From a Vicious Circle of Anxiety to a Virtuous Circle of Learning: Experience of Teaching to a Heterogenous Clientele

A considerable body of literature suggests that significant psychological barrier and anxiety characterize the teaching and learning process in statistics. This study investigates the incidence of statistics anxiety, the extent to which it can be overcome and the factors that contribute to the process of overcoming it. Self-study and overall teaching quality, amongst others, significantly contributed to this outcome. This study identifies factors contributing to overall teaching quality. The teaching and learning process typified a highly effective communication mechanism based on an appropriate diagnosis of individual needs. This cumulative change resulted from circular causation. It is argued that given appropriate conditions the vicious circle of anxiety can be transformed into a virtuous circle of learning.

Enhancing student performance through cooperative learning in physical sciences

Students in a physical sciences course were introduced to cooperative learning at the University of Queensland, Gatton Campus. Groups of four to five students worked together in tutorial and practical sessions. Mid-term and practical examinations were abolished and 40% of total marks were allocated to the cooperative learning activities. A peer- and self-assessment system was successfully adapted to account for individual performance in cooperative learning group assignments. The results suggest that cooperative learning was very well received by students, and they expressed willingness to join cooperative learning groups in other courses. In addition, cooperative learning offered many benefits to students in terms of graduate attributes such as teamwork, communication, lifelong learning and problem-solving.

The social identity perspective - Intergroup relations, self-conception, and small groups

The historical development, metatheoretical background, and current state of the social identity perspective in social psychology are described. Although originally, an analysis mainly of intergroup relations between large-scale social categories, and more recently an analysis with a strong social cognitive emphasis, this article shows that the social identity perspective is intended to be a general analysis of group membership and group processes. It focuses on the generative relationship between collective self-conception and group phenomena. To demonstrate the relevance of the social identity perspective to small groups, the article describes social identity research in a number of areas: differentiation within groups; leadership; deviance; group decision making; organizations; computer mediated communication; mobilization, collective action, and social loafing; and group culture. These art the areas in which most work has been done and which arc therefore best placed for further developments in the near future.

Copying actions and copying outcomes: Social learning through the second year

The present work documents how the logic of a model's demonstration and the communicative cues that the model provides interact with age to influence how children engage in social learning. Children at ages 12, 18, and 24 months (n = 204) watched a model open a series of boxes. Twelve-month-old subjects only copied the specific actions of the model when they were given a logical reason to do so- otherwise, they focused on reproducing the outcome of the demonstrated actions. Eighteen-month-old subjects focused on copying the outcome when the model was aloof. When the model acted socially, the subjects were as likely to focus on copying actions as outcomes, irrespective of the apparent logic of the model's behavior. Finally, 24-month-old subjects predominantly focused on copying the model's specific actions. However, they were less likely to produce the modeled outcome when the model acted nonsocially.

The parameterless self-organizing map algorithm

The parameterless self-organizing map (PLSOM) is a new neural network algorithm based on the self-organizing map (SOM). It eliminates the need for a learning rate and annealing schemes for learning rate and neighborhood size. We discuss the relative performance of the PLSOM and the SOM and demonstrate some tasks in which the SOM fails but the PLSOM performs satisfactory. Finally we discuss some example applications of the PLSOM and present a proof of ordering under certain limited conditions.

Generative acts: Family and community involvement of older Australians

The article adopts a developmental approach to successful human aging by exploring the concept of generativity in relation to a study of older Australians' lived experiences of involvement in the family and community. Qualitative data, collected through focus group interviews, were analyzed interpretively using recent developments in Erikson's theory of generativity as a framework. As a result, the present study contributes an in-depth understanding of the role of generative acts to the lives of older people. The data provide illustrative support for Erikson's contention of a generativity/stagnation crisis in later life. Involvement in the family and community is seen as a productive and generative activity, which promotes a positive experience of aging. Two further emergent themes are also explored. First, the experiences of study participants illustrate the reciprocal and cyclical nature of grand-generativity, and the importance of intergenerational relationships. Finally, the data contribute to our knowledge of cultural generativity, and in particular the passing on of cultural knowledge through narratives and modeling.

