Developing early warning systems to predict students’ online learning performance
學年 102
學期 2
出版（發表）日期 2014-07-01
作品名稱 Developing early warning systems to predict students’ online learning performance
作品名稱（其他語言）
著者 Hu, Ya-Han; Lo, Chia-Lun; Shih, Sheng-Pao
單位 淡江大學資訊管理學系
出版者 Kidlington: Pergamon Press
著錄名稱、卷期、頁數 Computers in Human Behavior 36, pp.469-478
摘要 An early warning system can help to identify at-risk students, or predict student learning performance by analyzing learning portfolios recorded in a learning management system (LMS). Although previous studies have shown the applicability of determining learner behaviors from an LMS, most investigated datasets are not assembled from online learning courses or from whole learning activities undertaken on courses that can be analyzed to evaluate students’ academic achievement. Previous studies generally focus on the construction of predictors for learner performance evaluation after a course has ended, and neglect the practical value of an “early warning” system to predict at-risk students while a course is in progress. We collected the complete learning activities of an online undergraduate course and applied data-mining techniques to develop an early warning system. Our results showed that, time-dependent variables extracted from LMS are critical factors for online learning. After students have used an LMS for a period of time, our early warning system effectively characterizes their current learning performance. Data-mining techniques are useful in the construction of early warning systems; based on our experimental results, classification and regression tree (CART), supplemented by AdaBoost is the best classifier for the evaluation of learning performance investigated by this study.
關鍵字 Learning management system; e-Learning; Early warning system; Data-mining; Learning performance prediction
語言 en
ISSN 0747-5632
期刊性質 國外
收錄於 SSCI
產學合作
通訊作者 Shih, Sheng-Pao
審稿制度 是
國別 GBR
公開徵稿
出版型式 ,電子版
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/100506 )

機構典藏連結