993 resultados para online counselling


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The OECD (2006 Starting Strong II: Early Childhood Education and Care. OECD Publishing: Paris) envisions early childhood education and care settings as meeting places for diverse social groups; places that build social capital. This vision was assessed in a comparison of three preschools types: full-fee paying, subsidised-fee and publicly funded. The social composition within each was examined and the connectedness of the children (n = 472) who attended compared. Publicly funded preschools had more socially diverse populations. The quantity of social connectedness did not differ but children in publicly funded preschools described higher quality social relationships. Not all preschool settings are socially diverse but, where they are, the quality of relationships is highest.

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Objective Substance-related expectancies are associated with substance use and post-substance use thoughts, feelings and behaviours. The expectancies held by specific cultural or sub-cultural groups have rarely been investigated. This research maps expectancies specific to gay and other men who have sex with men (MSM) and their relationship with substance patterns and behaviours following use, including sexual practices (e.g., unprotected anal intercourse). This study describes the development of a measure of such beliefs for cannabis, the Cannabis Expectancy Questionnaire for Men who have Sex with Men (CEQ-MSM). Method Items selected through a focus group and interviews were piloted on 180 self-identified gay or other MSM via an online questionnaire. Results Factor analysis revealed six distinct substance reinforcement domains (“Enhanced sexual experience”, “Sexual negotiation”, “Cognitive impairment”, “Social and emotional facilitation”, “Enhanced sexual desire”, and “Sexual inhibition”). The scale was associated with consumption patterns of cannabis, and in a crucial test of discriminant validity not with the consumption of alcohol or stimulants. Conclusions The CEQ-MSM represents a reliable and valid measure of outcome expectancies, related to cannabis among MSM. Future applications of the CEQ-MSM in health promotion, clinical settings and research may contribute to reducing harm associated with substance use among MSM, including HIV transmission.

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This chapter sets out to identify related issues surrounding the use of Information and Computer Technology (ICT) in developing relationships between local food producers and consumers (both individuals and businesses). Three surveys were conducted in South- East Wales to consider the overlapping issues. The first concerned the role of ICT in relationships between farmers’ market (FMs) vendors and their traditional customers. The second survey examined potential new markets for farmers in the propensity of restaurants and hotels to buy locally, the types and sources of purchases made and the modes of advertising of these businesses. The final survey focused on the potential to expand local web- based selling of farmers’ produce in the future, by examining the potential market of high ICT- use small hotels. Despite the development of tailored ICT facilities, farmers’ market vendors and current individual customers are antipathetic to them. In addition, whilst there is a desire for more local produce particularly amongst independent local restaurants and hotels, this has not been capitalised upon and there is much work to be done even amongst high ICT-use small hotels, to expand the range and scope of farmers’ markets. This raises the need for creation and utilisation of enhanced logistics, payment and marketing management capacity available through a web- based presence, linked to promotion of FMs in business- to- business (B2B) links with local restaurants and hotels. This linked quantitative research highlights the potential value in substantial development of both web portals and supporting logistics to exploit this potential in the future.

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This thesis investigates the place of online moderation in supporting teachers to work in a system of standards-based assessment. The participants of the study were fifty middle school teachers who met online with the aim of developing consistency in their judgement decisions. Data were gathered through observation of the online meetings, interviews, surveys and the collection of artefacts. The data were viewed and analysed through sociocultural theories of learning and sociocultural theories of technology, and demonstrates how utilising these theories can add depth to understanding the added complexity of developing shared meaning of standards in an online context. The findings contribute to current understanding of standards-based assessment by examining the social moderation process as it acts to increase the reliability of judgements that are made within a standards framework. Specifically, the study investigates the opportunities afforded by conducting social moderation practices in a synchronous online context. The study explicates how the technology affects the negotiation of judgements and the development of shared meanings of assessment standards, while demonstrating how involvement in online moderation discussions can support teachers to become and belong within a practice of standards-based assessment. This research responds to a growing international interest in standards-based assessment and the use of social moderation to develop consistency in judgement decisions. Online moderation is a new practice to address these concerns on a systemic basis.

