The interest in program- and colleges of education- level evaluation and alignment of student learning outcomes to course content has been increasing over the past several decades. Curriculum mapping establishes the links between content and expected student learning outcomes. Curriculum map is an overview of what is taking place in the classroom; and it includes evaluation tools and activities. Social Studies Department, Federal Capital Territory (FCT) College of Education Zuba, Abuja, recently completed an accreditation exercise by National Commission for Colleges of Education Abuja, Nigeria. The audit reported that there was no match between the student learning outcomes and Social Studies curricula. The purpose of this paper was to align the Nigeria Certificate in Education (NCE) (Social Studies) minimum standards with student learning outcomes to determine gaps and redundancies. The paper also looked at how virtual education enhances curriculum mapping during COVID-19 pandemic. Minimum standards learning outcomes were modified from existing learning outcomes to better align with college learning outcomes and the Social Studies Core and Elective Competencies. All NCE Social Studies courses were mapped to the Social Studies Core and Elective Competencies and assessed to determine the gaps and redundancies. The study used the documentary research method. The purposeful sampling strategy was used to select the research site. Potential gaps were defined as coverage for each competency in about ≤20% of the courses and potential redundancies was considered as coverage of ≥80% of the courses. The mapping exercise revealed gaps; and no redundancies in course content. The findings of the mapping exercises should be used to improve the content provided to NCE Social Studies students at FCT College of Education Zuba, with the overall objective of enhancing the quality of the education provided to those students and helping them to be better students that are prepared for a successful career in Social Studies.

Series Name: Adjusted wealth parity index for completion rate by sex location wealth quintile and education levelSeries Code: SE_AWP_CPRARelease Version: 2020.Q2.G.03This dataset is the part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 4.5.1: Parity indices (female/male, rural/urban, bottom/top wealth quintile and others such as disability status, indigenous peoples and conflict-affected, as data become available) for all education indicators on this list that can be disaggregatedTarget 4.5: By 2030, eliminate gender disparities in education and ensure equal access to all levels of education and vocational training for the vulnerable, including persons with disabilities, indigenous peoples and children in vulnerable situationsGoal 4: Ensure inclusive and equitable quality education and promote lifelong learning opportunities for allFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

Since March 2020, measures surrounding COVID-19 pandemic have resulted in emergency remote teaching with, among others, fewer opportunities for interpersonal contact (Burns et al., 2020). This has caused changes in the learning environment and students' functioning in the academic system, which may likely lead to changes within their well-being. The current study aims to map student well-being in times of COVID-19, identifying both individual factors and structural factors in the learning environment. Previous research has identified several individual factors that may influence student well-being in times of COVID-19. Amongst those are tolerance to uncertainty, resilience (growth), self-compassion, and attention regulation. In addition to these three individual factors, various factors within the learning environment may influence students' well-being. A sense of autonomy, competence and relatedness- as described within the Self-Determination Theory - play a central role in students' well-being. However, in times of social distancing and online education, it seems essential to investigate the potential of an adjusted learning environment to further fulfill students’ needs. That is why we are aiming to investigate these factors as potential predictors for student well-being, operationalised as positive and negative affect as well as life satisfaction.

This version of the secondary school district layer, representing districts that provide education to upper grade/age levels, uses high-quality base map spatial data published by the State of New Jersey. The U.S. Census Bureau's latest school district boundary data (TIGER) were used for guidance in establishing which municipalities to include in each district. The district boundaries were created using updated NJ Municipal Boundaries (Govt_admin_municipal_bnd). School district boundaries in two areas, on and near military bases, were edited to reflect special arrangements made for students residing in base housing. See Supplemental Information for details. By U.S. Census Bureau definition, school districts are single-purpose administrative units within which local officials provide, or pay other districts to provide, public educational services for the area's residents. The Census Bureau obtains the boundaries, names, local education agency codes, grade ranges, and school district levels for school districts from State officials for the primary purpose of providing the U.S. Department of Education with estimates of the number of children in poverty within each school district. This information serves as the basis for the Department of Education to determine the annual allocation of Title I funding to States and school districts. In 2015 NJ Department of Education (NJDOE) corrected grade ranges and district types according to financial obligation, not the provision of educational services. NJDOE used set of grades, based on financial responsibility, to assign the data for each child to exactly one school district, except for districts covering Joint Base McGuire-Dix-Lakehurst in Burlington County. See Supplemental Information and Process Steps for details.This data set normally is updated annually, with updated school district information provided by the NJ Department of Education (NJDOE).

