Large-scale Assessment Research Articles

Evaluating the Stability of the Jianchuan Ancient Town with TerraSAR-X images

Abstract. Architectural heritage holds significant historical and cultural value. However, it faces substantial risks of deterioration stemming from natural aging and human activities. Traditional methods rely on manual surveys or ground-based measurements, which are time-consuming and labor-intensive, making it challenging to meet the demand for large-scale stability assessments of architectural heritage. In this study, we designed a methodology based on the analysis of differential deformation and angular distortion of buildings using InSAR line-of-sight (LOS) displacements. Then, the stability of buildings is categorized into three groups: stable, moderate, and unstable, using these stability indicators. A total of 74 TerraSAR-X images acquired from August 2017 to November 2019 in ascending orbit were used to monitor the Jianchuan Ancient Town, which has a history of over 650 years. Our evaluation reveals that among the 1891 architectures in Jianchuan Ancient Town, 1191 are rated as stable, 696 as moderate, and 4 as unstable. Time-series results indicate that one of the four unstable buildings exhibited a severe accelerating trend that requires attention. In conclusion, this methodology can enhance the capability to assess the stability of large-scale architectural heritages.

Evaluating German PISA stratification designs: a simulation study

Stratification is an important design feature of many studies using complex sampling designs and it is often used in large-scale assessment (LSA) studies, such as the Programme for International Student Assessment (PISA), for two main reasons. First, stratification variables that achieve a high between and low within strata variance can improve the efficiency of a survey design. Second, stratification allows one to, explicitly or implicitly, control for sample sizes across subpopulations. It ensures that some parts of a population are in the sample in predetermined proportions. In this study, we determine through simulation which stratification scheme is best for PISA in Germany. For this, we consider the constraints imposed by the international sampling design, the available information about schools, and specific national characteristics of the German educational system. We examine seven different stratification designs selected based on scenarios used in past LSAs in Germany and theoretical considerations for future implementations. The chosen scenarios were compared with two reference scenarios: (1) an unstratified design and (2) a synthetic optimal stratification design. The simulation study reveals that the stratification design currently applied in PISA produces satisfactory results regarding sampling precision. The present stratification design is based on Germany's federal states and school types. However, this approach leads to small strata, which has been problematic for estimating sampling variance in previous cycles. Therefore, alternative stratification scenarios were considered and, in addition to overcoming the small-strata problem, also led to smaller standard errors for estimates of student mean performance in mathematics, science, and reading. As a result of this study, we recommend considering three different stratification designs for Germany in future cycles of PISA. These recommendations aim to: (1) improve the sampling efficiency while keeping the sample size constant, (2) follow a sound methodological approach, and (3) make conservative and cautious changes while maintaining a reflection of the structure of the German federal school system with different school types. These suggestions include a reinvented stratification of grouped German federal states and designs with school types as explicit stratifiers and federal states as implicit stratifiers.

Fisheries Impact Pathway: Making Global and Regionalised Impacts on Marine Ecosystem Quality Accessible in Life Cycle Impact Assessment

Fisheries Impact Pathway: Making Global and Regionalised Impacts on Marine Ecosystem Quality Accessible in Life Cycle Impact Assessment

Assessing Face Image Quality: A Large-Scale Database and a Transformer Method.

The amount of face images has been witnessing an explosive increase in the last decade, where various distortions inevitably exist on transmitted or stored face images. The distortions lead to visible and undesirable degradation on face images, affecting their quality of experience (QoE). To address this issue, this paper proposes a novel Transformer-based method for quality assessment on face images (named as TransFQA). Specifically, we first establish a large-scale face image quality assessment (FIQA) database, which includes 42,125 face images with diversifying content at different distortion types. Through an extensive crowdsource study, we obtain 712,808 subjective scores, which to the best of our knowledge contribute to the largest database for assessing the quality of face images. Furthermore, by investigating the established database, we comprehensively analyze the impacts of distortion types and facial components (FCs) on the overall image quality. Accordingly, we propose the TransFQA method, in which the FC-guided Transformer network (FT-Net) is developed to integrate the global context, face region and FC detailed features via a new progressive attention mechanism. Then, a distortion-specific prediction network (DP-Net) is designed to weight different distortions and accurately predict final quality scores. Finally, the experiments comprehensively verify that our TransFQA method significantly outperforms other state-of-the-art methods for quality assessment on face images.

