Time-series designs are an alternative to pretest-posttest methods that are able to identify and measure the impacts of multiple educational interventions, even for small student populations. Here, we use an instrument employing standard multiple-choice conceptual questions to collect data from students at regular intervals. The questions are modified by asking students to distribute 100 Confidence Points among the options in order to indicate the perceived likelihood of each answer option being the correct one. Tracking the class-averaged ratings for each option produces a set of time-series. ARIMA (autoregressive integrated moving average) analysis is then used to test for, and measure, changes in each series. In particular, it is possible to discern which educational interventions produce significant changes in class performance. Cluster analysis can also identify groups of students whose ratings evolve in similar ways. A brief overview of our methods and an example are presented.