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In the context of global warming, an increase in atmospheric aridity and global dryland expansion under the future climate has been expected in previous studies. However, this conflicts with observed greening over drylands and the insignificant increase in hydrological and ecological aridity from the ecohydrology perspective. Combining climatic, hydrological, and vegetation data, this study evaluated global dryland aridity changes at meteorological stations from 2003 to 2019. A decoupling between atmospheric, hydrological, and vegetation aridity was found. Atmospheric aridity represented by the vapor pressure deficit (VPD) increased, hydrological aridity indicated by machine-learning-based precipitation minus evapotranspiration (P − ET) data did not change significantly, and ecological aridity represented by the leaf area index (LAI) decreased. P − ET showed nonsignificant changes in most of the dominant combinations of the VPD, LAI, and P − ET. This study highlights the added value of using station-scale data to assess dryland change as a complement to results based on coarse-resolution reanalysis data and land surface models.

Using statistical methods that do not directly represent the causality between variables to attribute climate and plant traits as controlling ecosystem functions may lead to biased perceptions. We revisited this issue using a causal graphical model, the Bayesian network (BN), capable of quantifying causality by conditional probability tables. Based on expert knowledge and climate, vegetation, and ecosystem function data from the FLUXNET flux stations, we constructed a BN representing the causal relationship of climate–plant-trait–ecosystem functions. Based on the sensitivity analysis function of the BN, we attributed the control of climate and plant traits over ecosystem functions and compared the results with those based on random forests and correlation analysis. The main conclusions of this study include the following: BN can be used for the quantification of causal relationships between complex ecosystems in response to climate change and enables the analysis of indirect effects among variables. The causality reflected in the BN is as good as the expert knowledge of the causal links. Compared to BN, the feature importance difference between “mean vapor pressure deficit and cumulative soil water index” and “maximum leaf area index and maximum vegetation height” reported by random forests is higher and can be overestimated. With the causality relation between correlated variables constructed, a BN-based sensitivity analysis can reduce the uncertainty in quantifying the importance of correlated variables. The understanding of the mechanism of indirect effects of climate variables on ecosystem functions through plant traits can be deepened by the chain casuality quantification in BNs.

With the rapid accumulation of water flux observations from global eddy-covariance flux sites, many studies have used data-driven approaches to model water fluxes, with various predictors and machine learning algorithms used. However, it is unclear how various model features affect prediction accuracy. To fill this gap, we evaluated this issue based on records of 139 developed models collected from 32 such studies. Support vector machines (SVMs; average R-squared = 0.82) and RF (random forest; average R-squared = 0.81) outperformed other evaluated algorithms with sufficient sample size in both cross-study and intra-study (with the same data) comparisons. The average accuracy of the model applied to arid regions is higher than in other climate types. The average accuracy of the model was slightly lower for forest sites (average R-squared = 0.76) than for croplands and grasslands (average R-squared = 0.8 and 0.79) but higher than for shrubland sites (average R-squared = 0.67). Using Rn/Rs, precipitation, Ta, and the fraction of absorbed photosynthetically active radiation (FAPAR) improved the model accuracy. The combined use of Ta and Rn/Rs is very effective, especially in forests, while in grasslands the combination of Ws and Rn/Rs is also effective. Random cross-validation showed higher model accuracy than spatial cross-validation and temporal cross-validation, but spatial cross-validation is more important in spatial extrapolation. The findings of this study are promising to guide future research on such machine-learning-based modeling.

Net ecosystem exchange (NEE) is an important indicator of carbon cycling in terrestrial ecosystems. Many previous studies have combined flux observations and meteorological, biophysical, and ancillary predictors using machine learning to simulate the site-scale NEE. However, systematic evaluation of the performance of such models is limited. Therefore, we performed a meta-analysis of these NEE simulations. A total of 40 such studies and 178 model records were included. The impacts of various features throughout the modeling process on the accuracy of the model were evaluated. Random forests and support vector machines performed better than other algorithms. Models with larger timescales have lower average R2 values, especially when the timescale exceeds the monthly scale. Half-hourly models (average R2 = 0.73) were significantly more accurate than daily models (average R2 = 0.5). There are significant differences in the predictors used and their impacts on model accuracy for different plant functional types (PFTs). Studies at continental and global scales (average R2 = 0.37) with multiple PFTs, more sites, and a large span of years correspond to lower R2 values than studies at local (average R2 = 0.69) and regional (average R2 = 0.7) scales. Also, the site-scale NEE predictions need more focus on the internal heterogeneity of the NEE dataset and the matching of the training set and validation set.

