Building a Better Teacher Through VAMs? Not So Fast According to Mark Paige's Book

Building a Better Teacher: Understanding Value-Added Models in the Law of Teacher Evaluation is a short and concise read that any administrator who currently encounters the use of value-added data in teacher evaluations should read.
Author:
Publish date:
Social count:
0


As a part of my research explorations, I stumbled across a relatively new book published in 2016 about the problems with using value-added measures in teacher evaluations. This book entitled Building a Better Teacher: Understanding Value-Added Models in the Law of Teacher Evaluation is a short and concise read that any administrator who currently encounters the use of value-added data in teacher evaluations should read.

Paige's argument is rather straightforward. Value-added models have statistical flaws and are highly problematic, and should not be used to make high-stakes decisions about educators. Scholars across the board have made clear that are problems with VAMs, enough problems that they should only be used in research and to cautiously draw conclusions about teaching. Later, Paige also provides advice to opponents to using value-added models in teacher education as well. Attempting to challenge the use of value-added models in teacher evaluations through the federal courts may be fruitless. According to Paige:

"At least at the federal level, courts will tolerate an unfair law, so long as it may be constitutional." p. 24

In other words, our courts will allow the use of VAMs in teacher evaluations, even if used unfairly. Instead, Paige encourages action on the legislative side. Educator opponents of VAMs should inform legislators of the many issues with the statistical measures and push for laws that restrict their use. In states with teacher unions, he encourages teachers to use the collective bargaining process to ensure that VAMs are not used unwisely.

Throughout Paige's short read, there are reviews of legal cases that have developed around the use of VAMs to determine teacher effectiveness and lots of information about the negative consequences of this practice.

Here are some key points from chapter 1 of Mark Paige's book Building a Better Teacher: Understanding Value-Added Models in the Law of Teacher Evaluation.

  • VAMs are statistical models that attempt to estimate a teacher's contribution to student achievement.
  • There are at least (6) different VAMs, each with relative strengths and weaknesses.
  • VAMs rely heavily on standardized tests to assess student achievement.
  • VAMs have been criticized on a number of grounds as offending various statistical principles that ensure accuracy. Scholars have noted that VAMs are biased and unstable, for example.
  • VAMs originated in the field of economics as a means to improve efficiency and productivity.
  • The American Statistical Association has cautioned against using VAMs in making causal conclusions between a teacher's instruction and a student's achievement as measured on standardized tests.
  • VAMS raise numerous nontechnical issues that are potentially problematic to the health of a school or learning climate. These include the narrowing of curriculum offerings and a negative impact on workforce morale.

Throughout his book, Paige offers numerous key points that should allow one to pause and interrogate the practice of using VAMs to determine teacher effectiveness.

Image placeholder title

cross posted at the21stcenturyprincipal.blogspot.com

J. Robinson has decades of experience as a K12 Principal, Teacher, and Technology Advocate. Read more at The 21st Century Principal.

Featured

Related

Reading

So, what about books?

Cross posted to Langwitches Blog  So, what about books? How do YOU read, buy, store and (maybe even) write books? Are you opposed to reading digital books? Are you clinging to the smell and feel of the hard cover? Are

Building a Better Enterprise

Envisioning the emerging data architecture paradigm in K-12 and how district leaders can use it to streamline operations, target instruction, and improve accountability.