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Model analysis provides a mechanism for representing student learning as measured by standard multiple-choice surveys. The model plot contains information regarding both how likely students in a particular class are to choose the correct answer and how likely they are to choose an answer consistent with a well-documented conceptual model. Unfortunately Baos original presentation of the model plot did not include a way to represent uncertainty in these measurements. I present details of a method to add error bars to model plots by expanding the work of Sommer and Lindell. I also provide a template for generating model plots with error bars.
Traditional methods of reporting changes in student responses have focused on class-wide averages. Such models hide information about the switches in responses by individual students over the course of a semester. We extend unpublished work by Steven
Physics lab courses are an essential part of the physics undergraduate curriculum. Learning goals for these classes often include the ability to interpret measurements and uncertainties. The Physics Measurement Questionnaire (PMQ) is an established o
Physics lab courses are integral parts of an undergraduate physics education, and offer a variety of opportunities for learning. Many of these opportunities center around a common learning goal in introductory physics lab courses: measurement uncerta
In model-based testing (MBT) we may have to deal with a non-deterministic model, e.g. because abstraction was applied, or because the software under test itself is non-deterministic. The same test case may then trigger multiple possible execution pat
Probabilistic graphs are challenging to visualize using the traditional node-link diagram. Encoding edge probability using visual variables like width or fuzziness makes it difficult for users of static network visualizations to estimate network stat