Interactive picturebook reading is an easily accessible and enjoyable activity that provides preschoolers with opportunities to practice problem-solving skills. Increasingly, preschool age is considered the ideal time to foster problem-solving skills. This paper examines the effect of interactive picturebook reading on the problem-solving skills of preschool children. A total of 130 preschool children between 4 and 6 years old participated in a pretest-posttest quasi-experiment with a non-equivalent control group. During five interactive reading sessions in small groups, preschoolers were invited to discuss possible solutions to the problems the book characters encounter. Problem-solving skills were measured with two Pictorial Multiple-Solutions Tasks. Multilevel analysis indicates that interactive picturebook reading can improve children's flexibility, fluency, and originality in coming up with possible solutions to a problem. In addition, we found that the picture that was used and the emotional reactions of the children to the test situation influenced the test scores. The results show that giving children the opportunity to discuss possible solutions to problems presented in picturebooks is an effective way to work on problem-solving skills in preschool. Further research is needed to explore the optimal conditions for effective interactive picturebook reading to develop preschoolers' problem-solving skills and the extent to which the skills demonstrated during interactive picturebook reading can be transferred to real-life problem solving.

To allow for climate impact studies on human and natural systems, high-resolution climate information is needed. Over some parts of the world plenty of regional climate simulations have been carried out, while in other regions hardly any high-resolution climate information is available. The CORDEX Central Asia domain is one of these regions, and this article describes the evaluation for two regional climate models (RCMs), REMO and ALARO-0, that were run for the first time at a horizontal resolution of 0.22∘ (25 km) over this region. The output of the ERA-Interim-driven RCMs is compared with different observational datasets over the 1980–2017 period. REMO scores better for temperature, whereas the ALARO-0 model prevails for precipitation. Studying specific subregions provides deeper insight into the strengths and weaknesses of both RCMs over the CAS-CORDEX domain. For example, ALARO-0 has difficulties in simulating the temperature over the northern part of the domain, particularly when snow cover is present, while REMO poorly simulates the annual cycle of precipitation over the Tibetan Plateau. The evaluation of minimum and maximum temperature demonstrates that both models underestimate the daily temperature range. This study aims to evaluate whether REMO and ALARO-0 provide reliable climate information over the CAS-CORDEX domain for impact modeling and environmental assessment applications. Depending on the evaluated season and variable, it is demonstrated that the produced climate data can be used in several subregions, e.g., temperature and precipitation over western Central Asia in autumn. At the same time, a bias adjustment is required for regions where significant biases have been identified.

The previous comparative studies on watersheds were mostly based on the comparison of dispersive characteristics, which lacked systemicity and causality. We proposed a causal structure-based framework for basin comparison based on the Bayesian network (BN) and focus on the basin-scale water–energy–food–ecology (WEFE) nexus. We applied it to the Syr Darya River basin (SDB) and the Amu Darya River basin (ADB), of which poor water management caused the Aral Sea disaster. The causality of the nexus was effectively compared and universality of this framework was discussed. In terms of changes in the nexus, the sensitive factor for the water supplied to the Aral Sea changed from the agricultural development during the Soviet Union period to the disputes in the WEFE nexus after the disintegration. The water–energy contradiction of the SDB is more severe than that of the ADB, partly due to the higher upstream reservoir interception capacity. It further made management of the winter surplus water downstream of the SDB more controversial. Due to this, the water–food–ecology conflict between downstream countries may escalate and turn into a long-term chronic problem. Reducing water inflow to depressions and improving the planting structure prove beneficial to the Aral Sea ecology, and this effect of the SDB is more significant. The construction of reservoirs on the Panj River of the upstream ADB should be cautious to avoid an intense water–energy conflict such as the SDB's. It is also necessary to promote the water-saving drip irrigation and to strengthen the cooperation.

