Scaffolding learning by modelling: The effects of partially worked‐out models

Abstract

Abstract Creating executable computer models is a potentially powerful approach to science learning. Learning by modelling is also challenging because students can easily get overwhelmed by the inherent complexities of the task. This study investigated whether offering partially worked‐out models can facilitate students’ modelling practices and promote learning. Partially worked‐out models were expected to aid model construction by revealing the overall structure of the model, and thus enabling student to create better models and learn from the experience. This assumption was tested in high school biology classes where students modelled the human glucose‐insulin regulatory system. Students either received support in the form of a partial model that outlined the basic structure of the glucose‐insulin system (PM condition; n = 26), an extended partial model that also contained a set of variables students could use to complete the model (PM+ condition; n = 21), or no support (control condition; n = 23). Results showed a significant knowledge increase from pretest to posttest in all conditions. Consistent with expectations, knowledge gains were higher in the two partial model conditions than in the control condition. Students in both partial model conditions also ran their model more often to check its accuracy, and eventually built better models than students from the control condition. Comparison between the PM and PM+ conditions showed that more extensive support further increased knowledge acquisition, model quality, and model testing activities. Based on these findings, it was concluded that partial solutions can support learning by modelling, and that offering both a structure of a model and a list of variables yields the best results. © 2015 Wiley Periodicals, Inc. J Res Sci Teach 53:502–523, 2016

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