Reading through the report from the ASA (that doesn't really "slam" the VAM statistic but rightly points out the flaws inherent to any attempt to use statistics in areas with many confounding factors), it appears as if the VAM is usually derived thusly:
1. Calculate a regression model for a student's expected standardized test scores based off of background variables (like previous scores, socioeconomic status etc). This includes having teacher's as variables. 2. Use the coefficient for the teacher as determined by the model to determine the teacher's "Value Added" metric.
The weaknesses in such an approach are also spelled out in the report: namely, missing background variables, lack of precision, and a lack of time to test for the effectiveness of the statistics themselves.
What's interesting is that the teacher in question was rated as "effective" the year before. The question becomes whether that was based off of her VAM score that year as well as what the standard error was on her regression coefficient. Unfortunately, the article doesn't mention any of that.