Agriculture is one of the most critical sectors of the Mongolian economy. In Mongolia, land degradation is increasing in the cropland region, especially in a cultivated area. The country has challenges to identify new croplands with sufficient capacity for cultivation, especially for local decision-makers. GIS applications tremendously help science in making land assessments. This study was carried out in Bornuur soum, Mongolia. The goal of this study to estimate that best suitable area for supporting crop production in Bornuur soum, using a GIS-based multi-criteria analysis (MCA) and remote sensing. GIS-based multi-criteria analysis (MCA) has been widely used in land suitability analyses in many countries. In this research, the GIS-based spatial MCA among the Analytical Hierarchy Process (AHP) method has employed. The approach was enhanced for each criterion which as soil, topography and vegetation. The opinions of agronomist experts and a literature review helped in identifying criteria (soil data, topography, water and vegetation data) that are necessary to determine areas suitable for crops. The detailed cropland suitability maps indicate that 46.12 % is highly suitable for cropland, 34.68 % is moderate suitable, 13.64 % is marginal suitable and 5.56 % is not suitable. The MCA and AHP tools play an essential role in the multi-criteria analysis. Therefore, the results of these methods allow us to estimate an appropriate area for cultivation in Bornuur soum, Tuv province. The crop suitability method implies significant decisions on different levels and the result will be used for cropland management plan to make a decision. It is an integral role in agricultural management and land evaluation. Future research should further develop this method by including socio-economic (potential citizens for agriculture, current crop growth, water resource, etc.) and environmental variables (rainfall, vegetation types, permafrost distribution, etc.) to obtain specific results. However, it could be also be applied for a single crop type (mainly barley, wheat and potato) in Mongolia.

The successful conduct of a rescue mission in urban areas is directly related to the timely deployment of equipment and personnel to the incident location which justifies the quest for optimum path selection for emergency purposes. In this study, it is attempted to use Ant Colony Optimization (ACO) to find the optimum paths between fire stations and incident locations. It is also attempted to build up an evaluation tool using ACO to detect critical road segments that the overall accessibility to fire station services throughout the urban area is constituted upon their excellent functionality. Therefore, an ACO solution is designed to find optimum paths between the fire station and some randomly distributed incident locations. Regarding different variants of ACO, the algorithm enjoys the Simple Ant Colony Optimization deployment strategy combined with Ant Algorithm Transition rules. Iteration best pheromone updating is also used as the pheromone reinforcement strategy. The cost function used to optimize the path considers the shortest Euclidean distance on the network. The results explicitly state that the proposed method is successful to create the optimum path in 95.45 percent of all times, compared to Dijkstra deterministic approaches. Moreover, the pheromone map as an indicator of the criticality of road elements is generated and discussed. Visual inspection shows that the pheromone map is verified as the road criticality map concerning fire station access to the region and therefore pre-emptive measures can be defined by analyzing the generated pheromone map.

The shortest path problem has been studied to be solved through diverse deterministic and also stochastic approaches such as Ant Colony Optimization. One of the most challenging issues with the implication of Ant Colony Optimization to solve the shortest path problem is parameter selection and tuning which is found crucial to improve the computational performance of problem-solving. To tune parameters, it is vital to observe the response of each parameter to different values and study their effect on the final results. In this research, two experiments are designed and conducted to study the behavior of parameters in terms of generated results and computational performance. In the first experiment, evaporation, updating, and transition rule parameters are studied by iterative execution of shortest path generation between nodes considering different parameter values. In the second experiment, the number of initial ants is studied. Inspecting the results, it is observed that to avoid premature stagnation decreasing α value is recommended. On the other hand, ρ is observed to be considered for tuning of speed and number of diffusions of the algorithm. Moreover, it is realized that a high Q value would result in more correct results. Inspecting the initial number of ants, a threshold is realized where increasing the number of ants over this threshold would drastically result in more optimized paths.

