Comprehensive understanding of complex biological systems necessitates the use of computational models because they facilitate visualisation and interrogation of system dynamics and data-driven analysis. Computational model-based (CMB) activities have demonstrated effectiveness in improving students’ understanding and their ability to use and reason with models. To maximise the effectiveness of computational modelling, this study examined an improved cognitive scaffolding and its impact on student learning of cellular respiration. This scaffolding proposes the predict-observe-revise-explain (PORE) sequence of tasks that explicitly challenge students to revise their predictions and computational models to resolve cognitive conflict. Based on revision work in a CMB activity, a sample of n = 362 undergraduate biology students were categorised into three groups–not expected to revise (NR, n = 109), required-revised (RR, n = 179), and required-did not revise (RDNR, n = 74). Students’ performance in predict, revise, and explain tasks were significantly associated with post-test performance. RR students were more than twice as likely to demonstrate a positive learning gain in the post-test (odds ratio = 2.47) compared to RDNR students. While science education has implicitly acknowledged revision as a critical cognitive process in modelling, this study presents evidence that making revision an explicit cognitive task in a CMB activity supports student learning of a complex biological system.
- Computational model-based activity
- predict-observe-explain (POE) strategy
- undergraduate life sciences education
ASJC Scopus subject areas