The effects of estimating good vs. poor knowledge of results during acquisition of a spatial motor task

Recent studies have shown that providing learners Knowledge of Results (KR) after “good trials” rather than “poor trials” is superior for learning. The present study examined whether requiring participants to estimate their three best or three worst trials in a series of six trial blocks before receiving KR would prove superior to learning compared to not estimating their performance. Participants were required to push and release a slide along a confined pathway using their non-dominant hand to a target distance (133cm). The retention and transfer data suggest those participants who received KR after good trials demonstrated superior learning and performance estimations compared to those receiving KR after poor trials. The results of the present experiment offer an important theoretical extension in our understanding of the role of KR content and performance estimation on motor skill learning.

BP BLOGGER: DESCRIPTIVE STUDY OF ELECTRONIC KNOWLEDGE DISSEMINATION IN LONG TERM CARE

Little is known of the uptake and use of knowledge disseminated in electronic formats, especially in Long Term Care (LTC) settings. The purpose of this descriptive study was to examine the dissemination of the BP Bloggers, a series of brief, evidence summaries designed to meet the knowledge needs of LTC staff. Guided by Rogers’ (2003) Diffusion of Innovations theory, the study documents dissemination of the BP Blogger and examines factors affecting dissemination, awareness, perceptions and its use. The survey of BP Blogger recipients was conducted electronically (n=114) online (n=10), by telephone (n=55), and print (n=144). Managers usually received the newsletter electronically while staff in LTC were more likely to receive printed copies. Participants disseminated the newsletter through paper, email, or posting in the workplace. Most participants rated the content, format, and usefulness of the BP Blogger as good or excellent. Time and lack of email access were barriers to dissemination.

The really useful science book : a framework of knowledge for primary teachers.

Este recurso no ofrece un material que pueda ser utilizado en las aulas, está diseñado para apoyar y ampliar la comprensión de la ciencia de los profesores en formación y los profesores de aula. Consta de tres secciones: la vida y los procesos vivos; materiales y sus propiedades; procesos físicos. En cada sección se sugieren ideas clave que se amplían posteriormente en el texto. Proporciona un conocimiento exhaustivo de la ciencia en la etapa clave 1 y 2 (Key Stage 1 y 2), así como la preparación para la enseñanza y el aprendizaje en la etapa clave 3 (Key Stage 3).

Can knowledge of the state of the stratosphere be used to improve statistical forecasts of the troposphere?

Recent analysis of the Arctic Oscillation (AO) in the stratosphere and troposphere has suggested that predictability of the state of the tropospheric AO may be obtained from the state of the stratospheric AO. However, much of this research has been of a purely qualitative nature. We present a more thorough statistical analysis of a long AO amplitude dataset which seeks to establish the magnitude of such a link. A relationship between the AO in the lower stratosphere and on the 1000 hPa surface on a 10-45 day time-scale is revealed. The relationship accounts for 5% of the variance of the 1000 hPa time series at its peak value and is significant at the 5% level. Over a similar time-scale the 1000 hPa time series accounts for 1% of itself and is not significant at the 5% level. Further investigation of the relationship reveals that it is only present during the winter season and in particular during February and March. It is also demonstrated that using stratospheric AO amplitude data as a predictor in a simple statistical model results in a gain of skill of 5% over a troposphere-only statistical model. This gain in skill is not repeated if an unrelated time series is included as a predictor in the model. Copyright © 2003 Royal Meteorological Society

Assumption of knowledge and the Chinese Room in Turing test interrogation

Whilst common sense knowledge has been well researched in terms of intelligence and (in particular) artificial intelligence, specific, factual knowledge also plays a critical part in practice. When it comes to testing for intelligence, testing for factual knowledge is, in every-day life, frequently used as a front line tool. This paper presents new results which were the outcome of a series of practical Turing tests held on 23rd June 2012 at Bletchley Park, England. The focus of this paper is on the employment of specific knowledge testing by interrogators. Of interest are prejudiced assumptions made by interrogators as to what they believe should be widely known and subsequently the conclusions drawn if an entity does or does not appear to know a particular fact known to the interrogator. The paper is not at all about the performance of machines or hidden humans but rather the strategies based on assumptions of Turing test interrogators. Full, unedited transcripts from the tests are shown for the reader as working examples. As a result, it might be possible to draw critical conclusions with regard to the nature of human concepts of intelligence, in terms of the role played by specific, factual knowledge in our understanding of intelligence, whether this is exhibited by a human or a machine. This is specifically intended as a position paper, firstly by claiming that practicalising Turing's test is a useful exercise throwing light on how we humans think, and secondly, by taking a potentially controversial stance, because some interrogators adopt a solipsist questioning style of hidden entities with a view that it is a thinking intelligent human if it thinks like them and knows what they know. The paper is aimed at opening discussion with regard to the different aspects considered.

The 30-year TAMSAT African rainfall climatology and time-series (TARCAT) dataset

African societies are dependent on rainfall for agricultural and other water-dependent activities, yet rainfall is extremely variable in both space and time and reoccurring water shocks, such as drought, can have considerable social and economic impacts. To help improve our knowledge of the rainfall climate, we have constructed a 30-year (1983–2012), temporally consistent rainfall dataset for Africa known as TARCAT (TAMSAT African Rainfall Climatology And Time-series) using archived Meteosat thermal infra-red (TIR) imagery, calibrated against rain gauge records collated from numerous African agencies. TARCAT has been produced at 10-day (dekad) scale at a spatial resolution of 0.0375°. An intercomparison of TARCAT from 1983 to 2010 with six long-term precipitation datasets indicates that TARCAT replicates the spatial and seasonal rainfall patterns and interannual variability well, with correlation coefficients of 0.85 and 0.70 with the Climate Research Unit (CRU) and Global Precipitation Climatology Centre (GPCC) gridded-gauge analyses respectively in the interannual variability of the Africa-wide mean monthly rainfall. The design of the algorithm for drought monitoring leads to TARCAT underestimating the Africa-wide mean annual rainfall on average by −0.37 mm day−1 (21%) compared to other datasets. As the TARCAT rainfall estimates are historically calibrated across large climatically homogeneous regions, the data can provide users with robust estimates of climate related risk, even in regions where gauge records are inconsistent in time.

Stochastic simulation of time-series models combined with geostatistics to predict water-table scenarios in a Guarani Aquifer System outcrop area, Brazil

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Knowledge management in the public sector: an online presence as a tool for capture and sharing

Includes bibliography