The forest biomass is one of the most important parameters for the global carbon stock. Information on the forest volume, coverage and biomass are important to develop the global perspective on the CO2 concentration changes. Objective of this research is to estimate forest biomass in the study area. The study area is Hangal sum, Bulgan province, Mongolia. Backscatter coefficients for vertical transmit and vertical receive (VV), for vertical transmit and horizontal receive (VH) from Sentinel data and Leaf Area Index (LAI) from Landsat data were used in the study area. We developed biomass estimation approach using ground truth data which is DBH, height and soil moisture. The coefficient α, β, δ, γ were found from the approach. The output map from the approach was compared with VV and VH, LAI data. The relationship between output map and VH data shows a positive result R2 = 0.61. This study suggests that the biomass estimation using Remote sensing data can be applied in forest region in the North.

Writing a synthesis text involves interacting reading and writing processes, serving the comprehension of source information, and its integration into a reader-friendly and accurate synthesis text. Mastering these processes requires insight into process' orchestrations. A way of achieving this is via process feedback in which students compare their process orchestration with examples. Access to such examples of enacted process orchestration models might have an additional learning effect. In the present study we replicated and extended the study of Vandermeulen et al. ("Written Communication," 40(1), 90-144, 2023) on the effect of keystroke logging data-based process feedback with feed-forward exemplars when compared to national baseline performances. In addition, we report the effect of a brief extension in which learners had the opportunity to observe an enacted model of their choice, showing one of three orchestrations of the initial stage of writing a synthesis task. A total of 173 10th-grade students were randomly assigned to a process feedback condition with or without added models. A baseline, consisting of a nationally representative sample of upper-secondary students' texts and processes, served as an alternative control group. Results showed that the process feedback, both with and without observation, had a significant effect on text quality. Regarding the process data, students in the feedback condition had a more prominent focus on the sources as they spent more time in them and switched more often between text and sources, compared to the baseline. The observation task magnified this effect.

Increasing urbanisation, changes in land use (e.g., more impervious area) and climate change have all led to an increasing frequency and severity of flood events and increased socio-economic impact. In order to deploy an urban flood disaster and risk management system, it is necessary to know what the consequences of a specific urban flood event are to adapt to a potential event and prepare for its impact. Therefore, an accurate socio-economic impact assessment must be conducted. Unfortunately, until now, there has been a lack of data regarding the design and construction of flood-prone building structures (e.g., locations and dimensions of doors and door thresholds and presence and dimensions of basement ventilation holes) to consider when calculating the flood impact on buildings. We propose a pipeline to detect the dimension and location of doors and windows based on mobile LiDAR data and 360° images. This paper reports on the current state of research in the domain of object detection and instance segmentation of images to detect doors and windows in mobile LiDAR data. The use and improvement of this algorithm can greatly enhance the accuracy of socio-economic impact of urban flood events and, therefore, can be of great importance for flood disaster management.

Soil moisture (SM) content is one of the most important environmental variables in relation to land surface climatology, hydrology, and ecology. Long-term SM data-sets on a regional scale provide reasonable information about climate change and global warming specific regions. The aim of this research work is to develop an integrated methodology for SM of kastanozems soils using multispectral satellite data. The study area is Tuv (48°40′30″N and 106°15′55″E) province in the forest steppe zones in Mongolia. In addition to this, land surface temperature (LST) and normalized difference vegetation index (NDVI) from Landsat satellite images were integrated for the assessment. Furthermore, we used a digital elevation model (DEM) from ASTER satellite image with 30-m resolution. Aspect and slope maps were derived from this DEM. The soil moisture index (SMI) was obtained using spectral information from Landsat satellite data. We used regression analysis to develop the model. The model shows how SMI from satellite depends on LST, NDVI, DEM, Slope, and Aspect in the agricultural area. The results of the model were correlated with the ground SM data in Tuv province. The results indicate that there is a good agreement between output SM and SM of ground truth for agricultural area. Further research is focused on moisture mapping for different natural zones in Mongolia. The innovative part of this research is to estimate SM using drivers which are vegetation, land surface temperature, elevation, aspect, and slope in the forested steppe area. This integrative methodology can be applied for different regions with forest and desert steppe zones.

