Data reduction methods applied to understanding complex learning hypotheses
Abstract
Modern learning science researchers are facing a flood of data as it becomes easier and easier to collect multiple streams of information from students before, during, and after learning experiments. While oftentimes these experiments do experimentally manipulate specific variables to improve responses on a posttest, these experiments are also interested in how the many related student factors explain who responds to the treatment and why. This poster introduces a recent experiment and explains how the data were analyzed using a combination of exploratory factor analysis (using SPSS) and exploratory structural equation modeling (using Tetrad) to partially refute a theoretical hypothesis and reveal a new explanation for further testing.
Publication Title
Educational Data Mining 2010 - 3rd International Conference on Educational Data Mining
Recommended Citation
Pavlik, P. (2010). Data reduction methods applied to understanding complex learning hypotheses. Educational Data Mining 2010 - 3rd International Conference on Educational Data Mining, 311-312. Retrieved from https://digitalcommons.memphis.edu/facpubs/7673