Evaluating teaching readiness using the TPACK model: factor, reliability and validity analyses for Indonesian economics teacher candidates

Introduction. The preparedness of future economics teachers matters as it affects the quality of education, student results, workforce training, critical thinking abilities, global mindset, college prep, and adjustment to shifting economies. The future generation needs educated people to act as members of society and the world economy; hence, it requires a cadre of trained economists and teachers and is significantly shaped by intrinsic factors such as technological, pedagogical, and content knowledge (TPACK). This study refines and validates a measurement instrument to reliably and validity assess the essential competencies of these teachers, tailored to meet the evolving standards of economics education. Materials and Methods. The study used an online questionnaire with a purposive sample of 100 pre-service economics educators from Indonesia for Explanatory Factor Analysis (EFA) and 389 samples for Confirmatory Factor Analysis (CFA), all of whom had completed their teaching practicums. Then, statistical analysis was conducted using EFA in SPSS and CFA in AMOS. The EFA was employed to determine how well the instrument’s items capture the components of the TPACK framework and their relevance to the prospective teachers’ readiness for professional practice post-graduation. The CFA is Performed to guarantee that each variable analyzed has the validity of the configuration and the reliability of the construct as needed and is used to establish the extent to which the measured variable may represent the construct or variable that has previously been considered. Results. This study first developed seven factors, namely Technological Knowledge (TK), Pedagogical Knowledge (PK), Content Knowledge (CK), Pedagogical Content Knowledge (PCK), Technological Pedagogical Knowledge (TPK), Technological Content Knowledge (TCK), and Technological, Pedagogical and Content Knowledge (TPACK), consisting of 39 items. However, four items were removed because their factor loading was below 0.50. As a result, 35 items were retained as the factor loading was significantly greater than .50. The Cronbach's alpha value for each item of the TPACK construct was > .60 and met all the criteria of goodness of fit index, which means that it meets the requirements and can measure the TPACK construct in measuring job readiness of prospective teachers in Indonesia. As this study fulfills the requirements of validity and reliability of the constructs that lead to TPACK, the results of this study will help prospective teachers prepare themselves to become professional economics teachers. Conclusion. The teaching readiness of prospective teachers can be assessed using valid and reliable tools such as EFA in pilot testing and CFA in large-scale analysis assessments of the TPACK construct with 35 question items.

Worldwide Examination of Magnetic Responses to Heavy Metal Pollution in Agricultural Soils

Over the last decade, a large number of studies have been conducted on heavy metals and magnetic susceptibility (χlf) measurement in soils. Yet, a global understanding of soil contamination and magnetic responses remains elusive due to the limited scope or sampling sites of these studies. Hence, we attempted to explore a pollution proxy on a global scale. Through a meta-analysis of data from 102 published studies, our research aimed to provide a worldwide overview of heavy metal pollution and magnetic responses in agriculture soils. We mapped the geographic distribution of nine heavy metals (Cr, Cu, Zn, Pb, Ni, As, Cd, Mn, and Fe) in agricultural soils and explored their pollution sources and contributions. Since 2011, The accumulation of heavy metals has escalated, with industrial activities (31.5%) being the largest contributor, followed by agricultural inputs (27.1%), atmospheric deposition (22.66%), and natural sources (18.74%). The study reports χlf ranging from 6.45 × 10−8 m3/kg to 319.23 × 10−8 m3/kg and χfd from 0.59% and 12.85%, with the majority of the samples being below 6%, indicating heavy metal influence mainly from human activities. Pearson’s correlation and redundancy analysis show significant positive correlations of Pb, Zn, and Cu with χlf (r = 0.51–0.53) and Mn and Fe with χfd (r = 0.50–0.53), while Pb, Zn, Cu, and As metals were shown to be key factors of variation in magnetic response. The average heavy metal pollution load index of 2.03 suggests moderate global agricultural soil pollution, with higher heavy metal contamination in areas of high χlf. Regression analysis confirms soil is considered to be non-polluted below χlf of 26×10−8 m3/kg and polluted above this threshold, with all contamination factors of metals showing a linear correlation with χlf (R = 0.72), indicating that a significant relationship between χlf and the geochemical properties of soils continues to exist on a global scale. This study provides new insights for large-scale agricultural soil quality assessment and magnetic response.