The Small Island Developing States are characterized by an unstable economy and low-lying, densely populated cities, resulting in a high vulnerability to natural hazards. Flooding affects more people than any other hazard. To limit the consequences of these hazards, adequate risk assessments are indispensable. Satisfactory input data for these assessments are hard to acquire, especially in developing countries. Therefore, in this study, a methodology was developed and evaluated to test the sensitivity of a flood model towards its input data in order to determine a minimum set of indispensable data. In a first step, a flood damage assessment model was created for the case study of Annotto Bay, Jamaica. This model generates a damage map for the region based on the flood extent map of the 2001 inundations caused by Tropical Storm Michelle. Three damages were taken into account: building, road and crop damage. Twelve scenarios were generated, each with a different combination of input data, testing one of the three damage calculations for its sensitivity. One main conclusion was that population density, in combination with an average number of people per household, is a good parameter in determining the building damage when exact building locations are unknown. Furthermore, the importance of roads for an accurate visual result was demonstrated.

To writing anything on a keyboard at all requires us to know first what to type, then to activate motor programmes for finger movements, and execute these. An interruption in the information flow at any of these stages leads to disfluencies. To capture this combination of fluent typing and typing hesitations, researchers calculate different measures from keystroke-latency data--such as mean inter-keystroke interval and pause frequencies. There are two fundamental problems with this: first, summary statistics ignore important information in the data and frequently result in biased estimates; second, pauses and pause-related measures are defined using threshold values which are, in principle, arbitrary. We implemented a series of Bayesian models that aimed to address both issues while providing reliable estimates for individual typing speed and statistically inferred process disfluencies. We tested these models on a random sample of 250 copy-task recordings. Our results illustrate that we can model copy typing as a mixture process of fluent and disfluent key transitions. We conclude that mixture models (1) map onto the information cascade that generate keystrokes, and (2) provide a principled approach to detect disfluencies in keyboard typing.

Science education is increasingly integrating a focus on socio-scientific issues. Policy makers and scholars also suggest education for sustainable development (ESD) is important for tackling current and future sustainability issues. ESD may foster students' action competence in sustainable development (ACiSD; knowledgeability, willingness, capacity expectations and outcome expectancy) regarding contributing to sustainability action. However, reports on effectiveness research relating to ACiSD as a learning outcome of action-oriented ESD are scarce. The current study explores (1) how early adolescents perceive their teachers' ESD implementation and (2) whether perceived ESD (holism, pluralism, and action-orientedness) affects students' self-reported ACiSD. We administered two questionnaires to 12-16 year-old secondary school students, tapping into (1) their self-reported ACiSD and (2) their perceptions of teachers' ESD implementation efforts. Students' ESD perceptions were measured through descriptive statistics. Next, multilevel linear models were estimated to explore whether ESD and its components affect students' ACiSD. Our results suggest students did not distinctly perceive ESD implementation (esp. a participative approach). Holism and action-orientedness were neither perceived to be completely present nor completely missing. However, action-orientedness significantly affected students' reported ACiSD. While confirming the challenge an ESD implementation poses, our results may support efforts made. Implications for education and teacher development are discussed.

Serial transnational World Heritage nominations are challenging the way cultural heritage has been managed and evaluated in the past. Serial transnational World Heritage nominations are unique in that they consist of multiple sites listed as one property, distributed in different countries, involving a large diversity of stakeholders in the process. As a result, there is a need for precise baseline information for monitoring, reporting and decision making. This type of nomination requires different methodologies and tools to improve the monitoring cycle from the beginning of the nomination towards the periodic reporting. The case study of the Silk Roads Cultural Heritage Resource Information System (CHRIS) illustrates the use of a Geographical Content Management System (Geo-CMS) supporting the serial transnational World Heritage nomination and the monitoring of the Silk Roads in the five Central Asian countries. The Silk Roads CHRIS is an initiative supported by UNESCO World Heritage Centre (WHC) and the Belgian Federal Science Policy Office (BELSPO), and developed by a consortium headed by the Raymond Lemaire International Centre for Conservation (RLICC) at the KULeuven. The Silk Roads CHRIS has been successfully assisting in the preparation of the nomination dossiers of the Republics of Kazakhstan, Tajikistan and Uzbekistan and will be used as a tool for monitoring tool in the Central Asian countries.

