An Evolutionary Upgrade of Cognitive Load Theory: Using the Human Motor System and Collaboration to Support the Learning of Complex Cognitive Tasks

Cognitive load theory is intended to provide instructional strategies derived from experimental, cognitive load effects. Each effect is based on our knowledge of human cognitive architecture, primarily the limited capacity and duration of a human working memory. These limitations are ameliorated by changes in long-term memory associated with learning. Initially, cognitive load theory's view of human cognitive architecture was assumed to apply to all categories of information. Based on Geary's (Educational Psychologist 43, 179-195 2008; 2011) evolutionary account of educational psychology, this interpretation of human cognitive architecture requires amendment. Working memory limitations may be critical only when acquiring novel information based on culturally important knowledge that we have not specifically evolved to acquire. Cultural knowledge is known as biologically secondary information. Working memory limitations may have reduced significance when acquiring novel

LOFAR tied-array imaging and spectroscopy of solar S bursts

Context. The Sun is an active source of radio emission that is often associated with energetic phenomena ranging from nanoflares to coronal mass ejections (CMEs). At low radio frequencies (<100 MHz), numerous millisecond duration radio bursts have been reported, such as radio spikes or solar S bursts (where S stands for short). To date, these have neither been studied extensively nor imaged because of the instrumental limitations of previous radio telescopes. Aims. Here, LOw Frequency ARray (LOFAR) observations were used to study the spectral and spatial characteristics of a multitude of S bursts, as well as their origin and possible emission mechanisms. Methods. We used 170 simultaneous tied-array beams for spectroscopy and imaging of S bursts. Since S bursts have short timescales and fine frequency structures, high cadence (~50 ms) tied-array images were used instead of standard interferometric imaging, that is currently limited to one image per second. Results. On 9 July 2013, over 3000 S bursts were observed over a time period of ~8 h. S bursts were found to appear as groups of short-lived (<1 s) and narrow-bandwidth (~2.5 MHz) features, the majority drifting at ~3.5 MHz s-1 and a wide range of circular polarisation degrees (2−8 times more polarised than the accompanying Type III bursts). Extrapolation of the photospheric magnetic field using the potential field source surface (PFSS) model suggests that S bursts are associated with a trans-equatorial loop system that connects an active region in the southern hemisphere to a bipolar region of plage in the northern hemisphere. Conclusions. We have identified polarised, short-lived solar radio bursts that have never been imaged before. They are observed at a height and frequency range where plasma emission is the dominant emission mechanism, however, they possess some of the characteristics of electron-cyclotron maser emission

Imaging Jupiter's radiation belts down to 127 MHz with LOFAR

Context. Observing Jupiter's synchrotron emission from the Earth remains today the sole method to scrutinize the distribution and dynamical behavior of the ultra energetic electrons magnetically trapped around the planet (because in-situ particle data are limited in the inner magnetosphere). Aims. We perform the first resolved and low-frequency imaging of the synchrotron emission with LOFAR at 127 MHz. The radiation comes from low energy electrons (~1-30 MeV) which map a broad region of Jupiter's inner magnetosphere. Methods (see article for complete abstract) Results. The first resolved images of Jupiter's radiation belts at 127-172 MHz are obtained along with total integrated flux densities. They are compared with previous observations at higher frequencies and show a larger extent of the synchrotron emission source (>=4 $R_J$). The asymmetry and the dynamic of east-west emission peaks are measured and the presence of a hot spot at lambda_III=230 {\deg} $\pm$ 25 {\deg}. Spectral flux density measurements are on the low side of previous (unresolved) ones, suggesting a low-frequency turnover and/or time variations of the emission spectrum. Conclusions. LOFAR is a powerful and flexible planetary imager. The observations at 127 MHz depict an extended emission up to ~4-5 planetary radii. The similarities with high frequency results reinforce the conclusion that: i) the magnetic field morphology primarily shapes the brightness distribution of the emission and ii) the radiating electrons are likely radially and latitudinally distributed inside about 2 $R_J$. Nonetheless, the larger extent of the brightness combined with the overall lower flux density, yields new information on Jupiter's electron distribution, that may shed light on the origin and mode of transport of these particles.Comment: 10 pages, 12 figures, accepted for publication in A&A (27/11/2015) - abstract edited because of limited character

A prospective randomized trial of content expertise versus process expertise in small group teaching

Article deposited according to agreement with BMC, December 6, 2010.YesFunding provided by the Open Access Authors Fund