Challenges of Including Wet Grasslands with Variable Groundwater Tables in Large-Area Crop Production Simulations

Large-scale assessments of agricultural productivity necessitate integrated simulations of cropland and grassland ecosystems within their spatiotemporal context. However, simultaneous simulations face limitations due to assumptions of uniform species distribution. Grasslands, particularly those with shallow groundwater tables, are highly sensitive to water availability, undergoing rapid species composition changes. We hypothesised that predicting above-ground biomass (AGB) remains challenging due to these dynamic responses. Ten years of data from four lysimeters at a German wet grassland site, with varying water table treatments, was utilised to test this hypothesis. Correlation analysis revealed a strong positive indirect effect of the water regime on AGB, with a one-year time lag (r = 0.97). The MONICA model initially exhibited fair agreement (d = 0.69) in simulating Leaf-Area-Index (LAI) but performed poorly in replicating AGB (d = 0.3). After removing the species composition change effect from the LAI and AGB datasets, the simulation notably improved, with the overall relative root mean square error (rRMSE) of AGB decreasing from 1.55 to 0.90 between the first and second simulations. This demonstrates MONICA’s ability to predict grass growth patterns amidst changing water supply levels for constant species composition. However, it needs a competition model to capture biomass growth changes with varying water supply.

Satellite Imagery System for Pruning Vegetation Interference in Power Transmission Lines

Vegetation encroachment in power transmission lines can cause outages, which may result in severe impact on electricity board as well as the consumer. Vegetation detection and monitoring along the power lines are implemented to protect power transmission lines from vegetation interference. There were various methods used to monitor the vegetation interference with power transmission lines, however, most of them were too expensive and time consuming. Satellite images can play a pivotal role in vegetation monitoring, because it can cover high spatial area with relatively low cost. The current methods depend usually on setting manually threshold values and parameters which make the detection process very static. Machine Learning (ML) and deep learning (DL) algorithms can provide a very high accuracy with flexibility in the detection process. Hence the potential of using Deep Learning based algorithms are also included. The input data were derived from satellite images, UAV images and other aerial images. This work is significant because it shows how satellite images that are already commercially available can be used for the large-scale assessment of vegetation encroachment on transmission lines

A SUCCESS CASE

This research focuses on analyzing the results obtained by Municipal School in large-scale external assessments, such as SAEB. It is a qualitative and quantitative investigation, using documentary research and data analysis methodology. Furthermore, we will evaluate the changes in the school’s pedagogical practices to understand the modifications made by the school to achieve a prominent position in IDEB results. The research will demonstrate that there are several changes that a school needs to make in relation to traditional pedagogy to achieve excellent performance in IDEB and will indicate that the use of active methodologies enabled the gradual improvement of the school’s indicators.

The potential of following-up international large-scale assessment studies: using PISA 2018 to develop a comprehensive model of effective teaching

ABSTRACT The paper investigates the potential of using international large-scale assessment studies for conducting follow-up studies testing models of educational effectiveness. The impact of teacher factors coming from the dynamic model of educational effectiveness and the dialogic education theory on student literacy achievement is examined. Modern Greek teachers from secondary schools that participated in PISA 2018 (Accessed on https://www.oecd.org/pisa/data/pisa2018technicalreport/) in Cyprus were recruited. Two Grade 11 classes per teacher were selected, giving 392 students in total. Observation data from Greek lessons and student literacy achievement data were collected. Two multilevel regression analyses were conducted: one with the whole student sample and one with only students from whom PISA prior achievement measure was identified. When prior achievement (i.e., PISA measure) was considered, additional and larger effects of factors on student final achievement were detected. Both analyses reveal that when factors from both frameworks were considered, larger variance in student achievement was explained.

