The online
error analysis items challenged preservice teachers to analyze, diagnose, and provide targeted instructional remediation intended to help mock students overcome common error patterns and misconceptions.
This article describes how a free, web - based intelligent tutoring system, (ASSISTment), was used to create online
error analysis items for preservice elementary and secondary mathematics teachers.
A short description of how the ASSISTment system was used to support follow - up in - class discussions among preservice teachers is provided, as well as suggestions for producing similar online
error analysis items in other content areas.
Directions for accessing all of the mathematics error analysis problem sets currently available in the ASSISTment system, sample
error analysis items and responses, and a rubric for implementing these assignments in mathematics methods classes to support preservice teachers are included at the conclusion of the article.
Not exact matches
However, we recommend that researchers control the measurement
errors (e.g., correlated residuals between
items) before conducting further
analysis.
Mplus v7.11 was used for all
analyses.23 SDQ
items were treated as ordinal, with weighted least - squares means and variance — adjusted estimation used.23 Given the χ2 statistic's propensity to reject good models when samples are large and / or complex, the comparative fit index (CFI) and root mean square
error of approximation (RMSEA) were used to assess model fit.