Situating Learning: (Re)examining the Notion of apprenticeship in Coach Education

The last two decades has seen a proliferation in the provision of and importance attached to coach education in many Western countries. Pivotal to many coach education programmes is the notion of apprenticeship. Increasingly, mentoring is being positioned as a possible tool for enhancing coach education and professional expertise. However, there is a paucity of empirical data on interventions in and evaluations of coach education programmes. In their recent evaluation of a coach education programme, Cassidy, Potrac & McKenzie conclude that the situated learning literature could provide coach educators with a generative platform for the (re)examination of apprenticeships and mentoring in a coach education context. This paper discusses the merits of using Situated Learning theory and the associated concept of Communities of Practice (CoP) to stimulate discussion on developing new understandings of the practices of apprenticeship and mentoring in coach education.

Sharing Narratives Online: An international, self-organising curriculum in rural health

The aim of the Rural Medicine Rotation (RMR) at the University of Queensland (UQ) is to give all third year medical students exposure to and an understanding of, clinical practice in Australian rural or remote locations. A difficulty in achieving this is the relatively short period of student clinical placements, in only one or two rural or remote locations. A web-based Clinical Discussion Board (CDB) has been introduced to address this problem by allowing students at various rural sites to discuss their rural experiences and clinical issues with each other. The rationale is to encourage an understanding of the breadth and depth of rural medicine through peer-based learning. Students are required to submit a minimum of four contributions over the course of their six week rural placement. Analysis of student usage patterns shows that the majority of students exceeded the minimum submission criteria indicating motivation rather than compulsion to contribute to the CDB. There is clear evidence that contributing or responding to the CDB develops studentâ??s critical thinking skills by giving and receiving assistance from peers, challenging attitudes and beliefs and stimulating reflective thought. This is particularly evident in regard to issues involving ethics or clinical uncertainty, subject areas that are not in the medical undergraduate curriculum, yet are integral to real-world medical practice. The CDB has proved to be a successful way to understand the concerns and interests of third year medical students immersed in their RMR and also in demonstrating how technology can help address the challenge of supporting students across large geographical areas. We have recently broadened this approach by including students from the Rural Program at The Ohio State University College of Medicine. This important international exchange of ideas and approaches to learning is expected to broaden clinical training content and improve understanding of rural issues.

GTM: the generative topographic mapping

Latent variable models represent the probability density of data in a space of several dimensions in terms of a smaller number of latent, or hidden, variables. A familiar example is factor analysis which is based on a linear transformations between the latent space and the data space. In this paper we introduce a form of non-linear latent variable model called the Generative Topographic Mapping, for which the parameters of the model can be determined using the EM algorithm. GTM provides a principled alternative to the widely used Self-Organizing Map (SOM) of Kohonen (1982), and overcomes most of the significant limitations of the SOM. We demonstrate the performance of the GTM algorithm on a toy problem and on simulated data from flow diagnostics for a multi-phase oil pipeline.

GTM: A principled alternative to the self-organizing map

The Self-Organizing Map (SOM) algorithm has been extensively studied and has been applied with considerable success to a wide variety of problems. However, the algorithm is derived from heuristic ideas and this leads to a number of significant limitations. In this paper, we consider the problem of modelling the probability density of data in a space of several dimensions in terms of a smaller number of latent, or hidden, variables. We introduce a novel form of latent variable model, which we call the GTM algorithm (for Generative Topographic Mapping), which allows general non-linear transformations from latent space to data space, and which is trained using the EM (expectation-maximization) algorithm. Our approach overcomes the limitations of the SOM, while introducing no significant disadvantages. We demonstrate the performance of the GTM algorithm on simulated data from flow diagnostics for a multi-phase oil pipeline.