THE IMPORTANCE OF SCHOOL ORGANIZATION IN IMPROVING ITS RESULTS

Gilberto Lex Municipal School is considered a positive example for having achieved excellence indices in IDEB. However, this is a complex process that arises in a context of redefinitions of the school itself and its understanding of education, especially regarding the relationship that the student establishes with learning. Within this context, this work aims to understand the process of change in teaching practices at the school and how this change significantly impacts the indicators of large-scale assessments. To do this, we will conduct interviews with the families of the students and carry out a case study at the school to obtain data that helps us understand the high performance of the school through the implementation of project-based pedagogy.

Environmental justice implications of flood risk in the contiguous United States – a spatiotemporal assessment of flood exposure change from 2001 to 2019

ABSTRACT Flood hazard is one of America’s most frequent and expensive natural hazards and causes enormous economic losses in the United States every year. Flood hazards disproportionately affect marginalized and socioeconomically disadvantaged populations. This disproportionate flood exposure constitutes a form of environmental injustice. Few studies have undertaken a large-scale assessment of the long-term change of flood exposure. To fill this gap, this study utilized land use and flood zone data from 2001 to 2019 at a 5-year interval to analyze spatiotemporal changes in flood exposure in the Contiguous United States (CONUS). Two indices, the Deviational Exposure Index and Socioeconomic Disparity Index, were introduced to measure flood exposure and the socioeconomic disparities associated with flood exposure. At the national level, the overall flood exposure in the CONUS decreased in the past two decades, indicating increasing awareness of flood risk in the country. But the local variations of flood exposure and its changing trends vary among communities. In general, coastal and riverine counties show a general avoidance of developing urban areas in flood zones, while inland counties show an opposite tendency of urban development in floodplains. The results of this study reveal socioeconomic and demographic disparities between communities in and out of flood zones and evaluate environmental injustice among disadvantaged populations. The knowledge learned from this study can not only help address environmental justice issues but also benefit the decision-making of the federal government and local authorities in urban development and smart growth when faced with flood risk.

Development and evaluation of a Lateral flow immunoassay (LFIA) prototype for the detection of IgG anti-SARS-CoV-2 antibodies

Lateral flow immunoassays (LFIA) for antibody detection represent cost-effective and user-friendly tools for serology assessment. This study evaluated a new LFIA prototype developed with a recombinant chimeric antigen from the spike/S and nucleocapsid/N proteins to detect anti-SARS-CoV-2 IgG antibodies. The evaluation of LFIA sensitivity and specificity used 811 serum samples from 349 hospitalized, SARS-CoV-2 RT-qPCR positive COVID-19 patients, collected at different time points and 193 serum samples from healthy controls. The agreement between ELISA results with the S/N chimeric antigen and LFIA results was calculated. The LFIA prototype for SARS-CoV-2 using the chimeric S/N protein demonstrated 85 % sensitivity on the first week post symptoms onset, reaching 94 % in samples collected at the fourth week of disease. The agreement between LFIA and ELISA with the same antigen was 92.7 %, 0.827 kappa Cohen value (95 % CI [0.765–0.889]). Further improvements are needed to standardize the prototype for whole blood use. The inclusion of the novel chimeric S + N antigen in the COVID-19 IgG antibody LFIA demonstrated optimal agreement with results from a comparable ELISA, highlighting the prototype's potential for accurate large-scale serologic assessments in the field in a rapid and user-friendly format.

Detection of Typical Forest Degradation Patterns: Characteristics and Drivers of Forest Degradation in Northeast China

