Data-based student learning objectives for teacher evaluation

Abstract

Student learning objectives (SLOs) have become an increasingly popular tool for teacher evaluations as an alternative to Value-added Models (VAMs). However, the use of SLOs faces two major challenges. First, the target setting is mostly subjective and arbitrary. Second, there is little evidence on the reliability and validity of the tool. In this paper, we proposed three data-based SLO target-setting models: split, banded, and class-wide models. The data-based approach ensures that the targets set for students are challenging yet realistic and achievable. Using data of 176 pre-kindergarten teachers and two cohorts of students from a large school district in Texas, we investigated the reliability and predictive validity of teachers’ SLO scores. Results indicated that teachers’ SLO scores had moderate to high consistency across different subtests, and moderate stability over time. Teachers’ SLO scores were also demonstrated to be useful in predicting future students’ achievement, which supported the predictive validity of the tool.