More informationIn order to plan for school provision and analyse the relevant demographic data in a way that takes account of the significant local and regional variations in demographic trends and enrolment projections, the Department of Education divides the country into 314 school planning areas.The school planning areas were developed for use with the Department’s Geographic Information System (GIS) in 2008 and with the introduction of Small Areas in Census 2011, these areas were amended to align with Census Small Areas. The current school planning areas take account not only of local groupings of schools, but also of natural boundaries, Census Small Areas and other local conditions.The school planning areas provide a useful means of projecting demographic demand in a localised area or areas, thereby allowing the Department to determine oncoming growth at a relatively localised level to inform recommendations and decisions on where additional school places may be needed.However, there can be a high degree of inward and outward mobility of children between school planning areas, particularly in urban areas, and parents are free to apply to enroll their children in any school, whether that is in the school planning area in which they reside or not.

Overview

A set of maps revealing agricultural land use patterns in the irrigated drylands of the Amu Darya and Syr Darya basin between 1987 and 2019. The maps were produced using time series of 30m Landsat TM, ETM+ and OLI Collection 1 surface reflectance products and a Random Forest classification model trained with ~30,000 samples from the years 1987, 1998, 2008, and 2018. All processing steps were conducted in Google Earth Engine. The target classes of this product are "wet season cropping", "dry season cropping", "double cropping including fodder crops", and "non-cropland". Mapping was conducted across nine provinces in Uzbekistan, two in Turkmenistan, and three in Tajikistan, and post-processing was used to constrain the study area to regions which were irrigated in at least two years in the study period, areas below 2,000 m above sea level, and regions/years with a sufficient number of cloud-free Landsat images (n>6). Annual maps were aggregated temporally and spatially, resulting in different datasets described below.

We advise map users to read the open access paper and the associated supplementary materials for detailed insights. In case of further questions please contact the lead author of the work.

Data

This download contains three folders:

• landuse_30m: Land use layers with 30m spatial resolution, representing the percentage (0-100%) of the three key land use types ("wet season cropping", "dry season cropping", "double cropping including fodder crops") within two time frames (1987-2000 and 2001-2019). Years with insufficient cloud-free Landsat observations were excluded from the calculation of percentages.
• landuse_3km: Land use metrics with 3km spatial resolution, representing - for each grid cell and year - the "percentage of cropland", "percentage of dry-season cultivation", and "cropping frequency". The layers in these datasets are ordered chronologically, with the layer 1 representing the year 1987, and layer 33 the year 2019. Gaps in the time series were filled using linear interpolation for subsequent trend calculation using remotePARTS.
• mask: A mask with 30m spatial resolution constraining the study area to regions below 2,000 m above sea level and regions which were irrigated at least twice in the study period.

Map accuracy

We conducted an area-adjusted accuracy assessment based on a stratified random sample (n = 2,784 per year), which yielded important insights regarding accuracies and error types. The median area-adjusted overall accuracy of the maps across the study period is 91.4%, but class-specific user´s and producer´s accuracies vary substantially. Users should consult the supplementary materials of the article for details on accuracies, confusion matrices, and the most important error types.

Further resources
The production of this map was made possible through the Landsat Program of the United States Geological Survey (USGS) and the Google Earth Engine cloud computing platform for preprocessing of the satellite data and classification. The code for preprocessing the Landsat time series is based on the Google Earth Engine Python API and made available at https://github.com/philipperufin/eepypr/.

