Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

1.5K
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
1.5K
Multiple Bar Graph01:07

Multiple Bar Graph

10.6K
As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
10.6K
Aggregates Classification01:29

Aggregates Classification

1.2K
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
1.2K
Time-Series Graph00:54

Time-Series Graph

5.6K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
5.6K
Types of Aggregate Grading01:15

Types of Aggregate Grading

1.9K
Aggregate grading is crucial in economically obtaining a concrete mix with adequate strength, reasonable workability, and minimal segregation. There are four types of aggregate gradation: well-graded, uniformly (or one-sized) graded, gap-graded, and open-graded.
Well-graded aggregates include a complete range of necessary size fractions that fit together to create a dense matrix with minimal voids, represented by a smooth, continuous gradation curve. This type of grading ensures good...
1.9K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

CTSS regulates macrophage lipid metabolic reprogramming and white matter repair after intracerebral hemorrhage.

Journal of translational medicine·2026
Same author

Machine Learning-Based Integration Unveils RNA Methylation Regulator-Related Immune-Derived Gene Signatures in Ruptured Intracranial Aneurysm.

Biomedicines·2026
Same author

Experimental and Numerical Investigation of Slenderness Ratio on a Hollow Glued Bamboo Scrimber Column Under Eccentric Compression.

Materials (Basel, Switzerland)·2026
Same author

Incorporating dietary information to enhance polygenic prediction models with applications to body mass index and type 2 diabetes.

Genes & nutrition·2026
Same author

Altered source-sink dynamics influence Verticillium wilt expression in cotton.

Plant disease·2026
Same author

Fungal foe: exploring cotton's physiological responses to Verticillium wilt.

Frontiers in plant science·2026
Same journal

Turbulent flow in a vortex separator with a directed pipe inlet.

Scientific reports·2026
Same journal

Systematic characteristic evaluation of clay-based cementitious material derived from calcium carbide residue and waste tile powder.

Scientific reports·2026
Same journal

Retraction Note: Improvement of a rapid diagnostic application of monoclonal antibodies against avian influenza H7 subtype virus using Europium nanoparticles.

Scientific reports·2026
Same journal

Applying large language models to spam detection in the Kazakh low-resource language setting.

Scientific reports·2026
Same journal

An open-source 3D printing system enabling in-situ freeze-thaw processing of hydrogels.

Scientific reports·2026
Same journal

An enhanced EfficientNet framework for automated waste classification using cosine annealing and label smoothing.

Scientific reports·2026
See all related articles

Related Experiment Videos

Unified spatial-temporal graph aggregation framework for predicting student performance.

Xian Yu1, Yifen Zhou2

  • 1Xinglin College, Nantong University, Nantong, 226001, China.

Scientific Reports
|April 14, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a unified spatial-temporal graph aggregation (USTGA) framework for accurate student performance prediction. USTGA effectively models complex academic data dependencies, outperforming existing methods.

Related Experiment Videos

Area of Science:

  • Educational Data Mining
  • Machine Learning in Education
  • Artificial Intelligence in Education

Background:

  • Accurate student performance prediction is vital for timely interventions and data-driven instruction.
  • Existing models often struggle with spatial and temporal dependencies in academic data.
  • Independent modeling of spatial and temporal aspects limits capturing intricate data relationships.

Purpose of the Study:

  • To propose a unified spatial-temporal graph aggregation (USTGA) framework for improved student performance prediction.
  • To integrate spatial similarity directly into temporal dependency modeling for a dynamic graph representation.
  • To overcome limitations of conventional models by jointly capturing fine-grained spatial-temporal interactions.

Main Methods:

  • Developed a unified spatial-temporal graph aggregation (USTGA) framework.
  • Embedded spatial similarity into temporal dependency modeling for dynamic graph evolution.
  • Employed an attention-guided aggregation strategy and stacked aggregation layers for refined node representations.

Main Results:

  • The USTGA framework consistently outperformed competitive baselines on an educational dataset.
  • Achieved significant reductions in multiple error metrics across repeated evaluations.
  • Demonstrated the framework's effectiveness, robustness, scalability, and adaptability.

Conclusions:

  • The proposed USTGA framework effectively captures complex spatial-temporal interactions in academic data.
  • USTGA offers a robust and scalable solution for educational analytics systems.
  • The framework is well-suited for applications like early-warning dashboards and program advising.