Improving Predictive Modeling for At-Risk Student Identification: A Multistage Approach: A Multistage Approach

Jui-Long Hung, Brett E. Shelton, Juan Yang, Xu Du

Research output: Contribution to journalArticlepeer-review

45 Scopus citations

Abstract

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 than relative engagement levels. Two datasets, one from higher education and the other from a K-12 online school with 13 368 students in more than 300 courses, were applied using the predictive modeling technique. The results showed the newly suggested approach had higher overall accuracy and sensitivity rates than the traditional approach. In addition, two generalizable predictors were identified from instruction-intensive and discussion-intensive courses.

Original languageAmerican English
Article number8691494
Pages (from-to)148-157
Number of pages10
JournalEducational Technology Faculty Publications and Presentations
Volume12
Issue number2
DOIs
StatePublished - 1 Apr 2019

Keywords

  • analytical models
  • educational technology
  • learning management systems (LMS)
  • predictive methods
  • predictive models

EGS Disciplines

  • Instructional Media Design

Fingerprint

Dive into the research topics of 'Improving Predictive Modeling for At-Risk Student Identification: A Multistage Approach: A Multistage Approach'. Together they form a unique fingerprint.

Cite this