On Developing Generic Models for Predicting Student Outcomes in Educational Data Mining

Poor academic performance of students is a concern in the educational sector, especially if it leads to students being unable to meet minimum course requirements. However, with timely prediction of students’ performance, educators can detect at-risk students, thereby enabling early interventions for...

詳細記述

書誌詳細

出版年:	Big Data and Cognitive Computing
主要な著者:	Gomathy Ramaswami, Teo Susnjak, Anuradha Mathrani
フォーマット:	論文
言語:	英語
出版事項:	MDPI AG 2022-01-01
主題:	machine learning early prediction CatBoost at-risk students educational data mining
オンライン･アクセス:	https://www.mdpi.com/2504-2289/6/1/6