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Found 7 papers, 1 papers with code
A review of clustering models in educational data science towards fairness-aware learning
These models are believed to be practical tools for analyzing students' data and ensuring fairness in EDS.
Evaluation of group fairness measures in student performance prediction problems
Predicting students' academic performance is one of the key tasks of educational data mining (EDM).
Multiple Fairness and Cardinality constraints for Students-Topics Grouping Problem
Group work is a prevalent activity in educational settings, where students are often divided into topic-specific groups based on their preferences.
Attention Mechanism based Cognition-level Scene Understanding
Given a question-image input, the Visual Commonsense Reasoning (VCR) model can predict an answer with the corresponding rationale, which requires inference ability from the real world.
A survey on datasets for fairness-aware machine learning
For a deeper understanding of bias in the datasets, we investigate the interesting relationships using exploratory analysis.
Fair-Capacitated Clustering
To this end, we introduce the fair-capacitated clustering problem that partitions the data into clusters of similar instances while ensuring cluster fairness and balancing cluster cardinalities.
Data augmentation for dealing with low sampling rates in NILM
An important step towards improving the performance of these energy disaggregation methods is to improve the quality of the data sets.
