Designing a Web-based learning environment to maximise interactivity

The on-line learning support system for first-year physics students described in this paper gives students enrolled in an on-campus traditional course an alternative learning strategy for two-week modules of their course. This paper presents some of the decisions and choices made in designing such an on-line learning resource and examines the role of interactivity. In this presentation, some examples from the materials produced will be shown

The role of IT in teaching experimental science: from the multimedia perspective

Many motivations might bring one to examine the role of computers in the laboratory class: concern over the expense of real labs; concern over the encroachment of computers into these labs; a feel of excitement that computers might be able to add something to lab work that is currently missing. For whatever reason, it is maybe appropriate to begin by thinking about our motivations for doing real lab work in the first place

ONLINE HEALTH INFORMATION SEEKING BEHAVIOUR: UNDERSTANDING DIFFERENT SEARCH APPROACHES

People intuitively use search engines to look for health information. However, people take an exploratory search approach to find the information in some scenarios, and current search engines do not support these cases well. This exploratory information seeking behaviour is rarely investigated by researchers in the context of online consumer health information. We report on a qualitative study to conceptualise the health information seeking behaviour of lay-people. This paper describes the result of this study, and makes a contribution towards a conceptual understanding of search approaches by people seeking health information, search strategies used by health information seekers, and design implications for providing a better exploratory health search experience

INNOVATION IN DESIGNING HEALTH INFORMATION WEBSITES: RESULTS FROM A QUANTITATIVE STUDY

A wealth of health information exists on the Internet, but successfully finding that information is not easy. One of the issues causing this is the lack of tools for exploring information and assisting in navigation within health websites. As a result, relevant information cannot be easily discovered. We hope to rectify this issue from the design perspective. Based on previous work, we have created a prototype website called Better Health Explorer to better support such information seeking behaviours. This paper reports on a quantitative study evaluating this prototype. The results demonstrate several improvements in health information seeking supported by the tool. Furthermore, we have identified three general design characteristics that should to be considered when designing consumer health websites. These findings have design implications for health information seeking applications, as well as identifying directions for further research

Peer review as alearning tool

In recent years it has become quite common to use a routine research practice within our classrooms: that of peer review. Involving students in peer review is often regarded as an effective way to help students improve the quality of their assignment work before it is submitted. The peer review process offers many other benefits to students: an opportunity to reflect on their work in the light of the work of others; to observe the quality and scope of other students' work; to critically evaluate a piece of work and construct appropriate and constructive feedback; to develop critical thinking, higher-order cognitive, negotiation, and diplomacy skills. However, one benefit less often acknowledged is the impact that the peer review process can have on students' learning. In this presentation we will report on two case studies that illustrate how peer review improved the conceptual understanding of students. One study was a formal research study that assessed the effect of peer review on the performance of third year zoology students. The other less formal study describes students' self-reported conceptual gains in a first year physics subject

Predicting tree distributions in an East African biodiversity hotspot : model selection, data bias and envelope uncertainty

The Eastern Arc Mountains (EAMs) of Tanzania and Kenya support some of the most ancient tropical rainforest on Earth. The forests are a global priority for biodiversity conservation and provide vital resources to the Tanzanian population. Here, we make a first attempt to predict the spatial distribution of 40 EAM tree species, using generalised additive models, plot data and environmental predictor maps at sub 1 km resolution. The results of three modelling experiments are presented, investigating predictions obtained by (1) two different procedures for the stepwise selection of predictors, (2) down-weighting absence data, and (3) incorporating an autocovariate term to describe fine-scale spatial aggregation. In response to recent concerns regarding the extrapolation of model predictions beyond the restricted environmental range of training data, we also demonstrate a novel graphical tool for quantifying envelope uncertainty in restricted range niche-based models (envelope uncertainty maps). We find that even for species with very few documented occurrences useful estimates of distribution can be achieved. Initiating selection with a null model is found to be useful for explanatory purposes, while beginning with a full predictor set can over-fit the data. We show that a simple multimodel average of these two best-model predictions yields a superior compromise between generality and precision (parsimony). Down-weighting absences shifts the balance of errors in favour of higher sensitivity, reducing the number of serious mistakes (i.e., falsely predicted absences); however, response functions are more complex, exacerbating uncertainty in larger models. Spatial autocovariates help describe fine-scale patterns of occurrence and significantly improve explained deviance, though if important environmental constraints are omitted then model stability and explanatory power can be compromised. We conclude that the best modelling practice is contingent both on the intentions of the analyst (explanation or prediction) and on the quality of distribution data; generalised additive models have potential to provide valuable information for conservation in the EAMs, but methods must be carefully considered, particularly if occurrence data are scarce. Full results and details of all species models are supplied in an online Appendix. (C) 2008 Elsevier B.V. All rights reserved

Spin and Statistics and First Principles

It was shown in the early Seventies that, in Local Quantum Theory (that is the most general formulation of Quantum Field Theory, if we leave out only the unknown scenario of Quantum Gravity) the notion of Statistics can be grounded solely on the local observable quantities (without assuming neither the commutation relations nor even the existence of unobservable charged field operators); one finds that only the well known (para)statistics of Bose/Fermi type are allowed by the key principle of local commutativity of observables. In this frame it was possible to formulate and prove the Spin and Statistics Theorem purely on the basis of First Principles. In a subsequent stage it has been possible to prove the existence of a unique, canonical algebra of local field operators obeying ordinary Bose/Fermi commutation relations at spacelike separations. In this general guise the Spin - Statistics Theorem applies to Theories (on the four dimensional Minkowski space) where only massive particles with finite mass degeneracy can occur. Here we describe the underlying simple basic ideas, and briefly mention the subsequent generalisations; eventually we comment on the possible validity of the Spin - Statistics Theorem in presence of massless particles, or of violations of locality as expected in Quantum Gravity.Comment: Survey based on a talk given at the Meeting on "Theoretical and experimental aspects of the spin - statistics connection and related symmetries", Trieste, Italy - October 21-25, 200

Rim-to-Rim Wearables at the Canyon for Health (R2R WATCH): Physiological, Cognitive, and Biological Markers of Performance Decline in an Extreme Environment

Success in extreme environments comes with a cost of subtle performance decrements that if not mitigated properly can lead to lifethreatening consequences. Identification and prediction of performance decline could alleviate deleterious consequences and enhance success in challenging and high-risk operations. The Rim-to-Rim Wearables at the Canyon for Health (R2R WATCH) project was designed to examine the cognitive, physiological, and biological markers of performance decline in the extreme environment of the Grand Canyon Rim-to-Rim (R2R) hike. The study utilized commercial off-the-shelf cognitive and physiological monitoring techniques, along with subjective self-assessments and hematologic measurements to determine subject performance and changes across the hike. The multiyear effort collected these multiple data streams in parallel on a large sample of participants hiking the R2R, leading to a rich and complex data set. This article describes the methodology and its evolution as devices and measurements were assessed after each data collection event. It also highlights a subset of the patterns of results found across the data streams. Subsequent work will draw on this data set to focus on building more sophisticated, predictive statistical models and dive deeper into specific analyses (such as the physiological and biological profiles of hikers who were left behind by their hiking partners)