Model Analysis: Assessing the dynamics of student learning

TL;DR

This paper introduces a modeling and analysis method that uses mental models to quantitatively assess how instruction influences student understanding, exemplified through analysis of FCI results.

Contribution

It presents a novel approach combining cognitive modeling with test analysis to interpret student learning dynamics in physics education.

Findings

Identified common mental models among students

Mapped mental models onto multiple choice test data

Provided quantitative insights into instructional effects

Abstract

In this paper we present a method of modeling and analysis that permits the extraction and quantitative display of detailed information about the effects of instruction on a class's knowledge. The method relies on a congitive model that represents student thinking in terms of mental models. Students frequently fail to recognize relevant conditions that lead to appropriate uses of their models. As a result they can use multiple models inconsistently. Once the most common mental models have been determined by qualitative research, they can be mapping onto a multiple choice test. Model analysis permits the interpretation of such a situation. We illustrate the use of our method by analyzing results from the FCI.