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Cross-Classified Multilevel Modeling for Program Evaluation of State Virtual School

Thu, April 27, 4:05 to 5:35pm, Henry B. Gonzalez Convention Center, Floor: Ballroom Level, Hemisfair Ballroom 2

Abstract

The author examined institutional performance of a state virtual school using data from the learning management and customer management systems. To address the unique structure of virtual school data, the cross-classified multilevel modeling was used. Key findings include: (a) students who took courses for the credit recovery or learning preference are more likely to underperform in comparison to those with such reasons as schedule conflicts or unavailable courses at their schools; (b) female students are more likely to succeed; and (c) novice teachers with supports via the institute’s induction program performed as well as other experienced teachers can. The author is calling for more research on K-12 online learner’s attributes and the factors that account for the student success.

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