The accurate identification of forest degradation and its driving factors is a prerequisite for implementing high-quality forest management. However, distinguishing degradation patterns is often neglected in large-scale forest quality assessments. The indicators were constructed to identify typical forest degradation patterns using remote sensing indexes, followed by an analysis of the spatiotemporal dynamics of forest degradation and quantification of the contributions from various driving factors. The results indicated that the constructed indicators could effectively distinguish typical forest degradation patterns, with a fire degradation identification accuracy of 90.0% and a fitting accuracy of drought and pest degradation higher than 0.7. The cold temperate conifer forest zone had the largest proportion of fire degradation, accounting for 67.7% of the area, and totals of 99.0% of the subtropical evergreen broadleaf forest zone and 92.8% of the temperate conifer and broadleaf mixed forest zone were moderately to severely affected by drought, with long-term stability. Additionally, 0.1% of the temperate grassland region and 0.1% of the cold temperate conifer forest zone underwent severe pest infestations, with a long-term stable trend. Meteorological factors were the primary contributors to all typical degradation patterns, accounting for 81.35%, 58.70%, and 82.29%, respectively. The research developed an index for assessing forest degradation and explained the importance of natural and anthropogenic factors in forest degradation. The results are beneficial for the scientific management of forest degradation and for improving forest management efficiency.

Assessing the water quality dynamics in the coastal waters of Kollam (Kerala, India) using Sentinel images

Coastal waters are complex, dynamic, and sensitive, and any change in the system impacts the marine environment and life. Coastal water quality has been decreasing due to the incursion of anthropogenic derived waste and toxins into the ocean. This study investigates water quality along the Kollam coast of Kerala State, India, using Sentinel-2 Multispectral Imager (MSI) data for the period of 2019–2022. Four key water quality parameters, chlorophyll (Chl-a), total suspended matter (TSM), turbidity, and coloured dissolved organic matter (CDOM), were analysed for seasonal variations and driving factors. The study highlights the potential of web-based platforms like Google Earth Engine for facilitating large-scale water quality assessments. The results reveal a distinct seasonal pattern in all parameters, primarily influenced by monsoonal riverine discharge and anthropogenic activities as contributing factors to water quality degradation. Overall, the study emphasises the need for comprehensive monitoring and management strategies to ensure the long-term sustainability of the coastal ecosystem.

Tropical or extratropical cyclones: what drives the compound flood hazard, impact, and risk for the United States Southeast Atlantic coast?

AbstractSubtropical coastlines are impacted by both tropical and extratropical cyclones. While both may lead to substantial damage to coastal communities, it is difficult to determine the contribution of tropical cyclones to coastal flooding relative to that of extratropical cyclones. We conduct a large-scale flood hazard and impact assessment across the subtropical Southeast Atlantic Coast of the United States, from Virginia to Florida, including different flood hazards. The physics-based hydrodynamic modeling skillfully reproduces coastal water levels based on a comprehensive validation of tides, almost two hundred historical storms, and an in-depth hindcast of Hurricane Florence. We show that yearly flood impacts are two times as likely to be driven by extratropical than tropical cyclones. On the other hand, tropical cyclones are 30 times more likely to affect people during rarer 100-year events than extratropical cyclones and contribute to more than half of the regional flood risk. With increasing sea levels, more areas will be flooded, regardless of whether flooding is driven by tropical or extratropical cyclones. Most of the absolute flood risk is contained in the greater Miami metropolitan area. However, several less populous counties have the highest relative risks. The results of this study provide critical information for understanding the source and frequency of compound flooding across the Southeast Atlantic Coast of the United States.

The first extensive analysis of species composition and abundance of stink bugs (Hemiptera: Pentatomidae) on soybean crops in Brazil.

Soybean is the third-greatest global commodity crop with respect to grain production, Brazil is the largest soybean producer in the world. We performed the first extensive survey including all the five main soybean cultivation regions in Brazil over three seasons (2018/2019, 2019/2020, and 2020/2021). A total of 2386 localities were sampled, corresponding to 145 municipalities in 11 states. Sampling was carried out between the R1 and R8 soybean growth stages, using a beating sheet. Fifteen species were recorded, with five species accounting for more than 99% of the sampled insects. The Neotropical brown stink bug, Euschistus heros (F.), was the most abundant species (82.4% of the adults and 84.1% of the nymphs overall), with differences in the mean abundance between soybean macroregions. The melacanthus green belly stink bug, Diceraeus melacanthus Dallas was the second most abundant species overall, followed by the brown winged stink bug, Edessa meditabunda (F.), the furcatus green belly stink bug, Diceraeus furcatus (F.) and the red-banded green stink bug, Piezodorus guildinii (Westwood). The relative abundance of each species differed between soybean macroregions. The mean abundance of nymphs and adults of Euschistus heros at different soybean reproductive stages showed an increase from early reproductive stages to the beginning of the late reproductive stages (R5 or R6). This large-scale assessment of stink bugs provides a basis for outlining integrated pest management programs and drives the development of monitoring and control strategies, as well as future studies investigating population dynamics over time and space in soybean fields. © 2024 Society of Chemical Industry.