GTM: the generative topographic mapping

Latent variable models represent the probability density of data in a space of several dimensions in terms of a smaller number of latent, or hidden, variables. A familiar example is factor analysis which is based on a linear transformations between the latent space and the data space. In this paper we introduce a form of non-linear latent variable model called the Generative Topographic Mapping, for which the parameters of the model can be determined using the EM algorithm. GTM provides a principled alternative to the widely used Self-Organizing Map (SOM) of Kohonen (1982), and overcomes most of the significant limitations of the SOM. We demonstrate the performance of the GTM algorithm on a toy problem and on simulated data from flow diagnostics for a multi-phase oil pipeline.

Developments of the generative topographic mapping

The generative topographic mapping (GTM) model was introduced by Bishop et al. (1998, Neural Comput. 10(1), 215-234) as a probabilistic re- formulation of the self-organizing map (SOM). It offers a number of advantages compared with the standard SOM, and has already been used in a variety of applications. In this paper we report on several extensions of the GTM, including an incremental version of the EM algorithm for estimating the model parameters, the use of local subspace models, extensions to mixed discrete and continuous data, semi-linear models which permit the use of high-dimensional manifolds whilst avoiding computational intractability, Bayesian inference applied to hyper-parameters, and an alternative framework for the GTM based on Gaussian processes. All of these developments directly exploit the probabilistic structure of the GTM, thereby allowing the underlying modelling assumptions to be made explicit. They also highlight the advantages of adopting a consistent probabilistic framework for the formulation of pattern recognition algorithms.

Semi-supervised learning of hierarchical latent trait models for data visualisation

An interactive hierarchical Generative Topographic Mapping (HGTM) ¸iteH_GTM has been developed to visualise complex data sets. In this paper, we build a more general visualisation system by extending the HGTM visualisation system in 3 directions: bf (1) We generalize HGTM to noise models from the exponential family of distributions. The basic building block is the Latent Trait Model (LTM) developed in ¸iteKaban_pami. bf (2) We give the user a choice of initializing the child plots of the current plot in either em interactive, or em automatic mode. In the interactive mode the user interactively selects ``regions of interest'' as in ¸iteH_GTM, whereas in the automatic mode an unsupervised minimum message length (MML)-driven construction of a mixture of LTMs is employed. bf (3) We derive general formulas for magnification factors in latent trait models. Magnification factors are a useful tool to improve our understanding of the visualisation plots, since they can highlight the boundaries between data clusters. The unsupervised construction is particularly useful when high-level plots are covered with dense clusters of highly overlapping data projections, making it difficult to use the interactive mode. Such a situation often arises when visualizing large data sets. We illustrate our approach on a toy example and apply our system to three more complex real data sets.

A generative model for separating illumination and reflectance from images

It is well known that even slight changes in nonuniform illumination lead to a large image variability and are crucial for many visual tasks. This paper presents a new ICA related probabilistic model where the number of sources exceeds the number of sensors to perform an image segmentation and illumination removal, simultaneously. We model illumination and reflectance in log space by a generalized autoregressive process and Hidden Gaussian Markov random field, respectively. The model ability to deal with segmentation of illuminated images is compared with a Canny edge detector and homomorphic filtering. We apply the model to two problems: synthetic image segmentation and sea surface pollution detection from intensity images.

Inquiring how a lecturer keeps learning about their teaching:a personal case history on reflective imagination

In order to reverse the use of lecture-based teaching, it is argued that personal reflection can be used as part of the quality assurance process. This paper proposes one response to personal reflection - reflective imagination, which is summarised as an action plan with six activities. It combines two conceptual issues raised in the US, the need to think creatively about learning and the reflective mindset, and one issue raised in the UK, cultivating the entrepreneurial imagination. Reflective imagination is linked to wider social science research, the place of self and reflexivity in scholarship. Finally, a personal history case study is presented which records a visit to Harvard Business School. The visit implements the six activities associated with reflective imagination. This is a method paper exploring reflective imagination.