Large-scale Assessment Research Articles

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.

MelastomaTRAITs 1.0: A database of functional traits in Melastomataceae, a large pantropical angiosperm family.

The recent availability of open-access repositories of functional traits has revolutionized trait-based approaches in ecology and evolution. Nevertheless, the underrepresentation of tropical regions and lineages remains a pervasive bias in plant functional trait databases, which constrains large-scale assessments of plant ecology, evolution, and biogeography. Here, we present MelastomaTRAITs 1.0, a comprehensive and updatable database of functional traits for the pantropical Melastomataceae, the ninth-largest angiosperm family with 177 genera and more than 5800 species. Melastomataceae encompass species with a wide diversity of growth forms (herbs, shrubs, trees, epiphytes, and woody climbers), habitats (including tropical forests, savannas, grasslands, and wetlands from sea level to montane areas above the treeline), ecological strategies (from pioneer, edge-adapted and invasive species to shade-tolerant understory species), geographic distribution (from microendemic to continental-wide distribution), reproductive, pollination, and seed dispersal systems. MelastomaTRAITs builds on 581 references, such as taxonomic monographs, ecological research, and unpublished data, and includes four whole-plant traits, six leaf traits, 11 flower traits, 18 fruit traits, and 27 seed traits for 2520 species distributed in 144 genera across all 21 tribes. Most data come from the Neotropics where the family is most species-rich. Miconieae (the largest tribe) contains the highest number of trait records (49.6%) and species (41.1%) records. The trait types with the most information in the database were whole-plant traits, flowers, and leaf traits. With the breadth of functional traits recorded, our database helps to fill a gap in information for tropical plants and will significantly improve our capacity for large-scale trait-based syntheses across levels of organization, plant-animal interactions, regeneration ecology, and thereby support conservation and restoration programs. There are no copyright restrictions on the dataset; please cite this data paper when reusing the data.

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?

Subtropical 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.

Assessment of Academic Resilience and Its Associated Factors Among Pharmacy Students in Twelve Countries

ObjectiveAcademic resilience, a critical determinant of academic achievement, is affected by various factors. There is a paucity of large-scale international assessments of academic resilience among pharmacy students. Therefore, this study aimed to assess academic resilience among pharmacy students in 12 countries and to evaluate factors associated with their academic resilience levels. MethodsA cross-sectional online survey-based study was conducted among randomly selected pharmacy students in 12 countries: Egypt, Türkiye, Indonesia, Pakistan, Bangladesh, Iraq, Jordan, Nigeria, Malaysia, Saudi Arabia, Sudan, and the United Arab Emirates. After pilot testing, the validated 30-item academic resilience scale (ARS) was used for the assessment. The data were collected between November 1, 2022 and April 15, 2023. Descriptive and inferential statistics were performed, as appropriate. ResultsA total of 3950 were received from the 12 participating countries. The mean age was 21.68 ± 2.62 years. About two-thirds of the responses were from female participants and those studying for Bachelor of Pharmacy degrees. Overall, the findings show moderate academic resilience, which varied across countries. The median (IQR) of the total ARS-30 was 114 (103−124). Females exhibited lower negative affective and emotional response subscale levels than males. There were significant cross-country variations in the ARS-30 and all subscales. The highest overall levels were reported for Sudan, Pakistan, and Nigeria and the lowest were reported for Indonesia and Türkiye. Students in private universities tended to have higher overall ARS levels than public university students. Higher academic performance was significantly associated with ARS levels, whereas those with excellent performance exhibited the highest ARS levels. Students with exercise routines had higher ARS levels than those without exercise routines. Finally, students who were engaged in extracurricular activities had higher ARS levels than those who did not participate in these activities. ConclusionThe study offers insights into the factors affecting academic resilience in pharmacy students across several countries. The findings could guide interventions and support activities to improve resilience and academic outcomes.

Depression Detection Using Digital Traces on Social Media: A Knowledge-aware Deep Learning Approach

ABSTRACT Depression is a pressing yet underdiagnosed issue in health management. Because depressed patients share their symptoms, life events, and treatments on digital platforms, information systems (IS) scholars resort to user-generated digital traces for depression detection. While they facilitate innovative information technology (IT) approaches to alleviate the social and economic burden of depression, most studies lack effective means to incorporate domain knowledge in depression detection systems or suffer from feature extraction difficulties. Following the design science research in IS, we propose a Deep-Knowledge-Aware Depression Detection system to detect social media users at risk of depression and explain the detection factors. We deploy extensive empirical analyses to evaluate our designed IT artifact, which shows domain knowledge greatly improves performance. Our work has significant implications for IS research in knowledge-aware machine learning, digital traces utilization, and generalizable design principles. Practically, the early detection and factor explanation from our IT artifact can assist depression management and enable large-scale assessment of the population’s mental health.

