845 resultados para Multiple subspace learning


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In recent years, the Web 2.0 has provided considerable facilities for people to create, share and exchange information and ideas. Upon this, the user generated content, such as reviews, has exploded. Such data provide a rich source to exploit in order to identify the information associated with specific reviewed items. Opinion mining has been widely used to identify the significant features of items (e.g., cameras) based upon user reviews. Feature extraction is the most critical step to identify useful information from texts. Most existing approaches only find individual features about a product without revealing the structural relationships between the features which usually exist. In this paper, we propose an approach to extract features and feature relationships, represented as a tree structure called feature taxonomy, based on frequent patterns and associations between patterns derived from user reviews. The generated feature taxonomy profiles the product at multiple levels and provides more detailed information about the product. Our experiment results based on some popularly used review datasets show that our proposed approach is able to capture the product features and relations effectively.

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Emotions are inherently social, and are central to learning, online interaction and literacy practices (Shen, Wang, & Shen, 2009). Demonstrating the dynamic sociality of literacy practice, we used e-motion diaries or web logs to explore the emotional states of pre-service high school teachers’ experiences of online learning activities. This is because the methods of communication used by university educators in online learning and writing environments play an important role in fulfilling students’ need for social interaction and inclusion (McInnerney & Roberts, 2004). Feelings of isolation and frustration are common emotions experienced by students in many online learning environments, and are associated with the success or failure of online interactions and learning (Su, et al., 2005). The purpose of the study was to answer the research question: What are the trajectories of pre-service teachers’ emotional states during online learning experiences? This is important because emotions are central to learning, and the current trend toward Massive Open Online Courses (MOOCs) needs research about students’ emotional connections in online learning environments (Kop, 2011). The project was conducted with a graduate class of 64 high school science pre-service teachers in Science Education Curriculum Studies in a large Australian university, including males and females from a variety of cultural backgrounds, aged 22-55 years. Online activities involved the students watching a series of streamed live lectures for the first 5 weeks providing a varied set of learning experiences, such as viewing science demonstrations (e.g., modeling the use of discrepant events). Each week, students provided feedback on learning by writing and posting an e-motion diary or web log about their emotional response. Students answered the question: What emotions did you experience during this learning experience? The descriptive data set included 284 online posts, with students contributing multiple entries. Linguistic appraisal theory, following Martin and White (2005), was used to regroup the 22 different discrete emotions reported by students into the six main affect groups – three positive and three negative: unhappiness/happiness, insecurity/security, and dissatisfaction/satisfaction. The findings demonstrated that the pre-service teachers’ emotional responses to the streamed lectures tended towards happiness, security, and satisfaction within the typology of affect groups – un/happiness, in/security, and dis/satisfaction. Fewer students reported that the streamed lectures triggered negative feelings of frustration, powerlessness, and inadequacy, and when this occurred, it often pertained to expectations of themselves in the forthcoming field experience in classrooms. Exceptions to this pattern of responses occurred in relation to the fifth streamed lecture presented in a non-interactive slideshow format that compressed a large amount of content. Many students responded to the content of the lecture rather than providing their emotional responses to this lecture, and one student felt “completely disengaged”. The social practice of online writing as blogs enabled the students to articulate their emotions. The findings primarily contribute new understanding about students' wide range of differing emotional states, both positive and negative, experienced in response to streamed live lectures and other learning activities in higher education external coursework. The is important because the majority of previous studies have focused on particular negative emotions, such as anxiety in test taking. The research also highlights the potentials of appraisal theory for studying human emotions in online learning and writing.

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Research shows that approximately half of creative practitioners operate as embedded creatives by securing gainful employment within organisations located in the field beyond their core discipline. This foregrounds the significance of having the skills necessary to successfully cross the disciplinary boundaries in order to negotiate a professional role. The multiple implications of such reframing for emerging creative practitioners who navigate uncertain professional boundaries include developing a skill of identifying and successfully targeting the shifting professional and industry coordinates while remaining responsive to changes. A further implication involves creative practitioners engaging in a continuous cycle of re-negotiation of their professional identity making the management of multiple professional selves - along with creating and recreating a meaningful frame of references such as the language around their emerging practice - a necessary skill. This chapter presents a case study of a set of Work Integrated Learning subjects designed to develop in creative industries practitioners the skills to manage their emerging professional identities in response to the shifts in the professional world.

