@inproceedings{349bc524b429492fadaa45214be4b798,
title = "Attention-LGBM-BiLSTM: An Attention-Based Ensemble Method for Knowledge Tracing",
abstract = "Knowledge tracing plays a vital role in measuring students' learning behaviors. In this paper, we propose a novel ensemble model: Attention-LGBM-BiLSTM for knowledge tracing. We utilize the attention mechanism combined with LGBM (Light Gradient Boosting Machine) to obtain a feature of the most importance. Combined with the first-round outputs of LGBM, it is imported into BiLSTM (Bidirectional Long Short-Term Memory), thus obtaining the final classification results. We implement and evaluate the model based on the largest open-source dataset, EdNet, in education area. The results show that the accuracy, AUC, and Fl-score of the model are higher than its baselines. An ablation test is also conducted to prove its effectiveness.",
keywords = "ensemble method, knowledge tracing, machine learning",
author = "Si Shi and Wuman Luo and Rita Tse and Giovanni Pau",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 11th IEEE International Conference on Teaching, Assessment and Learning for Engineering, TALE 2022 ; Conference date: 04-12-2022 Through 07-12-2022",
year = "2022",
doi = "10.1109/TALE54877.2022.00057",
language = "English",
series = "Proceedings - 2022 IEEE International Conference on Teaching, Assessment and Learning for Engineering, TALE 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "306--309",
booktitle = "Proceedings - 2022 IEEE International Conference on Teaching, Assessment and Learning for Engineering, TALE 2022",
address = "United States",
}