Global analysis of groundwater pumping from increased river capture

In many regions globally, groundwater overuse exceeds natural replenishment, leading to immediate consequences such as reduced river flows and devastating impacts on freshwater ecosystems. In alluvial aquifers in particular, groundwater pumping contributes to river flow reduction in two significant ways: first, by intercepting water that would naturally discharge into the river, and second, by lowering groundwater levels below the riverbed, causing river water to infiltrate. Despite these critical interactions, large-scale water resources assessments often overlook the relationship between groundwater and surface water, hindering a comprehensive understanding of the consequences of groundwater pumping on both the groundwater and surface water systems. Our study, utilizing a coupled global-scale groundwater–surface water model, reveals that approximately 20% of globally pumped groundwater stems from diminished streamflow, while 16% results from reduced storage. Projections for the end of the century, accounting for climate change, suggest potential increases to 30% from reduced streamflow and a decrease to 12% from reduced storage. Notably, our results highlight that the impact on streamflow is more widespread and linked to smaller pumping rates, contrasting with impacts on storage associated with higher pumping rates. This study shows the crucial need to include groundwater–surface water interactions in large-scale water resources assessments, not only for accurate estimates of freshwater availability but also for a comprehensive understanding of the far-reaching impacts of groundwater overuse related to increasing water demands and climate change.

The limits of inference: reassessing causality in international assessments

AbstractThis paper scrutinizes the increasing trend of using international large-scale assessment (ILSA) data for causal inferences in educational research, arguing that such inferences are often tenuous. We explore the complexities of causality within ILSAs, highlighting the methodological constraints that challenge the validity of causal claims derived from these datasets. The analysis begins with an overview of causality in relation to ILSAs, followed by an examination of randomized control trials and quasi-experimental designs. We juxtapose two quasi-experimental studies demonstrating potential against three studies using ILSA data, revealing significant limitations in causal inference. The discussion addresses the ethical and epistemological challenges in applying quasi-experimental designs to ILSAs, emphasizing the difficulty in achieving robust causal inference. The paper concludes by suggesting a framework for critically evaluating quasi-experimental designs using ILSAs, advocating for a cautious approach in employing these data for causal inferences. We call for a reevaluation of methodologies and conceptual frameworks in comparative education, underscoring the need for a multifaceted approach that combines statistical rigor with an understanding of educational contexts and theoretical foundations.

Non-Parametric CD-CAT Item Selection Strategy and Termination Rules Based on Binary Search Algorithm

CD-CAT plays a significant role in diagnosing and assessing students, contributing significantly to improving teaching effectiveness. However, in classroom teaching scenarios, unlike large-scale assessments where a large number of samples can be used to accurately estimate item parameters, non-parametric CD-CAT becomes the only feasible choice. Compared to parametric CD-CAT, non-parametric CD-CAT started later, and research mainly focuses on non-parametric item selection strategies. However, the existing non-parametric item selection strategies have the disadvantage of low efficiency, and there is little research on non-parametric termination rules. Therefore, this study proposes two more efficient item selection strategies: Non-Parametric Dynamic Binary Search (NDBS) and General Non-Parametric Dynamic Binary Search (GNDBS), as well as a non-parametric termination rule：Non-parametric Dynamic Binary Searching Index (NDBI). Simulation results show: (1) Under all conditions, the pattern classification accuracy rate of NDBS is higher than that of NPS, so NDBS can be used as the item selection strategy when there are no samples available. (2) In most cases, the performance of GNDBS is better than other item selection strategies, so GNDBS can be chosen as the item selection strategy when there are few samples available. (3) In variable-length tests, when the research objective is to obtain more accurate classification results, the critical value of the NDBI rule can be reduced; conversely, the critical values of the NDBI and GNDBI rules can be appropriately increased.

Large-scale assessment of date palm plantations based on UAV remote sensing and multiscale vision transformer

Timely and efficient mapping of date palm plantations through unmanned aerial vehicle (UAV) remote sensing is critical for continuous observation, health and risk evaluation, pest management, resource optimization, and ensuring the long-term sustainability of the dates industry. This study presents an efficient and cost-effective transformer-based approach to identify, countify, monitor, and evaluate the overall well-being of palm trees using extensive UAV imagery. The suggested approach integrates an improved multiscale vision transformer, feature pyramid network, Mask R–CNN, and improved slicing-aided hyper inference for practical large-scale assessments. This combination enabled the extraction of multiscale features, capturing long-range dependencies in the data and boosting the model's generalizability. The proposed architecture outperformed several CNN-based architectures (including Mask R–CNN, Cascade Mask R–CNN, Point-based Rendering, and You Only Look At CoefficientTs), achieving F-scores of 94.33% and 94.2% for date palm tree detection and segmentation, respectively. The transformer-based architecture was optimized using transfer learning to differentiate between healthy and unhealthy date palm trees, particularly those with severe infestations. The potential generic condition of date palm trees was predicted with an F-score of 88.4%. Further advancements in this field could pave the way for a proactive strategy, enabling timely detection, which would aid in pest management and support the sustainable growth of the dates sector.