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The use of Wireless Sensor Networks (WSNs) for vibration-based Structural Health Monitoring (SHM) has become a promising approach due to many advantages such as low cost, fast and flexible deployment. However, inherent technical issues such as data asynchronicity and data loss have prevented these distinct systems from being extensively used. Recently, several SHM-oriented WSNs have been proposed and believed to be able to overcome a large number of technical uncertainties. Nevertheless, there is limited research verifying the applicability of those WSNs with respect to demanding SHM applications like modal analysis and damage identification. Based on a brief review, this paper first reveals that Data Synchronization Error (DSE) is the most inherent factor amongst uncertainties of SHM-oriented WSNs. Effects of this factor are then investigated on outcomes and performance of the most robust Output-only Modal Analysis (OMA) techniques when merging data from multiple sensor setups. The two OMA families selected for this investigation are Frequency Domain Decomposition (FDD) and data-driven Stochastic Subspace Identification (SSI-data) due to the fact that they both have been widely applied in the past decade. Accelerations collected by a wired sensory system on a large-scale laboratory bridge model are initially used as benchmark data after being added with a certain level of noise to account for the higher presence of this factor in SHM-oriented WSNs. From this source, a large number of simulations have been made to generate multiple DSE-corrupted datasets to facilitate statistical analyses. The results of this study show the robustness of FDD and the precautions needed for SSI-data family when dealing with DSE at a relaxed level. Finally, the combination of preferred OMA techniques and the use of the channel projection for the time-domain OMA technique to cope with DSE are recommended.

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Brain decoding of functional Magnetic Resonance Imaging data is a pattern analysis task that links brain activity patterns to the experimental conditions. Classifiers predict the neural states from the spatial and temporal pattern of brain activity extracted from multiple voxels in the functional images in a certain period of time. The prediction results offer insight into the nature of neural representations and cognitive mechanisms and the classification accuracy determines our confidence in understanding the relationship between brain activity and stimuli. In this paper, we compared the efficacy of three machine learning algorithms: neural network, support vector machines, and conditional random field to decode the visual stimuli or neural cognitive states from functional Magnetic Resonance data. Leave-one-out cross validation was performed to quantify the generalization accuracy of each algorithm on unseen data. The results indicated support vector machine and conditional random field have comparable performance and the potential of the latter is worthy of further investigation.

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The critical role that family plays in Chinese Heritage Language learning has gained increasing attention from psychological, political and sociological scholarship. Guided by Bourdieu’s notion of ‘habitus’, our mixed methods sociological study firstly addresses the need for quantitative evidence on the relationship between family support and Chinese Heritage Language proficiency through a survey of 230 young Chinese Australians; and then explores the dynamics of family support of Chinese Heritage Language learning through multiple interviews with five participants. The interview data demonstrate ongoing intergenerational reproduction of Chinese Heritage Language through various forms of family inculcation. Learners’ transition from resistance to commitment is a focus of the analysis. Extant research struggles to theorise the reasons behind this transition. We offer a Bourdieusian explanation that construes the transition as ‘habitus realisation’. Our study has implications for Chinese Heritage Language researchers, Chinese immigrant parents and Chinese teachers.