This paper discusses the application of Information Management Systems (IMS) for documenting and monitoring World Heritage (WH) properties. The application of IMS in WH can support all stakeholders involved in conservation, and management of cultural heritage by more easily inventorying, mining and exchanging information from multiple sources based on international standards. Moreover, IMS could assist in detecting damages and preparing management strategies to mitigate risks, and slowing down the deterioration of the integrity of WH properties. The case study of the Silk Roads Cultural Heritage Resource Information System (CHRIS), a Belgian Federal Science Policy Office funded project, illustrates the capabilities of IMS in the context of the nomination of the Central Asian Silk Roads on the WH List. This multi-lingual, web-based IMS will act as a collaborative platform allowing for the completion of improved transnational nomination dossiers and subsequent monitoring activities with all necessary baseline information to easily verify consistency and quality of the proposal. The Silk Roads CHRIS Geospatial Content Management System uses open source technologies and allows to georeference data from different scales and sources including data from field recording methods and combine it with historical and heritage features documented through various means such as textual descriptions, documents, photographs, 3D models or videos. Moreover, tailored maps can also be generated by overlaying a selection of available layers and then be exported to support the nomination dossier. Finally, by using this innovative information and decision support system, the State Parties and other interested stakeholders will have access to a complete nomination dossier and could therefore respond more effectively to hazards and disaster phenomena.

This intervention study aimed to test the effect of writing process feedback. Sixty-five Grade 10 students received a personal report based on keystroke logging data, including information on several writing process aspects. Participants compared their writing process to exemplar processes of equally scoring (position-setting condition) or higher-scoring students (feed-forward condition). The effect of the feedback on writing performance and process was compared to a national baseline study. Results showed that feed-forward process feedback had an effect on text quality comparable to one grade of regular schooling. The feedback had an effect on production, pausing, revision, and source use, which indicates that it supported participants in self-regulating their writing process. Additionally, we explored the students' perception of the feedback to get an insight into its strengths and weaknesses. This study shows the potential of writing process feedback and discusses pedagogical implications and options for future research.

In order to support research on school effectiveness, there is a need for valid and reliable instruments to assess policymaking capacities of schools. Increasingly, policymaking is seen as a shared responsibility of the entire pedagogical team of a school. In this article, data were analysed from a sample of 1,696 (care) teachers coordinators and principals from 77 Flemish primary schools to assess critical aspects concerning validity and reliability of the Policy Making Capacities Questionnaire (PMC-Q). Confirmatory factor analysis indicates that the fit for the model with eight factors is limited. The subscales were found to be strongly correlated and difficult to distinguish, and one of the subscales turned out to be not well captured by the questionnaire. The results indicate sufficient interrater reliability and within-group agreement to aggregate responses to school level. Suggestions are made for further development of the PMC-Q and its use for research and practice.

Completing questionnaires is a complex task in which multiple cognitive processes play an important role. Despite critiques of the reliability of questionnaires, our research strived to gain more insight into the process of completing questionnaires by using eye-tracking. We investigated how both the question and the categories of answers were being processed and how processing the question influenced the processing of the categories of answers. We also considered the effects of participants' personal characteristics, such as effort and ability. Results showed there were individual differences involved when respondents complete a questionnaire. The respondents spent different amounts of time reading and answering the items, and there were important differences in the quality of processing.

