Student Learning in Higher Education: a Commentary

This commentary begins by summarizing the five contributions to this special issue and briefly recapping the background to the topic of student learning in higher education. Narrative and systematic reviews are compared, and the relative value of different bibliographic databases in the context of systematic reviews is assessed. The importance of measures of effect size is stressed. The relationship of the five contributions to early research on levels of processing and approaches to learning is discussed, along with the presage–process–product model of student learning and historical discussions that are relevant to the current theoretical discussions. This field has benefited from the development of more robust instrumentation, but researchers must continue to develop new kinds of measure, including online measures of students’ strategy use. Researchers need to consider ways of enhancing the quality of student learning through the use of problem-based curricula and other student-centered approaches. Finally, it is suggested that researchers into student learning need to evaluate whether their concepts, methods, theories, and findings are valid in online environments and to investigate how curricula in higher education can build upon those in secondary education

Synaptic shot noise and conductance fluctuations affect the membrane voltage with equal significance

The subthresholdmembranevoltage of a neuron in active cortical tissue is a fluctuating quantity with a distribution that reflects the firing statistics of the presynaptic population. It was recently found that conductancebased synaptic drive can lead to distributions with a significant skew. Here it is demonstrated that the underlying shot noise caused by Poissonian spike arrival also skews the membrane distribution, but in the opposite sense. Using a perturbative method, we analyze the effects of shot noise on the distribution of synaptic conductances and calculate the consequent voltage distribution. To first order in the perturbation theory, the voltage distribution is a gaussian modulated by a prefactor that captures the skew. The gaussian component is identical to distributions derived using current-based models with an effective membrane time constant. The well-known effective-time-constant approximation can therefore be identified as the leading-order solution to the full conductance-based model. The higher-order modulatory prefactor containing the skew comprises terms due to both shot noise and conductance fluctuations. The diffusion approximation misses these shot-noise effects implying that analytical approaches such as the Fokker-Planck equation or simulation with filtered white noise cannot be used to improve on the gaussian approximation. It is further demonstrated that quantities used for fitting theory to experiment, such as the voltage mean and variance, are robust against these non-Gaussian effects. The effective-time-constant approximation is therefore relevant to experiment and provides a simple analytic base on which other pertinent biological details may be added

Unearthing learners’ conceptions of reflection to innovate business education for the 21st century

The development of learners’ capacities for critical reflection is an important learning outcome for 21st century business education. Theories suggest that a learner holds a particular orientation to reflection, and that this perspective will be influenced by his or her underlying beliefs. This, coupled with an increased focus on the student experience, personal development, and self-regulation in higher education, offers scope for considering instructional design from a second-order perspective, or in other words, from the student’s point of view. This study sought to understand: 1) the ways that business students orientate to reflection, 2) the different conceptions they hold of reflection, and 3) whether there is a relationship between the two. Reflective learning questionnaires were completed by 112 business students studying at the University of Northampton. Survey results showed that while the research instrument was a good fit for investigating orientations to and conceptions of reflection, there did not appear to be a correlation between the two. Learning analytics such as these will be useful for considering how the University can design more meaningful business curricula. However, the disconnect between conceptions of and orientations to reflection needs to be explored through further research

Intercomparison of phenological transition dates derived from the PhenoCam Dataset V1.0 and MODIS satellite remote sensing

Phenology is a valuable diagnostic of ecosystem health, and has applications to environmental monitoring and management. Here, we conduct an intercomparison analysis using phenological transition dates derived from near-surface PhenoCam imagery and MODIS satellite remote sensing. We used approximately 600 site-years of data, from 128 camera sites covering a wide range of vegetation types and climate zones. During both “greenness rising” and “greenness falling” transition phases, we found generally good agreement between PhenoCam and MODIS transition dates for agricultural, deciduous forest, and grassland sites, provided that the vegetation in the camera field of view was representative of the broader landscape. The correlation between PhenoCam and MODIS transition dates was poor for evergreen forest sites. We discuss potential reasons (including sub-pixel spatial heterogeneity, flexibility of the transition date extraction method, vegetation index sensitivity in evergreen systems, and PhenoCam geolocation uncertainty) for varying agreement between time series of vegetation indices derived from PhenoCam and MODIS imagery. This analysis increases our confidence in the ability of satellite remote sensing to accurately characterize seasonal dynamics in a range of ecosystems, and provides a basis for interpreting those dynamics in the context of tangible phenological changes occurring on the ground

DEMAND FOR FEED GRAINS AND CONCENTRATES BY LIVESTOCK CATEGORY

Livestock feed demand is a collection of derived feed demands by various livestock categories. A structural understanding of demand for feed grains and total concentrates requires knowledge of separate feed demand relationships for each major livestock category. While a number of aggregate livestock feed demand relationships have been estimated, little is known about the structure of feed demand by livestock type. In this study unique livestock feed demand relationships for feed grains and total concentrates are estimated for each of seven major livestock categories. The estimated relationships show substantial differences in elasticities of concentrate and feed grain feed demand with respect to livestock price across livestock groups. Using feed demand parameters by livestock category enables analysts to evaluate policy effects of changes in feed demand quantities and feed costs within the livestock economy, as well as to provide more reliable estimates of the total change in feed demand.Livestock Production/Industries,