigm research, the conclusion stresses the many institutional pressures serving to offset development.

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A re-examination of fundamental concepts and a formal structuring of the waveform analysis problem is presented in Part I. eg. the nature of frequency is examined and a novel alternative to the classical methods of detection proposed and implemented which has the advantage of speed and independence from amplitude. Waveform analysis provides the link between Parts I and II. Part II is devoted to Human Factors and the Adaptive Task Technique. The Historical, Technical and Intellectual development of the technique is traced in a review which examines the evidence of its advantages relative to non-adaptive fixed task methods of training, skill assessment and man-machine optimisation. A second review examines research evidence on the effect of vibration on manual control ability. Findings are presented in terms of percentage increment or decrement in performance relative to performance without vibration in the range 0-0.6Rms'g'. Primary task performance was found to vary by as much as 90% between tasks at the same Rms'g'. Differences in task difficulty accounted for this difference. Within tasks vibration-added-difficulty accounted for the effects of vibration intensity. Secondary tasks were found to be largely insensitive to vibration except secondaries which involved fine manual adjustment of minor controls. Three experiments are reported next in which an adaptive technique was used to measure the % task difficulty added by vertical random and sinusoidal vibration to a 'Critical Compensatory Tracking task. At vibration intensities between 0 - 0.09 Rms 'g' it was found that random vibration added (24.5 x Rms'g')/7.4 x 100% to the difficulty of the control task. An equivalence relationship between Random and Sinusoidal vibration effects was established based upon added task difficulty. Waveform Analyses which were applied to the experimental data served to validate Phase Plane analysis and uncovered the development of a control and possibly a vibration isolation strategy. The submission ends with an appraisal of subjects mentioned in the thesis title.

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Despite the increasing popularity of research on intercultural preparation and its effectiveness, research on training for inpatriates has not been developed with the same level of rigour as research on training for expatriates. Furthermore, research on intercultural training hardly ever includes the aspect of preparing for the corporate culture of a company. For expatriates coming from headquarters’ national culture and equipped with a good knowledge of headquarters’ corporate culture, it might be sufficient to address only the national culture of the location abroad. But can the same be said for inpatriates coming from a foreign subsidiary? Therefore the qualitative research of my thesis was aimed at finding out if intercultural training programmes that address only the national culture of the host country are sufficient to prepare inpatriates for working at headquarters. A case study using a German multinational company has been conducted in order to find out what kind of problems and irritations inpatriates at the company’s headquarters perceive at work. In order to determine whether the findings are related to the national or the corporate culture, Hall’s and Hofstede’s approaches to culture were used. The interview analysis produced the following conclusion: Although the researched company promotes standardised worldwide corporate guidelines, there are many differences between headquarters and subsidiaries regarding the interpretation and realisation of these guidelines. These differences cause irritation, confusion and problems for the inpatriates. Therefore an effective intercultural preparation for inpatriates should be tailor-made and take into account the aspect of corporate culture, as well as the specific roles and functions of inpatriates.

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This study investigates the discursive patterns of interactions between police interviewers and women reporting rape in significant witness interviews. Data in the form of video recorded interviews were obtained from a UK police force for the purposes of this study. The data are analysed using a multi-method approach, incorporating tools from micro-sociology, Conversation Analysis and Discursive Psychology, to reveal patterns of interactional control, negotiation, and interpretation. The study adopts a critical approach, which is to say that as well as describing discursive patterns, it explains them in light of the discourse processes involved in the production and consumption of police interview talk, and comments on the relationship between these discourse processes and the social context in which they occur. A central focus of the study is how interviewers draw on particular interactional resources to shape interviewees? accounts in particular ways, and this is discussed in relation to the institutional role of the significant witness interview. The discussion is also extended to the ways in which mainstream rape ideology is both reflected in, and maintained by, the discursive choices of participants. The findings of this study indicate that there are a number of issues to be addressed in terms of the training currently offered to officers at Level 2 of the Professionalising Investigation Programme (PIP) (NPIA, 2009) who intend to conduct significant witness interviews. Furthermore, a need is identified to bring the linguistic and discursive processes of negotiation and transformation identified by the study to the attention of the justice system as a whole. This is a particularly pressing need in light of judicial reluctance to replace written witness statements, the current „end product? of significant witness interviews, with the video recorded interview in place of direct examination in cases of rape.