This study investigates the differential effects of a task-based inside-school intervention and an outside-school project intervention on the development of speaking skills of 56 beginner second language (L2)-learners. The study extends previous research by looking at development across multiple occasions instead of two (i.e., a pretest and posttest), and by assessing speaking development by means of a non-intrusive task eliciting an informal conversation, rather than a formal test. To measure students' development of Dutch speaking skills, syntactic complexity, lexical diversity, accuracy, fluency, and communicative effectiveness were assessed. Multilevel analyses revealed differential effects for both interventions. The task-based intervention inside school led to significant positive development of communicative effectiveness and syntactic complexity measured by length and subordination, but also to more breakdowns in fluency. The outside-school project intervention, however, did not result in any significant speaking development. The findings demonstrate the learning potential of a task-based inside-school intervention for developing various aspects of informal speaking skills, except for fluency. From a methodological perspective, this study illustrates the complexity of test and measure selection when assessing L2 speaking development.

In this study we examine process configurations in synthesis tasks. We study whether these configurations are students traits or vary within students per task. In a national survey with a representative sample of 658 Dutch upper-secondary school students, we collected writing tasks, registered students' writing behaviors (via keylogging) and their task perceptions and assessed the quality of their texts. Each participant completed two informative and two argumentative synthesis tasks. Writing process configurations were based on a preselected set of writing behaviors that proved to be related to text quality: time spent on sources and production activities, switching between sources and between sources and text production, and speed of production; with reference to the phase in the process (first, mid, final part). Latent profile analyses distilled four process configurations, some of which were more likely to occur with the informative genre. One process configuration, that is, "Fast text production," was related to qualitatively higher text quality scores than the others. Additionally, at the age of 16-18 a writing process configuration is not a student trait: in most instances, we observed two or more task configurations within students. Writers' task experiences such as topic knowledge and topic interest predicted the occurrence of certain process configurations which could indicate adaptivity. The finding that writing configurations of writers vary even between similar tasks has important implications for the generalizability of (synthesis) writing research on the basis of a single writing task and process per student.

In this study, we investigate the relation between the school organization and outcomes of education for sustainable development, namely students' action competence in sustainable development. Data were collected among 629 students. At the organizational level, we collected data among 207 school staff concerning organizational characteristics including school resources, sustainable leadership, pluralistic communication, supportive relations, collective efficacy, adaptability, democratic decision-making, and shared vision. Results indicate that the school organization is primarily connected to students' knowledge and willingness. They also show that organizational characteristics such as sustainable leadership, pluralistic communication, adaptability, and democratic decision-making have a significant correlation with student level outcomes.

The discourse of quality has permeated everyday discussions about education, yet the concept as such remains complex. Moreover, during the constant struggle to improve educational quality, governments have increasingly devoted attention to the results of international large-scale assessments. As those results are used to inform policy decisions to improve countries' educational quality, it is important to understand how this concept of quality is appropriated. As a synthesis of how quality is conceptualised and operationalised in this body of research is lacking, the current paper seeks to fill that gap by presenting a systematic review. Our analysis identified definitional patterns and explored the corresponding methods for measuring quality. Based on this analysis of the reviewed studies, a framework of four approaches towards quality emerged to render the concept more accessible. In light of these results, benefits and limitations of the concept's complexity are discussed, and implications for research are forwarded.

Based on the concept of action competence, the current study introduces an integrated framework of Professional Action Competence in Education for Sustainable Development (PACesd) described by three defining features: willingness, knowledge of pedagogical approaches, and self-efficacy. To validate this framework, we developed a questionnaire measurement instrument: 'PACesd-Q'. This questionnaire was administered to a sample of 557 primary and secondary school in-service teachers in Flanders, Belgium. The instrument's psychometric quality was assessed in terms of its reliability, construct, and criterion-related validity. Results of reliability analysis and hierarchical (second-order) confirmatory factor analysis provided evidence of good measurement properties of the instrument in terms of reliability and construct and criterion-related validity. We suggest the instrument can be successfully used to provide educational practitioners with a tool to monitor and eventually identify professional development needs regarding education for sustainable development.

