Abstract
Analysis of patterns in time (APT) is a method for gathering information about observable phenomena such that probabilities of temporal patterns of events can be estimated empirically. If appropriate sampling strategies are employed, temporal patterns can be predicted from APT results. As an example of the fruitfulness of APT, it was discovered in a classroom observational study that elementary students were on task 97% of the time if some form of direct instruction was occurring also, whereas they were on task only 57% of the time during nondirect instruction. As a second example, APT results were used as a rule base for an expert system in adaptive computer-based testing. When two different computer tests were studied, average samples of 9 and 13 test items were required to make mastery and nonmastery decisions when items were selected at random. These decisions were, respectively, 94% and 98% accurate compared to those reached from two much larger test item pools. Finally, APT is compared to the linear models approach and event history analysis. The major difference is that in APT there is no mathematical model assumed to characterize relations among variables. In APT the model is the temporal pattern being investigated.
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