Refining the WLR: quantifying the difference in learning success between courses

Pre- and posttests are often used in Engineering Education Research to assess teaching and learning. In some cases, the pretest and the posttest are different instruments, making it difficult to analyze the data with traditional methods such as learning gain. Previously, we proposed a statistical method, the so-called Weighted Linear Regression (WLR), to analyze such data. The WLR is a tool for teachers and researchers to effectively compare two or more courses or cohorts based on data from non-identical pre- and posttests (NIPPs). So far, however, it has not been used to statistically quantify the difference between courses or cohorts. This work refines the current WLR method so that the difference in learning success between courses using NIPPs can be quantified. Consequently, informed conclusions can be drawn if courses differ in effectiveness.

DDC Class

000: Allgemeines, Wissenschaft

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Refining the WLR: quantifying the difference in learning success between courses