Sociocultural Aspects of Metacognitive Monitoring

Metacognitive monitoring (the ability to introspect and evaluate cognitive activities and processes) is crucial for children’s self-regulated learning and academic achievement. While previous work has primarily assessed metacognitive monitoring in WEIRD (Western, Educated, Industrialized, Rich, and Democratic) populations, research with non-WEIRD populations is sparse. This is problematic as it limits the generalizability of the findings to a minority of the world population. Thus, the primary goal of the present umbrella paper is to highlight sociocultural aspects of children’s metacognitive monitoring. Based on the multifaceted and multilevel model of metacognition (Efklides, 2008) and the cultural origins hypothesis (Heyes et al., 2020), I explored three research projects (Studies 1, 2, and 3) through a sociocultural lens. Study 1 revealed that native and non-native speakers do not differ in their metacognitive monitoring in memory and text comprehension tasks, which might suggest that native and non-native speakers share a highly similar sociocultural context for learning (e.g., schools). Study 2 found that native speakers' first language abilities in kindergarten predict metacognitive monitoring in grade one. Conversely, non-native speakers' overconfidence in kindergarten predicted their second language abilities in grade one. Study 3 revealed that metacognitive feedback benefits first graders' metacognitive monitoring. Taken together, our results suggest no cross-cultural differences between native and non-native speakers' metacognitive monitoring, and language and feedback as sociocultural features explaining within-cultural variance in children’s metacognitive monitoring. However, more cross-cultural and within-cultural research is needed to clarify the role of sociocultural aspects for children’s metacognitive monitoring development. This may benefit children’s learning worldwide. Keywords: metacognitive monitoring, sociocultural aspects, native and non-native speakers, language, feedbac

Im Flow beim Übersetzen : prozessbasierte Indikatoren zur Untersuchung von Flow-Zuständen im Übersetzungsprozess

Nr. 15 der Graduate Papers in Applied LinguisticsIn einem von Zeit- und Kostendruck geprägten Markt ist es für Übersetzende wichtig, effizient zu arbeiten und gleichzeitig ihre beste Leistung abzurufen. Dies können sie erreichen, wenn sie sich in einem kognitiven Zustand befinden, der als Flow bezeichnet wird. Flow wurde bis anhin in der Übersetzungswissenschaft noch kaum untersucht. In dieser Arbeit wurden deshalb prozessbasierte Indikatoren identifiziert, anhand derer Flow-Zustände im Übersetzungsprozess untersucht werden könnten. Davon ausgehend wurden flow-bezogene Vergleiche zwischen Übersetzenden mit unterschiedlichen Erfahrungsniveaus sowie zwischen unterschiedlichen Texten und zwischen unterschiedlichen Teilen der Entwurfsphase des Übersetzungsprozesses gezogen. Diese Vergleiche sollten den Fokus für kommende Untersuchungen schärfen. Verbale Daten und Beobachtungsdaten aus Übersetzungsprozessen wurden auf eine Auswahl von Indikatoren überprüft, die anhand der zur Verfügung stehenden Forschungsliteratur erarbeitet worden waren. Es zeigte sich, dass neben verbalen Daten auch Indikatoren in Keystroke-Logging-Daten Hinweise auf Flow liefern dürften, während die gewählten Indikatoren in Eye-Tracking-Daten wenig aussagekräftige Ergebnisse generierten. Ausserdem lassen die Resultate vermuten, dass professionelle Übersetzende häufiger Flow erleben als Masterstudierende. Zwischen den in dieser Arbeit betrachteten Texten und Teilen der Entwurfsphase waren die flow-bezogenen Unterschiede weniger deutlich. In a market characterised by time and cost constraints, translators must be able to work efficiently while at the same time performing at their best. Translators can achieve this balance if they enter into a cognitive state described as flow. In translation studies, flow has not yet been studied in detail. In this paper, process-based indicators were identified that could be used to investigate states of flow in the translation process. These indicators were used as the basis for drawing flow-related comparisons between translators possessing varying levels of experience, between different texts and between different parts of the drafting phase in the translation process. These comparisons aim to sharpen the focus of future studies. Verbal and observational data from translation processes were examined using a selection of indicators drawn from the available research literature. It was found that, in addition to verbal data, indicators in keystroke-logging data also seem to provide some indications of flow, while the selected indicators in eye-tracking data yielded inconclusive results. Furthermore, the results suggest that professional translators experience flow more often than master's students. Flow-related differences between the texts and between the parts of the drafting phase examined in this thesis were not as readily apparent

Electrochemical ruthenium-catalysed C–H activation in water through heterogenization of a molecular catalyst

