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.

Principals' and teachers' perceptions of learning disabilities: A study from Nara Prefecture, Japan

In this study, perceptions of learning disabilities were obtained from 128 principals and 123 teachers in the Nara Prefecture, Japan. A factor analysis indicated that five factors underlie perceptions of learning disabilities: changes in the family and social situation, insufficient knowledge of and support for learning disabilities, teachers' abilities and professional development, teachers' situation and governmental issues. Teachers' situation was perceived to be the main factor, whereas the least important factor was governmental issues. Teachers mainly indicated agreement on the factor of insufficient knowledge of and support for students with learning disabilities. Principals were more aware of governmental issues than teachers.

Coral mortality increases wave energy reaching shores protected by reef flats: Examples from the Seychelles

In the granitic Seychelles, many shores and beaches are fringed by coral reef flats which provide protection to shores from erosion by waves. The surfaces of these reef flats support a complex ecology. About 10 years ago their seaward zones were extensively covered by a rich coral growth, which reached approximately to mean low water level, but in 1998 this was largely killed by seawater warming. The resulting large expanses of dead coral skeletons in these locations are now disintegrating, and much of the subsequent modest recovery by new coral recruitment was set back by further mortalities. A mathematical model of wave energy reaching shorelines protected by coral reef flats has been applied to 14 Seychelles reefs. It is derived from equations which predict: (1) the raised water level, or wave set-up, on reef flats resulting from wave breaking, which depends upon offshore wave height and period, depth of still water over the reef flat and the reef crest profile, and (2) the decay of energy from reef edge to shoreline that is affected by width of reef flat, surface roughness, sea level rise and 'pseudo-sea level rise' created by increased depth resulting from disintegration of coral colonies. The model treats each reef as one entity, but because biota and zonation on reef flats are not homogenous, all reefs are divided into four zones. In each, cover by both living and dead biota was estimated for calculation of parameters, and then averaged to obtain input data for the model. All possible biological factors were taken into account, such as the ability of seagrass beds to grow upwards to match expected sea level rise, reduction in height of the reef flat in relation to sea level as zones of dead corals decay, and the observed 'rounding' of reef crests as erosion removes corals from those areas. Estimates were also made of all these factors for a time approximately a decade ago, representing a time before the mass coral mortality, and for approximately a decade in the future when the observed rapid state of dead coral colony disintegration is assumed to have reached an end point. Results of increased energy over the past decade explain observations of erosion in some sites in the Seychelles. Most importantly, it is estimated that the rise in energy reaching shores protected by fringing reefs will now accelerate more rapidly, such that the increase expected over the next decade will be approximately double than that seen over the past decade. (c) 2005 Elsevier Ltd. All rights reserved.

An Incremental EM-based Learning Approach for On-Line Prediction of Hospital Resource Utilization

Objective: Inpatient length of stay (LOS) is an important measure of hospital activity, health care resource consumption, and patient acuity. This research work aims at developing an incremental expectation maximization (EM) based learning approach on mixture of experts (ME) system for on-line prediction of LOS. The use of a batchmode learning process in most existing artificial neural networks to predict LOS is unrealistic, as the data become available over time and their pattern change dynamically. In contrast, an on-line process is capable of providing an output whenever a new datum becomes available. This on-the-spot information is therefore more useful and practical for making decisions, especially when one deals with a tremendous amount of data. Methods and material: The proposed approach is illustrated using a real example of gastroenteritis LOS data. The data set was extracted from a retrospective cohort study on all infants born in 1995-1997 and their subsequent admissions for gastroenteritis. The total number of admissions in this data set was n = 692. Linked hospitalization records of the cohort were retrieved retrospectively to derive the outcome measure, patient demographics, and associated co-morbidities information. A comparative study of the incremental learning and the batch-mode learning algorithms is considered. The performances of the learning algorithms are compared based on the mean absolute difference (MAD) between the predictions and the actual LOS, and the proportion of predictions with MAD < 1 day (Prop(MAD < 1)). The significance of the comparison is assessed through a regression analysis. Results: The incremental learning algorithm provides better on-line prediction of LOS when the system has gained sufficient training from more examples (MAD = 1.77 days and Prop(MAD < 1) = 54.3%), compared to that using the batch-mode learning. The regression analysis indicates a significant decrease of MAD (p-value = 0.063) and a significant (p-value = 0.044) increase of Prop(MAD