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This chapter discusses using phenomenography to study information experience. Phenomenographers aim to investigate people’s experiences of the world around them, which is comprised of the interrelationship between an individual and a phenomenon they are focusing on. Phenomenography has been identified as a research approach suited to the study of information experience. Phenomenographic research investigating experiences of using information in different contexts has led to the development of informed learning, which is an approach to information literacy that emphasizes learning as an outcome of using information. Recent research focusing on information experience has been referred to as informed learning research. The preliminary findings from a current informed learning study illustrate the educative benefits of researching information experience. This study investigates a classroom lesson, in which a teacher outlines an assignment that requires the students to understand a language and gender topic by investigating the evolution of research on the topic. The lesson is experienced in multiple ways by the students and the analysis suggests a way of enhancing the lesson to enable more students to experience it in the way intended by the teacher.

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Lave and Wenger’s legitimate peripheral participation is an important aspect of online learning environments. It is common for teachers to scaffold varying levels of online participation in Web 2.0 contexts, such as online discussion forums and blogs. This study argues that legitimate peripheral participation needs to be redefined in response to students’ decentralised multiple interactions and non-linear engagement in hyperlinked learning environments. The study examines students’ levels of participation in online learning through theories of interactivity, distinguishing between five levels of student participation in the context of a first-year university course delivered via a learning management system. The data collection was implemented through two instruments: i) a questionnaire about students’ interactivity perception in the online reflective learning (n = 238) and then ii) an open discussion on the reason for the diverse perceptions of interactivity (n = 34). The study findings indicate that student participants, other than those who were active, need high levels of teacher or moderator intervention, which better enables legitimate peripheral participation to occur in online learning contexts.

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Aim Evaluation or assessment of competence is an important step to ensure the safety and efficacy of health professionals, including dietitians. Most competency-based assessment studies are focussed on valid and reliable methods of assessment for the preparation of entry-level dietitians, few papers have explored student dietitians’ perceptions of these evaluations. This study aimed to explore the perceptions of recent graduates from accredited nutrition and dietetics training programs in Australia. It also aimed to establish the relevance of competency-based assessment to adequately prepare them for entry-level work roles. Methods A purposive sample of newly-graduated dietitians with a range of assessment experiences and varied employment areas was recruited. A qualitative approach, using in-depth interviews with 13 graduates, with differing assessment experiences was undertaken. Graduates were asked to reflect upon their competency-based assessment experiences whilst a student. Data was thematically analysed by multiple authors. Results Four themes emerged from the data analysis: (i) Transparency and consistency are critical elements of work-based competency assessment. (ii) Students are willing to take greater responsibility in their assessment process. (iii) Work-based competency assessment prepares students for employment. (iv) The relationship between students and their assessors can impact on the student experience and their assessment performance. Conclusions Understanding this unique perspective of students can improve evaluation of future health professionals and assist in designing valid competency-based assessment approaches.

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This thesis reports on a multiple case study of the actions of three Queensland secondary schools in the context of Year 9 NAPLAN numeracy testing, focusing on their administrative practices, curriculum, pedagogy and assessment. It was established that schools have found it both challenging and costly to operate in an environment of educational reform generally, and NAPLAN testing in particular. The lack of a common understanding of numeracy and the substantial demands of implementing the Australian Curriculum have impacted on schools' ability to prepare students appropriately for NAPLAN numeracy tests. It was concluded that there is scope for schools to improve their approaches to NAPLAN numeracy testing in a way that maximises learning as well as test outcomes.

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This book is about understanding the nature and application of reflection in higher education. It provides a theoretical model to guide the implementation of reflective learning and reflective practice across multiple disciplines and international contexts in higher education. The book presents research into the ways in which reflection is both considered and implemented in different ways across different professional disciplines, while maintaining a common purpose to transform and improve learning and/or practice. Readers will find this book innovative and new in three key ways. First, in its holistic theorisation of reflection within the pedagogic field of higher education; Secondly, in conceptualising reflection in different modes to achieve specific purposes in different disciplines; and finally, in providing conceptual guidance for embedding reflective learning and reflective practice in a systematic way across whole programmes, faculties or institutions in higher education. The book considers important contextual factors that influence the teaching of forms and methods of reflection. It provides a functional analysis of multiple modes of reflection, including written, oral, visual, auditory, and embodied forms. Empirical chapters analyse the application of these modes across disciplines and at different stages of a programme. The theoretical model accounts for students’ stage of development in the disciplinary field, along with progressive and cyclical levels of higher order thinking, and learning and professional practice that are expected within different disciplines and professional fields. The book provides: • A conceptual model for the application of reflection across disciplines in a variety of contexts. • Empirical examples of different modes and pedagogic patterns for reflection. • Guidance and support for embedding systemic pedagogical and curriculum change.