Efficient catalytic oxidative C–H activation of organic substrates remains an important challenge in synthetic chemistry. Here, we show that the combination of a transition metal catalyst, surface immobilisation and an electrochemical potential provide a promising approach to effecting these transformations in aqueous solution. A ruthenium-based molecular catalyst [Ru(tpy)(pic-PO3H2)(Cl)] (where tpy is 2,2′:6′,2′′-terpyridine, pic-PO3H2 is 4-phosphonopyrid-2-ylcarboxylic acid) was synthesised and fully characterised. Oxidation of benzyl alcohol with the catalyst in aqueous media using ceric ammonium nitrate as terminal oxidant resulted in a rapid deactivation of the catalyst. Immobilisation of the catalyst on a mesoporous indium tin oxide electrode surface through the phosphonate anchoring group was shown to circumvent the issues observed in solution. Using the heterogeneous catalyst system, the oxidation of a variety of organic substrates with varying bond dissociation energies was demonstrated with turnover numbers of up to 346. Finally, surface-analysis of the functionalised electrodes after catalysis revealed that fragmentation of the complex during the reaction was the limiting factor for catalytic performance

11C-PET imaging reveals transport dynamics and sectorial plasticity of oak phloem after girdling

Carbon transport processes in plants can be followed non-invasively by repeated application of the short-lived positron-emitting radioisotope (11)C, a technique which has rarely been used with trees. Recently, positron emission tomography (PET) allowing 3D visualization has been adapted for use with plants. To investigate the effects of stem girdling on the flow of assimilates, leaves on first order branches of two-year-old oak (Quercus robur L.) trees were labeled with (11)C by supplying (11)CO2-gas to a leaf cuvette. Magnetic resonance imaging gave an indication of the plant structure, while PET registered the tracer flow in a stem region downstream from the labeled branches. After repeated pulse labeling, phloem translocation was shown to be sectorial in the stem: leaf orthostichy determined the position of the phloem sieve tubes containing labeled (11)C. The observed pathway remained unchanged for days. Tracer time-series derived from each pulse and analysed with a mechanistic model showed for two adjacent heights in the stem a similar velocity but different loss of recent assimilates. With either complete or partial girdling of bark within the monitored region, transport immediately stopped and then resumed in a new location in the stem cross-section, demonstrating the plasticity of sectoriality. One day after partial girdling, the loss of tracer along the interrupted transport pathway increased, while the velocity was enhanced in a non-girdled sector for several days. These findings suggest that lateral sugar transport was enhanced after wounding by a change in the lateral sugar transport path and the axial transport resumed with the development of new conductive tissue.We thank the Research Foundation – Flanders (FWO) for the PhD funding granted to Veerle De Schepper, the scientific research committee (CWO) of the Faculty of bioscience engineering (UGent) to support the research visit of Veerle De Schepper at the Forschungszentrum Jülich and the Special Research Fund (B.O.F.) of Ghent University for the post-doc funding granted to Veerle De Schepper

Quantitative 3D Analysis of Plant Roots Growing in Soil Using Magnetic Resonance Imaging

Precise measurements of root system architecture traits are an important requirement for plant phenotyping. Most of the current methods for analyzing root growth require either artificial growing conditions (e.g. hydroponics), are severely restricted in the fraction of roots detectable (e.g. rhizotrons), or are destructive (e.g. soil coring). On the other hand, modalities such as magnetic resonance imaging (MRI) are noninvasive and allow high-quality three-dimensional imaging of roots in soil. Here, we present a plant root imaging and analysis pipeline using MRI together with an advanced image visualization and analysis software toolbox named NMRooting. Pots up to 117 mm in diameter and 800 mm in height can be measured with the 4.7 T MRI instrument used here. For 1.5 l pots (81 mm diameter, 300 mm high), a fully automated system was developed enabling measurement of up to 18 pots per day. The most important root traits that can be nondestructively monitored over time are root mass, length, diameter, tip number, and growth angles (in two-dimensional polar coordinates) and spatial distribution. Various validation measurements for these traits were performed, showing that roots down to a diameter range between 200 μm and 300 μm can be quantitatively measured. Root fresh weight correlates linearly with root mass determined by MRI. We demonstrate the capabilities of MRI and the dedicated imaging pipeline in experimental series performed on soil-grown maize (Zea mays) and barley (Hordeum vulgare) plants

