ABSTRACT Arguably one of the most valuable tools for investigating pupil behaviour in an educational environment is systematic classroom observation. Classroom observation is often cited as having the potential to enable research of the learning process in action. Low inference classroom observation instruments are designed to record a sequence of data points consisting of successive measurements made over a time interval, making them particularly appropriate as measurement tools of the latent learning process. This study aimed to demonstrate that the frequently used method of analyzing this inherently temporal data proportionally is failing to consider variability between individual pupils that is identifiable in the original time series. Using a combination of real and simulated data, substantially more unique patterns of behaviour were found with time series analyses of the same data than with proportional analyses. It was also found that proportional analyses explained very little variability in the time series data of the same pupils. Modifying the number of samples and categories in an observation schedule did not vastly improve the relationship between proportional and time series analyses. We argue that if the methodological focus of classroom observation is on examining differences between pupils, then methods that maintain temporal aspects are preferable.

中文翻译：

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重新考虑系统课堂观察的方法：分类时间序列观察的测量和分析

摘要可以说，有系统的课堂观察是调查教育环境中学生行为的最有价值的工具之一。课堂观察经常被认为有潜力使研究学习过程付诸实践。低推理教室观察仪器旨在记录一系列数据点，这些数据点包含在一个时间间隔内进行的连续测量，因此特别适合作为潜伏学习过程的测量工具。这项研究旨在证明按比例分析这种固有时间数据的常用方法未能考虑到在原始时间序列中可识别的各个学生之间的差异。结合实际数据和模拟数据，在相同数据的时间序列分析中，与比例分析相比，发现的行为模式要独特得多。还发现，比例分析解释了同一学生的时间序列数据中的变化很小。在观察计划中修改样本和类别的数量并不能极大地改善比例分析和时间序列分析之间的关系。我们认为，如果课堂观察的方法论重点是检查学生之间的差异，那么保持时间方面的方法是可取的。在观察计划中修改样本和类别的数量并不能极大地改善比例分析和时间序列分析之间的关系。我们认为，如果课堂观察的方法论重点是检查学生之间的差异，那么保持时间方面的方法是可取的。在观察计划中修改样本和类别的数量并不能极大地改善比例分析和时间序列分析之间的关系。我们认为，如果课堂观察的方法论重点是检查学生之间的差异，那么保持时间方面的方法是可取的。