Dynamic Models of Learning and Education Measurement
(Submitted on 6 Oct 2007)
Pre-post testing is a commonly used method in physics education for evaluating students' achievement and or the effectiveness of teaching through a short period of instruction. A popular method to analyze pre-post testing results is the normalized gain first brought to the physics education community in wide use by R. Hake. In his analysis with thousands of students' pre-post test results, it has been observed that students having very different pretest scores tend to have similar normalized gains when going through similar types of instruction, i.e., classes with traditional instruction often have systematically lower gains than classes with research-based collaborative types of instruction. This feature allows researchers to investigate the effectiveness of instruction using data collected from classes with different initial states. However, the question of why the normalized gain has this feature and to what extend this feature will be valid is not well understood. Recently, there have been debates on what the normalized gain is actually measuring and concerns that the normalized gain lacks a probability framework comparing to other methods such as Item Response Theory (IRT). Motivated by searching for answers to these questions, a theoretical model about the dynamic process of learning have been developed, which leads to an explanatory interpretation of the features of the normalized gain. Further the model also connects well to other models and methods such as IRT and shows that the normalized gain does have a probabilistic framework but one different from what the IRT emphasizes. This paper will report the basic theoretical formalism of the new model and explore its applications in data modeling and analysis.
Comments: Theoretical Models of Education Measurement
Subjects: Physics Education (physics.ed-ph); Data Analysis, Statistics and Probability (physics.data-an)
Cite as: arXiv:0710.1375v1 [physics.ed-ph]