A narrative review of adaptive testing and its application to medical education.

Adaptive testing has a long but largely unrecognized history. The advent of computer-based testing has created new opportunities to incorporate adaptive testing into conventional programmes of study. Relatively recently software has been developed that can automate the delivery of summative assessments that adapt by difficulty or content. Both types of adaptive testing require a large item bank that has been suitably quality assured. Adaptive testing by difficulty enables more reliable evaluation of individual candidate performance, although at the expense of transparency in decision making, and requiring unidirectional navigation. Adaptive testing by content enables reduction in compensation and targeted individual support to enable assurance of performance in all the required outcomes, although at the expense of discovery learning. With both types of adaptive testing, candidates are presented a different set of items to each other, and there is the potential for that to be perceived as unfair. However, when candidates of different abilities receive the same items, they may receive too many they can answer with ease, or too many that are too difficult to answer. Both situations may be considered unfair as neither provides the opportunity to demonstrate what they know. Adapting by difficulty addresses this. Similarly, when everyone is presented with the same items, but answer different items incorrectly, not providing individualized support and opportunity to demonstrate performance in all the required outcomes by revisiting content previously answered incorrectly could also be considered unfair; a point addressed when adapting by content. We review the educational rationale behind the evolution of adaptive testing and consider its inherent strengths and limitations. We explore the continuous pursuit of improvement of examination methodology and how software can facilitate personalized assessment. We highlight how this can serve as a catalyst for learning and refinement of curricula; fostering engagement of learner and educator alike

Rainfall intensity and catchment size control storm runoff in a gullied blanket peatland

Upland blanket peat is widespread in the headwaters of UK catchments, but much of it has been degraded through atmospheric pollution, vegetation change and erosion. Runoff generation in these headwaters is an important element of downstream flood risk and these areas are increasingly the focus of interventions to restore the peat ecosystem and to potentially mitigate downstream flooding. Here we use a series of multivariate analysis techniques to examine controls on storm runoff behavior within and between ten blanket peat catchments all within 5 km of one another and ranging in size from 0.2 to 3.9 ha. We find that: 1) for all 10 catchments, rainfall intensity is the dominant driver for both magnitude and timing of peak discharge, and that total and antecedent rainfall is important for peak discharge only in small storms; 2) there is considerable inter-catchment variability in: runoff coefficient, lag time, peak runoff, and their predictability from rainfall; however, 3) a significant fraction of the inter-catchment variability can be explained by catchment characteristics, particularly catchment area; and 4) catchment controls on peak discharge and runoff coefficient for small storms highlight the importance of storage and connectivity while those for large events suggest that surface flow attenuation dominates. Together these results suggest a switching rainfall-runoff behavior where catchment storage, connectivity and antecedent conditions control small discharge peaks but become increasingly irrelevant for larger storms. Our results suggest that, in the context of Natural Flood Management potential, expanding depression storage (e.g. distributed shallow water pools) in addition to existing restoration methods could increase the range of storms within which connectivity and storage remain important and that for larger storms measures which target surface runoff velocities are likely to be important

Universal clinical Parkinson’s disease axes identify a major influence of neuroinflammation

: Background: There is large individual variation in both clinical presentation and progression between Parkinson’s disease patients. Generation of deeply and longitudinally phenotyped patient cohorts has enormous potential to identify disease subtypes for prognosis and therapeutic targeting. Methods: Replicating across three large Parkinson’s cohorts (Oxford Discovery cohort (n = 842)/Tracking UK Parkinson’s study (n = 1807) and Parkinson’s Progression Markers Initiative (n = 472)) with clinical observational measures collected longitudinally over 5–10 years, we developed a Bayesian multiple phenotypes mixed model incorporating genetic relationships between individuals able to explain many diverse clinical measurements as a smaller number of continuous underlying factors (“phenotypic axes”). Results: When applied to disease severity at diagnosis, the most influential of three phenotypic axes “Axis 1” was characterised by severe non-tremor motor phenotype, anxiety and depression at diagnosis, accompanied by faster progression in cognitive function measures. Axis 1 was associated with increased genetic risk of Alzheimer’s disease and reduced CSF Aβ1-42 levels. As observed previously for Alzheimer’s disease genetic risk, and in contrast to Parkinson’s disease genetic risk, the loci influencing Axis 1 were associated with microglia-expressed genes implicating neuroinflammation. When applied to measures of disease progression for each individual, integration of Alzheimer’s disease genetic loci haplotypes improved the accuracy of progression modelling, while integrating Parkinson’s disease genetics did not. Conclusions: We identify universal axes of Parkinson’s disease phenotypic variation which reveal that Parkinson’s patients with high concomitant genetic risk for Alzheimer’s disease are more likely to present with severe motor and non-motor features at baseline and progress more rapidly to early dementia

Effect of a Strawberry and Spinach Dietary Supplement on Spatial Learning in Early and Late Middle-Aged Female Rats

The present experiment sought to determine the effect of an eight-week, high antioxidant, whole-foods dietary supplement on Morris Water Maze performance in early and late middle-aged female rats. To improve ecological validity over past experimental studies, rats in the current study received antioxidants by consuming freeze-dried organic strawberries and spinach rather than by being given food extracts or antioxidant injections. Latency and path length measures both indicated that late middle-aged rats fed the high antioxidant diet performed on a par with the younger animals earlier in training than their standard diet counterparts (p < 0.05). Superior performance was not due to improved fitness in the antioxidant-supplemented rats. Thus, our model showed that a high antioxidant diet of relatively short duration mitigated the mild cognitive decline that was seen in control animals during the developmental period of late middle-age. The current results offer support for the promising role of dietary antioxidants in maintaining cognitive health in normal aging and extend past findings to females, who have been relatively neglected in experimental investigations. Moreover, the current model suggests that the period of transition from early to late middle age is a promising target for dietary intervention in healthy adults

Unpacking the efficacy of Reading to Learn using Cognitive Load Theory

This paper synthesises the key findings of past separate studies conducted by the same authors, which sought to assess the efficacy of the Reading to Learn (RtL) literacy intervention on students' academic writing performance. Both studies of RtL were implemented in response to growing concerns about the academic under-preparedness of undergraduate students at universities across South Africa. The first study aimed to support mostly first-generation, first-year English Additional Language (EAL) learners in their transition to higher education. The second study aimed to support EAL students' academic writing development at a senior secondary school level prior to the school-to-university transition. In both studies, the cohorts of students examined originated from low socioeconomic communities, where linguistic marginalisation arguably imposes significant barriers to successful university completion. The novel contribution of this paper is to use a Cognitive Load Theoretical lens to explicate why RtL might improve the academic writing skills of under-prepared students making the transition to university