Assessing quantitative modelling practices, metamodelling, and capability confidence of biology undergraduate students

ABSTRACT

Quantitative modelling plays an important role as biology increasingly deals with big data sets, relies on modelling to understand system dynamics, makes predictions about impacts of changes, and revises our understanding of system interactions. An assessment of quantitative modelling in biology was administered to students (n = 612) in undergraduate biology courses at two universities to provide a picture of student ability in quantitative reasoning within biology and to determine how capable those students felt about this ability. A Rasch analysis was used to construct linear measures and provide validity evidence for the assessment and to examine item statistics on the same scale as student ability measures. Students overall had greater ability in quantitative literacy than in quantitative interpretation of models or modelling. There was no effect of class standing (Freshmen, Sophomore, etc.) on student performance. The assessment showed that students who participated felt confidence in their ability to quantitatively model biological phenomena, even while their performance on ability questions were low. Collectively modelling practices were correlated with students’ metamodelling knowledge and not correlated with students’ modelling capability confidence. Biology instructors who incorporate the process of modelling into their courses may see improved abilities of students to perform on quantitative modelling tasks.

KEYWORDS:

• Models and modelling
• Rasch process
• higher education

Acknowledgements

We thank the instructors who graciously agreed to allow us to assess their students. We thank Autumn Fluent for her logistical and technical support. We thank A. Schuchardt and M. Aikens for providing expert reviews of the assessment. This work emerged from a working group organised by QUBES, and the attendees have directly and indirectly impacted this work throughout its development. Comments from two anonymous reviewers greatly improved this paper.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability

University of Nebraska-Lincoln Data Repository (QM BUGS assessment).