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Repository for the LAK 2023 paper: "Protected Attributes Tell Us Who, Behavior Tells Us How: A Comparison of Demographic and Behavioral Oversampling for Fair Student Success Modeling" by Jade Mai Cock, Muhammad Bilal, Richard Lee Davis, Mirko Marras and Tanja Käser

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epfl-ml4ed/behavioral-oversampling

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blind-mitigation

Repository's structure

├── data
│   ├── beerslaw
│   └── PISA
├── notebooks
│   ├── 0_data_processing
│   ├── 1_algos_investigation
│   └── 2_dirty_prototypes
│   	├── oversampling
│   	└── clustering
├── src
│   ├── utils
│   ├── configs
│   ├── data_handlers
│   ├── ml
│   └── visualisers
├── experiments
│   ├── oversample
│   └── clustering
└── reports
    ├── presentation
    └── reports

Launching the oversampling script

  1. configure the experiment by editing the src/configs/oversampling_config.yaml
  2. place yourself in the src folder
  3. run python script_oversampling --oversamplesimple

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Repository for the LAK 2023 paper: "Protected Attributes Tell Us Who, Behavior Tells Us How: A Comparison of Demographic and Behavioral Oversampling for Fair Student Success Modeling" by Jade Mai Cock, Muhammad Bilal, Richard Lee Davis, Mirko Marras and Tanja Käser

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