Model based data analysis and design of tracer transport experiments in plants

In tracer transport experiments in plants the distribution of tracer is observed over time within different parts of the plants, resulting in spatio-temporal tracer profiles. The distribution of tracer is caused by plant internal transport mechanisms which cannot be measured directly in a straightforward way. For this reason, inverse modeling is required in order to quantify certain transport properties like tracer transport velocity or leakage along the pathway. Compartmental tracer transport models can be used to quantitatively characterize or compare such data sets derived from different experiments. In this work, a general class of compartmental tracer transport models is presented which allows a systematic comparison of different models regarding the quality of fitting to the experimental data. This model class is defined by a system of partial differential equations for an arbitrary number of parallel compartments. Model parameters are transport velocities and diffusion coefficients inside the compartments as well as numerous lateral exchange connections between compartments. A large number of model instances with adjustable complexity can be derived from this model class by permitting only certain model parameters to be non-zero. For the solution of the partial differential equations, different finite volumes schemes up to fifth order for spatial discretization were compared with respect to accuracy, computation time and numerical oscillations. The comparison was performed using initial conditions with varying steepness. For smooth initial conditions, fifth order upwind schemes yielded the most precise and fast solutions. For higher steepness of the initial condition, higher order upwind schemes produced spurious oscillations while flux limiter schemes as well as weighted essentially non-oscillating schemes can suppress these oscillations, at the expense of comparably slower convergence rates and higher computation times. Several candidate models were successfully fitted to experimental positron emission tomography data of sugar beet, radish and maize root. A Monte Carlo multi-start strategy was used to approximate the global optimum within a certain parameter space. The analysis resulted in different best models depending on the respective data and the required fit quality. While long-distance transport of tracer isotopes in plants offer a high potential for functional phenotyping, measurement time is a bottleneck because continuous time series of at least 1 hour are required to obtain reliable estimates of transport properties. Hence, usual throughput values are between 0.5 and 1 samples h-1. In this work, increasing sample throughput is proposed by introducing temporal gaps in the data acquisition of each plant sample and measuring multiple plants one after each other in a rotating scheme. This results in interrupted time series which can be analyzed by the compartmental tracer transport models. The uncertainties of the model parameter estimates are used as a measure of how much information was lost compared to complete time series. Three case studies were set up to systematically investigate different experimental schedules for different throughput scenarios ranging from 1 to 12 samples h-1. Selected designs with only a small amount of data points were found to be sufficient for an adequate parameter estimation, implying that the presented approach enables a substantial increase of sample throughput. The presented general framework for automated generation and evaluation of experimental schedules allows the determination of a maximal sample throughput and the respective optimal measurement schedule depending on the required statistical reliability of data acquired by future experiments

Model based data analysis and design of tracer transport experiments in plants

In tracer transport experiments in plants the distribution of tracer is observed over time within different parts of the plants, resulting in spatio-temporal tracer profiles. The distribution of tracer is caused by plant internal transport mechanisms which cannot be measured directly in a straightforward way. For this reason, inverse modeling is required in order to quantify certain transport properties like tracer transport velocity or leakage along the pathway. Compartmental tracer transport models can be used to quantitatively characterize or compare such data sets derived from different experiments. In this work, a general class of compartmental tracer transport models is presented which allows a systematic comparison of different models regarding the quality of fitting to the experimental data. This model class is defined by a system of partial differential equations for an arbitrary number of parallel compartments. Model parameters are transport velocities and diffusion coefficients inside the compartments as well as numerous lateral exchange connections between compartments. A large number of model instances with adjustable complexity can be derived from this model class by permitting only certain model parameters to be non-zero. For the solution of the partial differential equations, different finite volumes schemes up to fifth order for spatial discretization were compared with respect to accuracy, computation time and numerical oscillations. The comparison was performed using initial conditions with varying steepness. For smooth initial conditions, fifth order upwind schemes yielded the most precise and fast solutions. For higher steepness of the initial condition, higher order upwind schemes produced spurious oscillations while flux limiter schemes as well as weighted essentially non-oscillating schemes can suppress these oscillations, at the expense of comparably slower convergence rates and higher computation times. Several candidate models were successfully fitted to experimental positron emission tomography data of sugar beet, radish and maize root. A Monte Carlo multi-start strategy was used to approximate the global optimum within a certain parameter space. The analysis resulted in different best models depending on the respective data and the required fit quality. While long-distance transport of tracer isotopes in plants offer a high potential for functional phenotyping, measurement time is a bottleneck because continuous time series of at least 1 hour are required to obtain reliable estimates of transport properties. Hence, usual throughput values are between 0.5 and 1 samples h-1. In this work, increasing sample throughput is proposed by introducing temporal gaps in the data acquisition of each plant sample and measuring multiple plants one after each other in a rotating scheme. This results in interrupted time series which can be analyzed by the compartmental tracer transport models. The uncertainties of the model parameter estimates are used as a measure of how much information was lost compared to complete time series. Three case studies were set up to systematically investigate different experimental schedules for different throughput scenarios ranging from 1 to 12 samples h-1. Selected designs with only a small amount of data points were found to be sufficient for an adequate parameter estimation, implying that the presented approach enables a substantial increase of sample throughput. The presented general framework for automated generation and evaluation of experimental schedules allows the determination of a maximal sample throughput and the respective optimal measurement schedule depending on the required statistical reliability of data acquired by future experiments