@inproceedings{d43f8a58b5c446c9aae6513bc31cdca4,
title = "Prediction of students{\textquoteright} academic performance based on tracking logs",
abstract = "In this paper, we predict students{\textquoteright} academic performance based on tracking log of students{\textquoteright} learning activities. We compare the prediction of six datasets from Kyoto University (KU), National Central University (NCU), and Chung Yuan Christian University (CYCU) by eight classification models. We use the evaluators of accuracy, recall, precision, F1-score, and Area Under the Curve (AUC) of Receiver Operating Characteristic (ROC). According to the prediction results, we found that sample size and feature category influence the prediction performance of classification. We also found that the significant features based on Pearson correlation analysis have greatly influence on the prediction performance of classification.",
keywords = "Academic performance, Classification",
author = "Hung, \{Anna Y.Q.\} and Weng, \{Jian Xuan\} and Huang, \{Jeff C.H.\} and Lu, \{Owen H.T.\} and Jong, \{Bin Shyan\} and Yang, \{Stephen J.H.\}",
note = "Publisher Copyright: {\textcopyright} 2018 Asia-Pacific Society for Computers in Education..All Rights Reserved.; 26th International Conference on Computers in Education, ICCE 2018 ; Conference date: 26-11-2018 Through 30-11-2018",
year = "2018",
month = nov,
day = "24",
language = "???core.languages.en\_GB???",
series = "ICCE 2018 - 26th International Conference on Computers in Education, Workshop Proceedings",
publisher = "Asia-Pacific Society for Computers in Education",
pages = "467--476",
editor = "Lung-Hsiang Wong and Michelle Banawan and Niwat Srisawasdi and Yang, \{Jie Chi\} and Rodrigo, \{Ma. Mercedes T.\} and Maiga Chang and Ying-Tien Wu",
booktitle = "ICCE 2018 - 26th International Conference on Computers in Education, Workshop Proceedings",
}