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Despite the challenges that giftedness can add to self-formation during early adolescence, gifted young adolescents seldom are asked about their lives outside of counselling and educational contexts. The study considers the complexities that face gifted young adolescents in the process of self-discovery and self-representation, thereby building a case for seeking their own viewpoints. A guiding assumption for the study was that gifted young adolescents may respond positively to the opportunity to share their own perspectives and their own versions of “who they are”. The theoretical underpinnings for this study drew from Dialogical Self Theory. The study resides within an interactive view of self as a dynamic construction rather than a static state, where “who we are” is formed in everyday exchanges with self and others. Self-making as a process among gifted young adolescents is presented as an interactive network of “I” voices interpreted to reflect internal and external dialogue. In this way, self is understood within dialogical concepts of voices as multiple expressions. The study invited twelve gifted young adolescents to write freely about themselves over a six month period in an email journal project. Participants were recruited online and by word-of-mouth and they were able to negotiate their own levels of involvement. Access to the lives of individual young adolescents was sought in an out-of-school setting using narrative methods of personal writing in the form of journals sent as emails to the researcher. The role of the researcher was to act as a supportive listener who responded to participant-led emails and thereby facilitated the process of authoring that occurred across the data-gathering phase. The listening process involved responses that were affirming and designed to build trust. Data in the form of email texts were analysed using a close listening method that uncovered patterns of voices that were explicitly or subtly expressed by participants. The interpretation of voices highlighted the tensions and contradictions involved in the process of participants forming a “self” that emerged as multiple “I” voices. There were three key findings of the study. First, the gifted young adolescent participants each constructed a self around four key voices of Author, Achiever, Resistor/Co-operator and Self-Innovator. These voices were dialogical selfconstructions that showed multiplicity as a normal way of being. Second, the selfmaking processes of the gifted young adolescent participants were guided by a hierarchy of voices that were directed through self-awareness. Third, authoring in association with a responsive adult listener emerged as a dialogic space for promoting self-awareness and a language of self-expression among gifted young adolescents. The findings of the study contribute to knowledge about gifted young adolescents by presenting their own versions of “who” they are, perspectives that might differ from mainstream perceptions. Participants were shown to have highly diverse, complex and individual expressions that have implications for how well they are understood and supported by others. The use of email journals helped to create a synergy for self-disclosure and a safe space for self-expression where participants’ abilities to be themselves were encouraged. Increased self-awareness and selfknowledge among gifted young adolescents is vital to their self-formation and their management of self and others’ expectations. This study makes an original contribution to the field of self-study by highlighting the processes and complexities of young adolescents’ self-constructions. Through the innovative use of narrative methods and an inter-disciplinary approach, the voices of gifted young adolescents were privileged. At a practical level, the study can inform educators, policy-makers, parents and all those who seek to contribute to the well-being of gifted young adolescents.

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Newspapers and, if to a lesser extent as yet, linear broadcast news providers on TV and radio are in the process of being replaced as the dominant carrier media of journalism by an emerging network of online outlets.

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The advent of e-learning has seen the adaptation and use of a plethora of educational techniques. Of these, online discussion forums have met with success and been used widely in both undergraduate and postgraduate education. The authors of this paper, having previously used online discussion forums in the postgraduate arena with success, adopted this approach for the design and subsequent delivery of a learning and teaching subject. This learning and teaching subject, however, was part of an international collaboration and designed for nurse academics in another country – Vietnam. With the nursing curriculum in Vietnam currently moving to adopt a competency based approach, two learning and teaching subjects were designed by an Australian university for Vietnamese nurse academics. Subject materials constituted a DVD which arrived by post and access to an online platform. Assessment for the subject included (but was not limited to) mandatory participation in online discussion with the other nurse academics enrolled in the subject. The purpose behind the online discussion was to generate discourse between the Vietnamese nurse academics located across Vietnam. Consequently the online discussions occurred in both Vietnamese and English; the Australian academic moderating the discussion did so in Australia with a Vietnamese translator. For the Australian University delivering this subject the difference between this and past online discussions were twofold: delivery was in a foreign language; and the teaching experience of the Vietnamese nurse teachers was mixed and frequently very limited. This paper will provide a discussion addressing the design of an online learning environment for foreign correspondents, the resources and translation required to maximise the success of the online discussion, the lessons learnt and consequent changes made, as well as the rationale of delivering complex content in a foreign language. While specifically addressing the first iteration of the first learning module designed, this paper will also address subsequent changes made for the second iteration of the first module and comment on their success. While a translator is clearly a key component of success, the elements of simplicity and clarity in hand with supportive online moderation must not be overlooked.