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The subject of this research is interaction and language use in an institutional context, the teacher training classroom. Trainer talk is an interactional accomplishment and the research question is: what structures of talk-in-interaction characterise trainer talk in this institutional setting? While there has been research into other kinds of classroom and into other kinds of institutional talk, this study is the first on trainer discourse. The study takes a Conversation Analysis approach to studying institutional interaction and aims to identify the main structures of sequential organization that characterize teacher trainer talk as well as the tasks and identities that are accomplished in it. The research identifies three main interactional contexts in which trainer talk is done: expository, exploratory and experiential. It describes the main characteristics of each and how they relate to each other. Expository sequences are the predominant interactional contexts for trainer talk. But the research findings show that these contexts are flexible and open to the embedding of the other two contexts. All three contexts contribute to the main institutional goal of teaching teachers how to teach. Trainer identity is related to the different sequential contexts. Three main forms of identity in interaction are evidenced in the interactional contexts: the trainer as trainer, the trainer as teacher and the trainer as colleague. Each of them play an important role in teacher trainer pedagogy. The main features of trainer talk as a form of institutional talk are characterised by the following interactional properties: 1. Professional discourse is both the vehicle and object of instruction - the articulation of reflection on experience. 2. There is a reflexive relationship between pedagogy and interaction. 3. The professional discourse that is produced by trainees is not evaluated by trainers but, rather, reformulated to give it relevant precision in terms of accuracy and appropriacy.

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Sentiment analysis over Twitter offer organisations a fast and effective way to monitor the publics' feelings towards their brand, business, directors, etc. A wide range of features and methods for training sentiment classifiers for Twitter datasets have been researched in recent years with varying results. In this paper, we introduce a novel approach of adding semantics as additional features into the training set for sentiment analysis. For each extracted entity (e.g. iPhone) from tweets, we add its semantic concept (e.g. Apple product) as an additional feature, and measure the correlation of the representative concept with negative/positive sentiment. We apply this approach to predict sentiment for three different Twitter datasets. Our results show an average increase of F harmonic accuracy score for identifying both negative and positive sentiment of around 6.5% and 4.8% over the baselines of unigrams and part-of-speech features respectively. We also compare against an approach based on sentiment-bearing topic analysis, and find that semantic features produce better Recall and F score when classifying negative sentiment, and better Precision with lower Recall and F score in positive sentiment classification.

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Sentiment analysis or opinion mining aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text. This paper proposes a novel probabilistic modeling framework based on Latent Dirichlet Allocation (LDA), called joint sentiment/topic model (JST), which detects sentiment and topic simultaneously from text. Unlike other machine learning approaches to sentiment classification which often require labeled corpora for classifier training, the proposed JST model is fully unsupervised. The model has been evaluated on the movie review dataset to classify the review sentiment polarity and minimum prior information have also been explored to further improve the sentiment classification accuracy. Preliminary experiments have shown promising results achieved by JST.

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The appraisal and relative performance evaluation of nurses are very important and beneficial for both nurses and employers in an era of clinical governance, increased accountability and high standards of health care services. They enhance and consolidate the knowledge and practical skills of nurses by identification of training and career development plans as well as improvement in health care quality services, increase in job satisfaction and use of cost-effective resources. In this paper, a data envelopment analysis (DEA) model is proposed for the appraisal and relative performance evaluation of nurses. The model is validated on thirty-two nurses working at an Intensive Care Unit (ICU) at one of the most recognized hospitals in Lebanon. The DEA was able to classify nurses into efficient and inefficient ones. The set of efficient nurses was used to establish an internal best practice benchmark to project career development plans for improving the performance of other inefficient nurses. The DEA result confirmed the ranking of some nurses and highlighted injustice in other cases that were produced by the currently practiced appraisal system. Further, the DEA model is shown to be an effective talent management and motivational tool as it can provide clear managerial plans related to promoting, training and development activities from the perspective of nurses, hence increasing their satisfaction, motivation and acceptance of appraisal results. Due to such features, the model is currently being considered for implementation at ICU. Finally, the ratio of the number DEA units to the number of input/output measures is revisited with new suggested values on its upper and lower limits depending on the type of DEA models and the desired number of efficient units from a managerial perspective.

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The port industry is facing a dramatic wave of changes that have transformed the structure of the industry. Modern seaports are increasingly shifting from a “hardware-based” approach towards “knowhow intensive” configuration. In this context knowledge resources, learning processes and training initiatives increasingly represent key elements to guarantee the quality of service supplied and hence the competitiveness of modern seaport communities. This paper describes the learning needs analysis conducted amongst key port community actors in three ports in the south east of Ireland during 2005 in the context of the I-Sea.Net project. It goes on to describe the learning requirements report and the training design carried out based on this analysis.