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Twenty first century society presents critical challenges for higher education (Brew 2013, 2). The challenges facing modern communities require graduates to have skills that respond to issues at the boundaries of, and intersections between, disciplines. Mounting evidence suggests that interdisciplinary curriculum and pedagogies help students to develop boundary-crossing skills and a deeper awareness of the student’s domain-specific knowledge (Spelt et al. 2009; Strober 2011). Spelt et al. (2009) describe boundary-crossing skills as the ability to engage with different discourses, take account of multiple perspectives, synthesise knowledge of different disciplines, and cope with complexity. In this chapter we investigate emerging conditions, practical processes, and pedagogical strategies that are enabling the Lab stakeholders, the community, the university, and students to participate in interdisciplinary community-engaged learning. Aspects of the Lab that are considered in this chapter include building trust, sharing values, establishing learning goals that are reflected in learning experiences and assessment, and employing strategies that define and attend to relationships and roles. The case study, “The Recognition of Aboriginal and Torres Strait Islander Peoples in the Australian Constitution”, a QUT collaborative project with the Social Justice Research Unit Anglicare Southern Queensland, describes the collaborators, processes, outcomes, and the lessons learned through one Lab project over three semesters. The issues illustrated in the case study are then further explored in a critical discussion of the strategies supporting interdisciplinarity in community-engaged learning across university/community collaboration, within and across the university, and for student participants

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With the increasing need to adapt to new environments, data-driven approaches have been developed to estimate terrain traversability by learning the rover’s response on the terrain based on experience. Multiple learning inputs are often used to adequately describe the various aspects of terrain traversability. In a complex learning framework, it can be difficult to identify the relevance of each learning input to the resulting estimate. This paper addresses the suitability of each learning input by systematically analyzing the impact of each input on the estimate. Sensitivity Analysis (SA) methods provide a means to measure the contribution of each learning input to the estimate variability. Using a variance-based SA method, we characterize how the prediction changes as one or more of the input changes, and also quantify the prediction uncertainty as attributed from each of the inputs in the framework of dependent inputs. We propose an approach built on Analysis of Variance (ANOVA) decomposition to examine the prediction made in a near-to-far learning framework based on multi-task GP regression. We demonstrate the approach by analyzing the impact of driving speed and terrain geometry on the prediction of the rover’s attitude and chassis configuration in a Marsanalogue terrain using our prototype rover Mawson.

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Discounted Cumulative Gain (DCG) is a well-known ranking evaluation measure for models built with multiple relevance graded data. By handling tagging data used in recommendation systems as an ordinal relevance set of {negative,null,positive}, we propose to build a DCG based recommendation model. We present an efficient and novel learning-to-rank method by optimizing DCG for a recommendation model using the tagging data interpretation scheme. Evaluating the proposed method on real-world datasets, we demonstrate that the method is scalable and outperforms the benchmarking methods by generating a quality top-N item recommendation list.

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The advancement of technology and the internet have created global collaborative learning opportunities and changed learning and teaching in any discipline around the world, including in developing countries. The availability of web-based resources and high-speed internet infrastructure has extended the opportunities to conduct blended learning and new ways of distance learning beyond virtual class room webinars. The aim of this exploratory paper is to review the challenges and opportunities for increasing student engagement in virtual learning. A reflective analysis of international collaborative learning case studies, published articles and practices in virtual learning is used to explore the extension of blended learning organised and participated by institutions from multiple countries. The lessons learnt from flexible learning delivery in professional practice courses in Property Education are used to evaluate potential extensions of blended learning implementation in a global context.