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In many prediction problems, including those that arise in computer security and computational finance, the process generating the data is best modelled as an adversary with whom the predictor competes. Even decision problems that are not inherently adversarial can be usefully modeled in this way, since the assumptions are sufficiently weak that effective prediction strategies for adversarial settings are very widely applicable.

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In many prediction problems, including those that arise in computer security and computational finance, the process generating the data is best modelled as an adversary with whom the predictor competes. Even decision problems that are not inherently adversarial can be usefully modeled in this way, since the assumptions are sufficiently weak that effective prediction strategies for adversarial settings are very widely applicable.

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We consider the problem of choosing, sequentially, a map which assigns elements of a set A to a few elements of a set B. On each round, the algorithm suffers some cost associated with the chosen assignment, and the goal is to minimize the cumulative loss of these choices relative to the best map on the entire sequence. Even though the offline problem of finding the best map is provably hard, we show that there is an equivalent online approximation algorithm, Randomized Map Prediction (RMP), that is efficient and performs nearly as well. While drawing upon results from the "Online Prediction with Expert Advice" setting, we show how RMP can be utilized as an online approach to several standard batch problems. We apply RMP to online clustering as well as online feature selection and, surprisingly, RMP often outperforms the standard batch algorithms on these problems.

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Online learning algorithms have recently risen to prominence due to their strong theoretical guarantees and an increasing number of practical applications for large-scale data analysis problems. In this paper, we analyze a class of online learning algorithms based on fixed potentials and nonlinearized losses, which yields algorithms with implicit update rules. We show how to efficiently compute these updates, and we prove regret bounds for the algorithms. We apply our formulation to several special cases where our approach has benefits over existing online learning methods. In particular, we provide improved algorithms and bounds for the online metric learning problem, and show improved robustness for online linear prediction problems. Results over a variety of data sets demonstrate the advantages of our framework.

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A number of learning problems can be cast as an Online Convex Game: on each round, a learner makes a prediction x from a convex set, the environment plays a loss function f, and the learner’s long-term goal is to minimize regret. Algorithms have been proposed by Zinkevich, when f is assumed to be convex, and Hazan et al., when f is assumed to be strongly convex, that have provably low regret. We consider these two settings and analyze such games from a minimax perspective, proving minimax strategies and lower bounds in each case. These results prove that the existing algorithms are essentially optimal.

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In this paper we examine the problem of prediction with expert advice in a setup where the learner is presented with a sequence of examples coming from different tasks. In order for the learner to be able to benefit from performing multiple tasks simultaneously, we make assumptions of task relatedness by constraining the comparator to use a lesser number of best experts than the number of tasks. We show how this corresponds naturally to learning under spectral or structural matrix constraints, and propose regularization techniques to enforce the constraints. The regularization techniques proposed here are interesting in their own right and multitask learning is just one application for the ideas. A theoretical analysis of one such regularizer is performed, and a regret bound that shows benefits of this setup is reported.

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We demonstrate a modification of the algorithm of Dani et al for the online linear optimization problem in the bandit setting, which allows us to achieve an O( \sqrt{T ln T} ) regret bound in high probability against an adaptive adversary, as opposed to the in expectation result against an oblivious adversary of Dani et al. We obtain the same dependence on the dimension as that exhibited by Dani et al. The results of this paper rest firmly on those of Dani et al and the remarkable technique of Auer et al for obtaining high-probability bounds via optimistic estimates. This paper answers an open question: it eliminates the gap between the high-probability bounds obtained in the full-information vs bandit settings.

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We study the rates of growth of the regret in online convex optimization. First, we show that a simple extension of the algorithm of Hazan et al eliminates the need for a priori knowledge of the lower bound on the second derivatives of the observed functions. We then provide an algorithm, Adaptive Online Gradient Descent, which interpolates between the results of Zinkevich for linear functions and of Hazan et al for strongly convex functions, achieving intermediate rates between [square root T] and [log T]. Furthermore, we show strong optimality of the algorithm. Finally, we provide an extension of our results to general norms.