Integrating Data Mining in Program Evaluation of K-12 Online Education

Research output: Contribution to journalArticlepeer-review

64 Scopus citations

Abstract

This study investigated an innovative approach of program evaluation through analyses of student learning logs, demographic data, and end-of-course evaluation surveys in an online K-12 supplemental program. The results support the development of a program evaluation model for decision making on teaching and learning at the K-12 level. A case study was conducted with a total of 7,539 students (whose activities resulted in 23,854,527 learning logs in 883 courses). Clustering analysis was applied to reveal students' shared characteristics, and decision tree analysis was applied to predict student performance and satisfaction levels toward course and instructor. This study demonstrated how data mining can be incorporated into program evaluation in order to generate in-depth information for decision making. In addition, it explored potential EDM applications at the K-12 level that have already been broadly adopted in higher education institutions.

Original languageAmerican English
Pages (from-to)27-41
Number of pages15
JournalEducational Technology and Society
Volume15
Issue number3
StatePublished - 1 Jul 2012

Keywords

  • Educational data mining
  • K-12 virtual school
  • Pattern discovery
  • Predictive modeling
  • Program evaluation

EGS Disciplines

  • Instructional Media Design

Fingerprint

Dive into the research topics of 'Integrating Data Mining in Program Evaluation of K-12 Online Education'. Together they form a unique fingerprint.

Cite this