Curriculum Alignment Software Market Outlook

As per our latest research, the global curriculum alignment software market size in 2024 stands at USD 1.28 billion, reflecting the growing demand for solutions that streamline educational content and ensure compliance with learning standards. The market is expanding at a robust CAGR of 12.7% from 2025 to 2033, propelled by the digitization of education and the increasing emphasis on outcome-based learning. By 2033, the market is forecasted to reach USD 3.74 billion, underscoring the pivotal role curriculum alignment software plays in transforming educational institutions and corporate learning environments worldwide. This growth is primarily attributed to the rising need for standardized curriculum delivery, enhanced learning analytics, and the integration of artificial intelligence in education technology.

One of the primary growth drivers for the curriculum alignment software market is the global shift toward competency-based education and personalized learning pathways. Educational institutions and enterprises are increasingly adopting digital tools to ensure that their curriculum is not only compliant with local and international standards but also adaptable to the unique learning needs of students and trainees. The adoption of such software enables educators to map learning objectives directly to assessments, track student progress in real-time, and identify gaps in curriculum delivery. Moreover, the growing pressure from accreditation bodies and government agencies to demonstrate measurable learning outcomes has compelled schools, colleges, and training institutes to invest in robust curriculum alignment solutions. These platforms offer advanced analytics and reporting features, which simplify the process of maintaining curriculum integrity and adjusting instructional strategies based on performance data.

The rapid advancement of educational technology, particularly the integration of artificial intelligence and machine learning, is further fueling the expansion of the curriculum alignment software market. Modern solutions leverage AI-driven analytics to automate the alignment of learning materials with standards, recommend personalized learning paths, and predict student performance trends. This not only enhances the efficiency of curriculum planning and management but also empowers educators to make data-driven decisions. The proliferation of cloud-based deployment models has also played a significant role in market growth, providing scalable, cost-effective, and easily accessible solutions for institutions of all sizes. Additionally, the COVID-19 pandemic accelerated the adoption of digital education tools, highlighting the necessity for seamless curriculum alignment in both remote and hybrid learning environments.

Another crucial growth factor is the increasing demand for continuous professional development and corporate training across industries. Enterprises are recognizing the value of curriculum alignment software in ensuring that their workforce training programs are consistent, up-to-date, and aligned with organizational goals and compliance requirements. These platforms facilitate the rapid rollout of new training modules, track employee progress, and provide actionable insights to improve learning outcomes. As businesses face mounting regulatory pressures and a rapidly evolving skills landscape, the need for agile, standards-driven learning solutions becomes even more pronounced. This trend is expected to drive significant adoption in the corporate sector, complementing the already strong demand from educational institutions.

Regionally, North America continues to dominate the curriculum alignment software market, accounting for the largest share in 2024, thanks to its advanced educational infrastructure, high digital literacy, and supportive government policies. However, the Asia Pacific region is emerging as a key growth engine, fueled by substantial investments in edtech, expanding internet penetration, and a burgeoning population of learners. Europe also maintains a strong presence, with a focus on harmonizing educational standards across countries and promoting digital transformation in schools and universities. Meanwhile, Latin America and the Middle East & Africa are witnessing steady growth, driven by government-led digital education initiatives and increased adoption of cloud-based solutions. The regional outlook underscores the global relevance of curriculum alignment software and its critical role in shaping the future of education and workfor

According to exit polling in *** key states of the 2024 presidential election in the United States, almost ********** of voters who had never attended college reported voting for Donald Trump. In comparison, a similar share of voters with ******** degrees reported voting for Kamala Harris.

