期刊论文

Du, Xu

英文

IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES

1939-1382

2019

12

2

148-157

10.1109/TLT.2019.2911072

Manuscript received August 30, 2018; revised March 29, 2019; accepted April 9, 2019. Date of publication April 15, 2019; date of current version June 17, 2019. This work was supported by the National Natural Science Foundation of China under Grant 61877027. (Corresponding author: Xu Du.) J.-L. Hung is with the National Engineering Laboratory for Educational Big Data, Central China Normal University, Wuhan 430079, China, and the Department of Educational Technology, Boise, ID 83725, USA (e-mail: andyhung@boisestate.edu). B. E. Shelton is with the Department of Educational Technology, Boise State University, Boise, ID 83725, USA (e-mail: brettshelton@boisestate.edu). J. Yang and X. Du are with the National Engineering Research Center for E-Learning, Central China Normal University, Wuhan 430079, China (e-mail: yangjuan_hust@163.com; duxu@mail.ccnu.edu.cn). Digital Object Identifier 10.1109/TLT.2019.2911072

本校为第一且通讯机构

Performance prediction is a leading topic in learning analytics research due to its potential to impact all tiers of education. This study proposes a novel predictive modeling method to address the research gaps in existing performance prediction research. The gaps addressed include: the lack of existing research focus on performance prediction rather than identifying key performance factors; the lack of common predictors identified for both K-12 and higher education environments; and the misplaced focus on absolute engagement levels rather tha...