@inproceedings{000b67cdcb83467082c592f6c67e14e8,
title = "Two-Stage Predictive Modeling for Identifying At-Risk Students",
abstract = "This study proposes an analytic approach which combines two predictive models (the predictive model of successful students and the predictive model of at-risk students) to enhance prediction performance for use under the constraints of limited data collection. A case study was conducted to examine the effects of the model combination approach. Eight variables were collected from a data warehouse and the Learning Management System. The best model was selected based on the lowest misclassification rate in the validation dataset. The confusion matrix compares the model{\textquoteright}s performance with the following parameters: accuracy, misclassification, and sensitivity. The results show the new combination approach can capture more at-risk students than the singular predictive model, and is only suitable for the ensemble predictive algorithms.",
keywords = "Academic at-risk factors, Academic success factors, Ensemble model, Learning analytics",
author = "Shelton, \{Brett E.\} and Juan Yang and Hung, \{Jui Long\} and Xu Du",
note = "Publisher Copyright: {\textcopyright} 2018, Springer Nature Switzerland AG.; 1st International Conference on Innovative Technologies and Learning, ICITL 2018 ; Conference date: 27-08-2018 Through 30-08-2018",
year = "2018",
doi = "10.1007/978-3-319-99737-7\_61",
language = "American English",
isbn = "9783319997360",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "578--583",
editor = "Lin Lin and Ting-Ting Wu and Yueh-Min Huang and Yueh-Min Huang and Starcic, \{Andreja Istenic\} and Rustam Shadieva",
booktitle = "Innovative Technologies and Learning: First International Conference, ICITL 2018, Portoroz, Slovenia, August 27–30, 2018, Proceedings",
address = "Germany",
}