Overview

This repository contains supplementary materials for the following conference paper:

Sukrit Leelaluk, Cheng Tang, Valdemar Švábenský, and Atsushi Shimada.
Knowledge Distillation in RNN-Attention Models for Early Prediction of Student Performance.
In Proceedings of the 40th ACM/SIGAPP Symposium on Applied Computing (SAC 2025).
DOI: 10.1145/3672608.3707805

Preprint: https://arxiv.org/pdf/2412.14526

@inproceedings{Leelaluk2025knowledge,
    author    = {Leelaluk, Sukrit and Tang, Cheng and \v{S}v\'{a}bensk\'{y}, Valdemar and Shimada, Atsushi},
    title     = {{Knowledge Distillation in RNN-Attention Models for Early Prediction of Student Performance}},
    booktitle = {Proceedings of the 40th ACM/SIGAPP Symposium on Applied Computing},
    series    = {SAC '25},
    location  = {Sicily, Italy},
    publisher = {Association for Computing Machinery},
    address   = {New York, NY, USA},
    month     = {03},
    year      = {2025},
    numpages  = {10},
    isbn      = {979-8-4007-0629-5},
    url       = {https://doi.org/10.1145/3672608.3707805},
    doi       = {10.1145/3672608.3707805},
}

File description

utils/rnn_attention_kd.py

Show the structure of RNN-Attention-KD
See Section 3.2 in the paper.

01_teacher_model_training.ipynb

Training the teacher model using the entire course data to prepare for distilling knowledge to the student model.
See Section 3.2 and 4.2.2 in the paper.

02_student_model_training.ipynb

Training the student model using the knowledge from the teacher model.
See Section 3.2 and 4.2.2 in the paper.