Combining machine translation and automated scoring in international large-scale assessments

BackgroundArtificial intelligence (AI) is rapidly changing communication and technology-driven content creation and is also being used more frequently in education. Despite these advancements, AI-powered automated scoring in international large-scale assessments (ILSAs) remains largely unexplored due to the scoring challenges associated with processing large amounts of multilingual responses. However, due to their low-stakes nature, ILSAs are an ideal ground for innovations and exploring new methodologies.MethodsThis study proposes combining state-of-the-art machine translations (i.e., Google Translate & ChatGPT) and artificial neural networks (ANNs) to mitigate two key concerns of human scoring: inconsistency and high expense. We applied AI-based automated scoring to multilingual student responses from eight countries and six different languages, using six constructed response items from TIMSS 2019.ResultsAutomated scoring displayed comparable performance to human scoring, especially when the ANNs were trained and tested on ChatGPT-translated responses. Furthermore, psychometric characteristics derived from machine scores generally exhibited similarity to those obtained from human scores. These results can be considered as supportive evidence for the validity of automated scoring for survey assessments.ConclusionsThis study highlights that automated scoring integrated with the recent machine translation holds great promise for consistent and resource-efficient scoring in ILSAs.

Large-scale assessment of physical activity in a population using high-resolution hip-worn accelerometry: the German National Cohort (NAKO)

Large population-based cohort studies utilizing device-based measures of physical activity are crucial to close important research gaps regarding the potential protective effects of physical activity on chronic diseases. The present study details the quality control processes and the derivation of physical activity metrics from 100 Hz accelerometer data collected in the German National Cohort (NAKO). During the 2014 to 2019 baseline assessment, a subsample of NAKO participants wore a triaxial ActiGraph accelerometer on their right hip for seven consecutive days. Auto-calibration, signal feature calculations including Euclidean Norm Minus One (ENMO) and Mean Amplitude Deviation (MAD), identification of non-wear time, and imputation, were conducted using the R package GGIR version 2.10-3. A total of 73,334 participants contributed data for accelerometry analysis, of whom 63,236 provided valid data. The average ENMO was 11.7 ± 3.7 mg (milli gravitational acceleration) and the average MAD was 19.9 ± 6.1 mg. Notably, acceleration summary metrics were higher in men than women and diminished with increasing age. Work generated in the present study will facilitate harmonized analysis, reproducibility, and utilization of NAKO accelerometry data. The NAKO accelerometry dataset represents a valuable asset for physical activity research and will be accessible through a specified application process.

Geospatial Science for the Environmental Epidemiology of Cancer in the Exposome Era.

Geospatial science is the science of location or place that harnesses geospatial tools, such as geographic information systems (GIS), to understand the features of the environment according to their locations. Geospatial science has been transformative for cancer epidemiologic studies through enabling large-scale environmental exposure assessments. As the research paradigm for the exposome, or the totality of environmental exposures across the life course, continues to evolve, geospatial science will serve a critical role in determining optimal practices for how to measure the environment as part of the external exposome. The objectives of this article are to provide a summary of key concepts, present a conceptual framework that illustrates how geospatial science is applied to environmental epidemiology in practice and through the lens of the exposome, and discuss the following opportunities for advancing geospatial science in cancer epidemiologic research: enhancing spatial and temporal resolutions and extents for geospatial data; geospatial methodologies to measure climate change factors; approaches facilitating the use of patient addresses in epidemiologic studies; combining internal exposome data and geospatial exposure models of the external exposome to provide insights into biological pathways for environment-disease relationships; and incorporation of geospatial data into personalized cancer screening policies and clinical decision making.