Assessing students’ approaches to modelling in undergraduate biology

ABSTRACT

In this study, we propose an ‘Approach to Modeling’ (AtM) framework for examining how undergraduates approach tasks that require modelling scientific phenomena. Our framework is adapted from Approach to Learning (AtL) theories and consists of three observable behavioural constructs: metacognition, generative thinking, and causal reasoning. Twenty students participated in two think-aloud interviews, one year apart, in which they engaged in a total of five modelling tasks. We used AtM to analyse and score students’ modelling behaviours. We concluded that: (1) students’ AtM is associated with, but cannot be predicted by their previous academic achievement, and (2) students’ AtM is not an inherent characteristic of the individual, but differs as modelling tasks change. We discuss the influence of prompt construction on AtM; specifically the presence of cueing words, the type and amount of background information provided, and familiarity with the contexts and concepts addressed in the prompts. In addition, we discuss connections between AtM and broader notions of science learning. We conclude with recommendations for constructing modelling prompts that may encourage diverse learners to engage more deeply in modelling tasks.

KEYWORDS:

• Modelling
• scientific modelling
• approaches to learning
• deep/surface learning
• model-based instruction
• model-based learning

Acknowledgements

We thank Dr. Caleb Trujillo, E. Usoro, S. Chan, H. Rose and Dr. Elena Bray Speth for assisting with the interviews and providing analytical support throughout the project.

Disclosure statement

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

Additional information

Funding

This work was supported by the National Science Foundation [grant number DRL 1420492].