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Student performance, MOOC logs, knowledge tracing, standardized tests, learning analytics
12,889 datasets
Three completed strategic assessments in the Australian Capital Territory (ACT) for Gungahlin, Molonglo Valley, and West Belconnen. The dataset shows the entire area considered under a strategic assessment as well as areas identified for development and conservation. It is provided by the ACT Government Geospatial Data Catalogue (ACTmapi) and was last updated on April 4, 2026.
ACTmapi provides geospatial data showing the entire area considered under a strategic assessment for West Belconnen, including zones identified for development and conservation. The dataset is part of a broader process under the EPBC Act, alongside assessments for Gungahlin and Molonglo Valley. It was last updated by the ACT Government Geospatial Data Catalogue on April 4, 2026.
ACTmapi provides geospatial data on strategic assessments under the EPBC Act. The dataset shows the entire area considered under the West Belconnen strategic assessment, including zones identified for development and conservation. It is maintained by the ACT Government Geospatial Data Catalogue and was last updated on April 4, 2026.
Geospatial data delineates areas considered under strategic assessments for streamlined environmental approvals under the EPBC Act in the Australian Capital Territory. The dataset includes the entire assessment area and identifies zones for development and conservation. It is provided by ACTmapi and was last updated on April 4, 2026.
ACTmapi provides geospatial data showing the entire area considered under a strategic assessment for the Molonglo Valley, including areas identified for development and conservation. Strategic assessments allow broader project areas to be assessed under a single approval process under the EPBC Act. The dataset is part of a collection of three completed strategic assessments in the ACT.
The Australian Capital Territory (ACT) dataset shows the entire project area considered under a strategic assessment, including zones identified for development and conservation. It covers three completed assessments in the ACT: Gungahlin, Molonglo Valley, and West Belconnen. The dataset is provided by the ACT Government Geospatial Data Catalogue (ACTmapi) and was last updated on April 4, 2026.
The Australian Capital Territory's Gungahlin Strategic Assessment Area dataset delineates zones for development and conservation under a single approval process. It was created by ACTmapi and last updated on April 4, 2026. The dataset covers the entire area considered under the strategic assessment.
Strategic assessments allow broader project areas to be assessed under a single approval process. This dataset shows the entire area considered under the Gungahlin strategic assessment, including areas identified for development and conservation. It is provided by ACTmapi, the ACT Government Geospatial Data Catalogue.
ACTmapi provides geospatial data delineating development and conservation zones within the Gungahlin strategic assessment area. The dataset likely contains polygon boundaries for the entire assessed area and its designated sub-areas. It was last updated by the ACT Government Geospatial Data Catalogue on April 4, 2026.
A collation of surveys gathering data and evidence from a variety of marine environments. The survey purposes include recommended Marine Conservation Zone verification, condition assessments, and surveys of Natura 2000 sites. All surveys are carried out to specified standards and follow established methodologies by Natural England.
A short course curriculum introduces high school students to materials science and engineering through hands-on chocolate experiments and Python programming. The curriculum, designed by Janine K. Nunes, uses a Context-Based Learning framework to link everyday observations to the materials science tetrahedron. It includes computational modules for modeling and data visualization, enabling students to produce publication-quality figures.
Malissa Maria Mahmud's systematic literature review synthesizes 30 empirical studies on Hybrid-Flexible (HyFlex) learning in higher education from 2019 to 2024. The review examines the impact of HyFlex course designs on student engagement across diverse disciplinary and geographical contexts. It follows PRISMA 2020 reporting guidelines.
A 2024 systematic literature review synthesizes 30 empirical studies published from 2019 to 2024 on the impact of Hybrid-Flexible (HyFlex) course designs on student engagement in higher education. Authored by Malissa Maria Mahmud, the document analyzes findings across diverse disciplinary and geographical contexts, following PRISMA 2020 reporting guidelines.
A systematic literature review synthesizes 30 empirical studies on the impact of HyFlex learning on student engagement in higher education. Authored by Malissa Maria Mahmud, the document analyzes studies published from 2019 to 2024. It examines factors like self-regulation, sense of community, and instructor support across diverse disciplinary and geographical contexts.
30 empirical studies from 2019 to 2024 are synthesized in this systematic review. The document analyzes the impact of Hybrid-Flexible course designs on student engagement in higher education. Authored by Malissa Maria Mahmud, it was last updated in April 2026.
A list of driving schools licensed by the Ontario Ministry of Transportation. The dataset identifies schools meeting provincial standards for Beginner Driver Education programs, including mandatory 40 hours of instruction. It is published by the Government of Ontario and was last updated in March 2026.
GOCAL was an internationally coordinated long-term sampling program in the Gulf of California. It was designed to examine temporal and spatial variability in the region's biogeochemical properties, involving researchers from CICESE, University of Sonora, San Diego State University, and Oregon State University. Components were funded by the NASA SIMBIOS initiative.
HeiGIT provides a geospatial dataset classifying road surfaces in Macao, China, as paved or unpaved. The data originates from OpenStreetMap and is augmented with deep learning predictions from Mapillary imagery, using a hybrid method detailed in an arXiv paper. The dataset was last updated on March 2, 2026.
Guadeloupe road network data provides AI-derived surface classifications for paved and unpaved roads. The dataset originates from OpenStreetMap and is augmented with deep learning predictions based on Mapillary imagery and urban layers. It was published by the Heidelberg Institute for Geoinformation Technology (HeiGIT) and last updated in March 2026.
Road surface data for French Guiana derived from OpenStreetMap and augmented with deep learning predictions from Mapillary imagery. The dataset includes AI-derived classifications for paved and unpaved roads, along with standard OSM attributes and urban classification layers. It was created by HeiGIT and last updated in March 2026.