Saturday, July 30, 2016

Algorithms in Use: Evaluating Teachers and “Personalizing” Learning (Part 2) | Larry Cuban on School Reform and Classroom Practice

Algorithms in Use: Evaluating Teachers and “Personalizing” Learning (Part 2) | Larry Cuban on School Reform and Classroom Practice:

Algorithms in Use: Evaluating Teachers and “Personalizing” Learning (Part 2)


In Part 1, I made the point that consumer-driven or educationally-oriented algorithms for all of their mathematical exactness and appearance of objectivity in regression equations contain different values among which programmers judge some to be more important than others.  In making value choices (like everyone else, programmers are constrained by space, time, and resources), decisions get made that have consequences for both teachers and students. In this post, I look first at those algorithms used to judge teachers’ effectiveness (or lack of it) and then I turn to “personalized learning” algorithms customized for individual students.
Washington, D.C.’s IMPACT program of teacher evaluation
Much has been written about the program that Chancellor Michelle Rhee created during her short tenure (2007-2010) leading the District of Columbia public schools (see here and here). Under Rhee, IMPACT,  a new system of teacher evaluation has been put into practice. The system is anchored in The “Teaching and Learning Framework,”  that D.C. teachers call the “nine commandments” of good teaching.
1. Lead well-organized, objective-driven lessons.
2. Explain content clearly.
3. Engage students at all learning levels in rigorous work.
4. Provide students with multiple ways to engage with content.
5. Check for student understanding.
6. Respond to student misunderstandings.
7. Develop higher-level understanding through effective questioning.
8. Maximize instructional time.
9. Build a supportive, learning-focused classroom community.
IMPACT uses multiple measures to judge the quality of teaching. At first, 50 percent of an annual evaluation was based upon student test scores; 35 percent based on judgments of instructional expertise (see “nine commandments”) drawn from five classroom observations by the principal and “master educators,” and 15 percent based on other measures. Note that policymakers initially decided on Algorithms in Use: Evaluating Teachers and “Personalizing” Learning (Part 2) | Larry Cuban on School Reform and Classroom Practice:

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