An item response theory evaluation of the Light and Spectroscopy Concept Inventory national data set

TL;DR

This study applies item response theory to analyze national data from the Light and Spectroscopy Concept Inventory, revealing how active learning strategies impact student learning gains in introductory astronomy courses.

Contribution

It provides the first IRT-based analysis of the LSCI data, offering deeper insights into student learning and teaching effectiveness in astronomy education.

Findings

Active learning increases average IRT-estimated gains by about 1 logit.

At least 25% of class time on active learning is needed for significant improvement.

Single measures of learning gain may obscure individual student progress.

Abstract

This paper presents the first item response theory (IRT) analysis of the national data set on introductory, general education, college-level astronomy teaching using the Light and Spectroscopy Concept Inventory (LSCI). We used the difference between students' pre- and post-instruction IRT-estimated abilities as a measure of learning gain. This analysis provides deeper insights than prior publications into both the LSCI as an instrument and into the effectiveness of teaching and learning in introductory astronomy courses. Our IRT analysis supports the classical test theory findings of prior studies using the LSCI with this population. In particular, we found that students in classes that used active learning strategies at least 25\% of the time had average IRT-estimated learning gains that were approximately 1 logit larger than students in classes that spent less time on active learning strategies. We also found that instructors who want their classes to achieve an improvement in abilities of average $\Delta \theta = 1$ logit must spend at least 25\% of class time on active learning strategies. However, our analysis also powerfully illustrates the lack of insight into student learning that is revealed by looking at a single measure of learning gain, such as average $\Delta \theta$. Educators and researchers should also examine the distributions of students' abilities pre- and post-instruction in order to understand how many students actually achieved an improvement in their abilities and whether or not a majority of students have moved to post-abilities significantly greater than the national average.