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Aims: To establish the sensitivity and reliability of objective image analysis in direct comparison with subjective grading of bulbar hyperaemia. Methods: Images of the same eyes were captured with a range of bulbar hyperaemia caused by vasodilation. The progression was recorded and 45 images extracted. The images were objectively analysed on 14 occasions using previously validated edge-detection and colour-extraction techniques. They were also graded by 14 eye-care practitioners (ECPs) and 14 non-clinicians (NCb) using the Efron scale. Six ECPs repeated the grading on three separate occasions Results: Subjective grading was only able to differentiate images with differences in grade of 0.70-1.03 Efron units (sensitivity of 0.30-0.53), compared to 0,02-0.09 Efron units with objective techniques (sensitivity of 0.94-0.99). Significant differences were found between ECPs and individual repeats were also inconsistent (p<0.001). Objective analysis was 16x more reliable than subjective analysis. The NCLs used wider ranges of the scale but were more variable than ECPs, implying that training may have an effect on grading. Conclusions: Objective analysis may offer a new gold standard in anterior ocular examination, and should be developed further as a clinical research tool to allow more highly powered analysis, and to enhance the clinical monitoring of anterior eye disease.

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In recent years, learning word vector representations has attracted much interest in Natural Language Processing. Word representations or embeddings learned using unsupervised methods help addressing the problem of traditional bag-of-word approaches which fail to capture contextual semantics. In this paper we go beyond the vector representations at the word level and propose a novel framework that learns higher-level feature representations of n-grams, phrases and sentences using a deep neural network built from stacked Convolutional Restricted Boltzmann Machines (CRBMs). These representations have been shown to map syntactically and semantically related n-grams to closeby locations in the hidden feature space. We have experimented to additionally incorporate these higher-level features into supervised classifier training for two sentiment analysis tasks: subjectivity classification and sentiment classification. Our results have demonstrated the success of our proposed framework with 4% improvement in accuracy observed for subjectivity classification and improved the results achieved for sentiment classification over models trained without our higher level features.

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Abstract A new LIBS quantitative analysis method based on analytical line adaptive selection and Relevance Vector Machine (RVM) regression model is proposed. First, a scheme of adaptively selecting analytical line is put forward in order to overcome the drawback of high dependency on a priori knowledge. The candidate analytical lines are automatically selected based on the built-in characteristics of spectral lines, such as spectral intensity, wavelength and width at half height. The analytical lines which will be used as input variables of regression model are determined adaptively according to the samples for both training and testing. Second, an LIBS quantitative analysis method based on RVM is presented. The intensities of analytical lines and the elemental concentrations of certified standard samples are used to train the RVM regression model. The predicted elemental concentration analysis results will be given with a form of confidence interval of probabilistic distribution, which is helpful for evaluating the uncertainness contained in the measured spectra. Chromium concentration analysis experiments of 23 certified standard high-alloy steel samples have been carried out. The multiple correlation coefficient of the prediction was up to 98.85%, and the average relative error of the prediction was 4.01%. The experiment results showed that the proposed LIBS quantitative analysis method achieved better prediction accuracy and better modeling robustness compared with the methods based on partial least squares regression, artificial neural network and standard support vector machine.

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The relationship between sleep apnoea–hypopnoea syndrome (SAHS) severity and the regularity of nocturnal oxygen saturation (SaO2) recordings was analysed. Three different methods were proposed to quantify regularity: approximate entropy (AEn), sample entropy (SEn) and kernel entropy (KEn). A total of 240 subjects suspected of suffering from SAHS took part in the study. They were randomly divided into a training set (96 subjects) and a test set (144 subjects) for the adjustment and assessment of the proposed methods, respectively. According to the measurements provided by AEn, SEn and KEn, higher irregularity of oximetry signals is associated with SAHS-positive patients. Receiver operating characteristic (ROC) and Pearson correlation analyses showed that KEn was the most reliable predictor of SAHS. It provided an area under the ROC curve of 0.91 in two-class classification of subjects as SAHS-negative or SAHS-positive. Moreover, KEn measurements from oximetry data exhibited a linear dependence on the apnoea–hypopnoea index, as shown by a correlation coefficient of 0.87. Therefore, these measurements could be used for the development of simplified diagnostic techniques in order to reduce the demand for polysomnographies. Furthermore, KEn represents a convincing alternative to AEn and SEn for the diagnostic analysis of noisy biomedical signals.