LOFAR Sparse Image Reconstruction

Context. The LOw Frequency ARray (LOFAR) radio telescope is a giant digital phased array interferometer with multiple antennas distributed in Europe. It provides discrete sets of Fourier components of the sky brightness. Recovering the original brightness distribution with aperture synthesis forms an inverse problem that can be solved by various deconvolution and minimization methods Aims. Recent papers have established a clear link between the discrete nature of radio interferometry measurement and the "compressed sensing" (CS) theory, which supports sparse reconstruction methods to form an image from the measured visibilities. Empowered by proximal theory, CS offers a sound framework for efficient global minimization and sparse data representation using fast algorithms. Combined with instrumental direction-dependent effects (DDE) in the scope of a real instrument, we developed and validated a new method based on this framework Methods. We implemented a sparse reconstruction method in the standard LOFAR imaging tool and compared the photometric and resolution performance of this new imager with that of CLEAN-based methods (CLEAN and MS-CLEAN) with simulated and real LOFAR data Results. We show that i) sparse reconstruction performs as well as CLEAN in recovering the flux of point sources; ii) performs much better on extended objects (the root mean square error is reduced by a factor of up to 10); and iii) provides a solution with an effective angular resolution 2-3 times better than the CLEAN images. Conclusions. Sparse recovery gives a correct photometry on high dynamic and wide-field images and improved realistic structures of extended sources (of simulated and real LOFAR datasets). This sparse reconstruction method is compatible with modern interferometric imagers that handle DDE corrections (A- and W-projections) required for current and future instruments such as LOFAR and SKAComment: Published in A&A, 19 pages, 9 figure

Air quality and urban sustainable development: the application of machine learning tools

[EN] Air quality has an efect on a population¿s quality of life. As a dimension of sustainable urban development, governments have been concerned about this indicator. This is refected in the references consulted that have demonstrated progress in forecasting pollution events to issue early warnings using conventional tools which, as a result of the new era of big data, are becoming obsolete. There are a limited number of studies with applications of machine learning tools to characterize and forecast behavior of the environmental, social and economic dimensions of sustainable development as they pertain to air quality. This article presents an analysis of studies that developed machine learning models to forecast sustainable development and air quality. Additionally, this paper sets out to present research that studied the relationship between air quality and urban sustainable development to identify the reliability and possible applications in diferent urban contexts of these machine learning tools. To that end, a systematic review was carried out, revealing that machine learning tools have been primarily used for clustering and classifying variables and indicators according to the problem analyzed, while tools such as artifcial neural networks and support vector machines are the most widely used to predict diferent types of events. The nonlinear nature and synergy of the dimensions of sustainable development are of great interest for the application of machine learning tools.Molina-Gómez, NI.; Díaz-Arévalo, JL.; López Jiménez, PA. (2021). Air quality and urban sustainable development: the application of machine learning tools. 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Watching People Making Decisions: A Gogglebox on Online Consumer Interaction

This paper presents a research study, using eye tracking technology, to measure participant cognitive load when encountering micro-decision. It elaborates and improves on a pilot study that was used to test the experiment design. Prior research that led to a taxonomy of decision constructs faced in online transactional processes is discussed. The main findings relate to participants’ subjective cognitive load and task error rates

LOFAR detections of low-frequency radio recombination lines towards Cassiopeia A

Cassiopeia A was observed using the low-band antennas of the LOw Frequency ARray (LOFAR) with high spectral resolution. This allowed a search for radio recombination lines (RRLs) along the line-of-sight to this source. Five carbon {$α$} RRLs were detected in absorption between 40 and 50 MHz with a signal-to-noise ratio of {gt}5 from two independent LOFAR data sets. The derived line velocities (v$_{LSR}$ ~{} - 50 km s$^{-1}$) and integrated optical depths (~{}13 s$^{-1}$) of the RRLs in our spectra, extracted over the whole supernova remnant, are consistent within each LOFAR data set and with those previously reported. For the first time, we are able to extract spectra against the brightest hotspot of the remnant at frequencies below 330 MHz. These spectra show significantly higher (15-80 percent) integrated optical depths, indicating that there is small-scale angular structure of the order of ~{}1 pc in the absorbing gas distribution over the face of the remnant. We also place an upper limit of 3 { imes} 10$^{-4}$ on the peak optical depths of hydrogen and helium RRLs. These results demonstrate that LOFAR has the desired spectral stability and sensitivity to study faint recombination lines in the decameter band