This version of the unified school district layer, representing districts that provide education to all grade/age levels, uses high-quality base map spatial data published by the State of New Jersey. The U.S. Census Bureau's latest school district boundary data (TIGER) were used for guidance in establishing which municipalities to include in each district. The district boundaries were created using updated NJ Municipal Boundaries (Govt_admin_municipal_bnd). In addition, school district boundaries in two areas, on and near military bases, were edited to reflect special arrangements made for students residing in base housing. See Supplemental Information for details. By U.S. Census Bureau definition, school districts are single-purpose administrative units within which local officials provide, or pay other districts to provide, public educational services for the area's residents. The Census Bureau obtains the boundaries, names, local education agency codes, grade ranges, and school district levels for school districts from State officials for the primary purpose of providing the U.S. Department of Education with estimates of the number of children in poverty within each school district. This information serves as the basis for the Department of Education to determine the annual allocation of Title I funding to States and school districts. In 2015 NJ Department of Education (NJDOE) corrected grade ranges and district types according to financial obligation, not the provision of educational services. NJDOE used set of grades, based on financial responsibility, to assign the data for each child to exactly one school district, except for districts covering Joint Base McGuire-Dix-Lakehurst in Burlington County. See Supplemental Information and Process Steps for details. This data set normally is reviewed annually, and updated if necessary using school district information provided by the NJ Department of Education (NJDOE). The data for Somerset County was extracted & processed from the latest dataset on NJGIN by the Somerset County Office of GIS Services (SCOGIS) on December 27, 2022

30 participants（15 females and 15 males）were recruited. All participants were master’s students. They all had normal naked eye vision or corrected vision, and were right-handed. They had prior education in map reading and frequently used maps, but they had no prior knowledge of the map content involved in the experiment.Participants were assigned to three groups based on the Santa Barbara Sense of Direction Scale self-assessment, ensuring that each group had a roughly equivalent number of participants, gender ratio, proportion of geographic education, frequency of map usage, and sense of direction.The experiment was conducted in a laboratory setting. The experimental display was a Dell desktop computer with a 23-inch screen and a resolution of 1920×1080 pixels. The eye-tracking data was collected using aSeePro desktop eye-tracking device with a sampling frequency of 140 Hz. The aSeeStudio software was used to manage and analyze the eye-tracking data.We selected a 3D map from Amap, along with 2D and imagery maps depicting approximately the same geographical area , and ensured identical spatial coverage across all map types through a preliminary questionnaire survey. These three map types served as distinct experimental materials. Participants, pre-grouped based on the Santa Barbara Sense of Direction Scale, were assigned to one of the three map conditions. During the experiment, participants were given six seconds to freely explore the map. Subsequently, they performed tasks following audio instructions. The target location name was displayed on the map alongside a red bounding box, and participants were required to select the specified geographical area by clicking on it.

This data set includes classified land cover transition maps at 30-m resolution derived from Landsat TM, MSS, ETM+ imagery and aerial photos of Altamira, Santarem, and Ponta de Pedras, in the state of Para, Brazil. The Landsat images were classified into several types of land use (e.g., forest, secondary succession, pasture, annual crops, perennial crops, and water) and subjected to change detection analysis to create transition matrices of land cover change. Dates of acquired images represent the most cloud-free image retrievals from 1970-2001 for each site and are therefore not continuous. There are 3 GeoTIFF files (.tif) with this data set.

Data Notes:

• NSW public schools have defined local enrolment areas, meaning that every child is entitled to enrol in a particular school based on his or her residential address. Every public school reserves enough places within their school for students in their local enrolment area.

• Disclaimer: Due to the evolving nature of school information and local enrolment areas, no responsibility can be taken by the NSW Department of Education, or any of its associated departments, if information is relied upon. For example, but not limited to, real estate purchases or rentals where the school intake zone data is used as a reference source.

• School catchments areas can change for multiple reasons. These include schools opening and closing, and changes in population demographics, for example.

• It is recommended that this dataset be used in conjunction with the Master Dataset to ensure a comprehensive understanding of all government school information.

• School catchment data is updated nightly and accessible through the School Finder tool.

Data Source:

• Asset Management Directorate. NSW Department of Education.

