Valid and reliable measurements of teaching quality facilitate school-level decision-making and policies pertaining to teachers. Using nearly 1,000 word-to-word transcriptions of fourth- and fifth-grade English language arts classes, we apply novel text-as-data methods to develop automated measures of teaching to complement classroom observations traditionally done by human raters. This approach is free of rater bias and enables the detection of three instructional factors that are well aligned with commonly used observation protocols: classroom management, interactive instruction, and teacher-centered instruction. The teacher-centered instruction factor is a consistent negative predictor of value-added scores, even after controlling for teachers’ average classroom observation scores. The interactive instruction factor predicts positive value-added scores. Our results suggest that the text-as-data approach has the potential to enhance existing classroom observation systems through collecting far more data on teaching with a lower cost, higher speed, and the detection of multifaceted classroom practices.

有效和可靠的教学质量评估有助于学校一级的决策和有关教师的政策。我们使用四年级和五年级英语艺术课的近千个单词到单词的转录，我们运用新颖的“文本作为数据”方法来开发自动化的教学手段，以补充传统上由人类评估者进行的课堂观察。这种方法没有评估者的偏见，并且能够检测出与常用观察协议完全吻合的三个教学因素：教室管理，交互式教学和以教师为中心的教学。以教师为中心的教学因素即使在控制了教师的平均课堂观察分数之后，仍是增值分数的一致否定预测因子。互动式教学因素可预测积极的增值得分。我们的研究结果表明，“文本即数据”方法具有潜力，可以通过以更低的成本，更高的速度以及检测多面教室实践的方式收集更多有关教学的数据来增强现有的教室观察系统。