Among other things, learning to write entails learning how to use complex sentences effectively in discourse. Some research has therefore focused on relating measures of syntactic complexity to text quality. Apart from the fact that the existing research on this topic appears inconclusive, most of it has been conducted in English L1 contexts. This is potentially problematic, since relevant syntactic indices may not be the same across languages. The current study is the first to explore which syntactic features predict text quality in Dutch secondary school students' argumentative writing. In order to do so, the quality of 125 argumentative essays written by students was rated and the syntactic features of the texts were analyzed. A multilevel regression analysis was then used to investigate which features contribute to text quality. The resulting model (explaining 14.5% of the variance in text quality) shows that the relative number of finite clauses and the ratio between the number of relative clauses and the number of finite clauses positively predict text quality. Discrepancies between our findings and those of previous studies indicate that the relations between syntactic features and text quality may vary based on factors such as language and genre. Additional (cross-linguistic) research is needed to gain a more complete understanding of the relationships between syntactic constructions and text quality and the potential moderating role of language and genre.

Educating student teachers to teach highly diverse groups of pupils has become one of the central tasks of teacher education programmes. Research on inclusive education has pointed to the need for more studies around student teachers' inclusive teaching competence. This study explores the extent to which student teachers' beliefs and self-efficacy related to inclusive teaching practices predict their professional vision of two inclusive classrooms' characteristics being (1) teacher-student interactions (TSI) and (2) differentiated instruction (DI). The data were collected from a sample of 1397 student teachers in Flemish primary teacher education institutions (N = 8). The video and survey data were combined through multiple regression analysis. The findings indicate that student teachers' constructivist beliefs and self-efficacy in inclusive instruction contribute to the development of their professional vision of TSI and DI. The results are discussed with recommendations for teacher education programmes on inclusive teaching practices.

The aim of this study is to provide -- first theoretically and, subsequently, through an empirical analysis -- a rationale for including the concept of overexcitability in talent research, beyond the five-factor model personality traits. Moreover, the empirical part of this study makes use of an innovative statistical method to address the problem of a frequentist approach to statistics in complex trait models which are based on personality questionnaire data. This study offers insight into the differential significance of overexcitability in relation to the established personality traits, emphasizing Dabrowski's dynamic approach to personality and the key contribution of overexcitability in the developmental process. Furthermore, implications for the field of giftedness are discussed.

Young students have raised their voices in debates on what action for sustainable development (SD) is necessary. Nevertheless, research that gives voice to 10 to 13-year-olds while looking into SD issues in all their complexity of interrelated environmental, social, and socio-economic perspectives, is scarce. This study aims to give voice to these youngsters, asking them directly how they suggest they can contribute to SD. Building on the concepts of action and SD, this qualitative study reports on early adolescents' own suggestions for action. Participants suggested direct, indirect, individual, and collective actions both in the private and public sphere. Their actions targeted SD issues with interconnections between areas concerning the planet, peace, people, partnership, and to some extent also prosperity. We compare our results with findings of earlier studies to further the discussion on how young people feel they can and want to contribute to SD.

This study starts from the observation that current empirical research on students' processing strategies in higher education has mainly focused on the use of self-report instruments to measure students' general preferences towards processing strategies. In contrast, there is a rather limited use of more direct and online observation techniques to uncover differences in processing strategies at a task specific level. We based our study on one of the most influential studies in the domain of Students' Approaches to Learning (SAL) (Marton, Dahlgren, Säljö, & Svensson, 1975). In our exploratory experiment we used eye tracking followed by a cued recall to investigate how students use processing strategies in learning from expository text. Nineteen university students participated in the experiment. Results suggested that students in the deep condition did not look longer at the essentials in the text compared with students in the surface condition, but that they processed them in a more deep way. In our sample, students in the surface condition looked longer at facts and details and also reported repeating these facts and details more often. We suggest that the combination of eye tracking followed by a cued recall is a promising tool to investigate students' processing strategies since not all differences in processing strategies are reflected in overt eye movement behaviour. The current methodology allows researchers in the domain of SAL to complement and extend the present knowledge base that has accumulated through years of research with self-report questionnaires and interviews on students' general preferences towards processing strategies.