S1 Fig. Project School Map in Jhenaidah, Bangladesh. S1 Table. Endline (non-DID) estimation of family-wise mean-standardised effect in average effect size on nine outcome families adjusting for baseline covariates (all children). S2 Table. DID estimation of family-wise mean-standardised effect in average effect size on nine outcome families with additional covariates (all children). S3 Table. DID estimation of family-wise mean-standardised cross-cutting HESP-treatment effect in average effect size on five selected outcome families with additional covariates (all children). S4 Table. HE-treatment effects on single outcomes (selected outcomes) (all children; children in both surveys) S1 File. Study Protocol. S1 Checklist. CONSORT Checklist. (ZIP)

Threshold image at 0.95-1 to view the results that are TFCE-corrected for multiple comparisons

Collection description

Understanding the mechanisms behind academic buoyancy, the ability to effectively cope with everyday academic challenges, is essential for identifying the factors and mechanisms that help students maintain their motivation and cope with routine academic pressures. One potential underlying mechanism is reward sensitivity, or the capacity to experience pleasure both in anticipating and receiving reward-related stimuli. We hypothesized that individuals with higher sensitivity to anticipated reward would exhibit greater academic buoyancy. To test this in an academic context, we modified the Monetary Incentive Delay (MID) task into a School Grade Incentive Delay (SGID) task, where participants work towards a fictitious school grade by winning or losing points on each of the trials. In this study, we investigated whether the SGID activates the neural reward circuitry similar to the traditional MID and whether this is associated with academic buoyancy. The SGID task activated key brain regions associated with reward anticipation, validating its use for studying reward processing in academic contexts. However, we found no significant correlation between reward-related neural activations and academic buoyancy. Further research in larger samples is needed to capture the full complexity of reward processing in relation to academic buoyancy.

Subject species

homo sapiens

Modality

fMRI-BOLD

Analysis level

group

Cognitive paradigm (task)

None / Other

Map type

IP

The National Center for Education Statistics' (NCES) Education Demographic and Geographic Estimate (EDGE) program develops annually updated point locations (latitude and longitude) for postsecondary institutions included in the NCES Integrated Postsecondary Education Data System (IPEDS). The IPEDS program annually collects information about enrollments, program completions, graduation rates, faculty and staff, finances, institutional prices, and student financial aid from every college, university, and technical and vocational institution that participates in federal student financial aid programs under the Higher Education Act of 1965 (as amended). IPEDS school point locations are derived from reported information about the physical location of schools. The NCES EDGE program collaborates with the U.S. Census Bureau's Education Demographic, Geographic, and Economic Statistics (EDGE) Branch to develop point locations for schools reported in the annual IPEDS file. The point locations in this data layer were developed from the 2018-2019 IPEDS collection. For more information about NCES school point data, see: https://nces.ed.gov/programs/edge/Geographic/SchoolLocations.All information contained in this file is in the public domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data.

The number of dataset files divided into the original published studies (original) and expert-modified distributions (expert) with two overall time periods.

School- and individual-level characteristics of the study sample.

A collection of 10 brain maps. Each brain map is a 3D array of values representing properties of the brain at different locations.

Collection description

Understanding the mechanisms behind academic buoyancy, the ability to effectively cope with everyday academic challenges, is essential for identifying the factors and mechanisms that help students maintain their motivation and cope with routine academic pressures. One potential underlying mechanism is reward sensitivity, or the capacity to experience pleasure both in anticipating and receiving reward-related stimuli. We hypothesized that individuals with higher sensitivity to anticipated reward would exhibit greater academic buoyancy. To test this in an academic context, we modified the Monetary Incentive Delay (MID) task into a School Grade Incentive Delay (SGID) task, where participants work towards a fictitious school grade by winning or losing points on each of the trials. In this study, we investigated whether the SGID activates the neural reward circuitry similar to the traditional MID and whether this is associated with academic buoyancy. The SGID task activated key brain regions associated with reward anticipation, validating its use for studying reward processing in academic contexts. However, we found no significant correlation between reward-related neural activations and academic buoyancy. Further research in larger samples is needed to capture the full complexity of reward processing in relation to academic buoyancy.

Median (percentiles 25 and 75) of children’s age and cognitive outcomes (working memory and inattentiveness) at each of the 4 repeated visits.