No abstract

No abstract

No abstract

Model progression denotes the organization of the inquiry learning process in successive phases of increasing complexity. This study investigated the effectiveness of model progression in general, and explored the added value of either broadening or narrowing students' possibilities to change model progression phases. Results showed that high-school students in the 'standard' model progression condition (n = 19), who could enter subsequent phases at will, outperformed students from a control condition (n = 30) without model progression. The unrestricted condition (n = 22) had the additional option of returning to previous phases, whereas the restricted condition (n = 20) disallowed such downward progressions as well as upward progressions in case insufficient knowledge was acquired. Both variants were found to be more effective in terms of performance than the 'standard' form of model progression. However, as performance in all three model progression conditions was still rather weak, additional support is needed for students to reach full understanding of the learning content.

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 ¼ 26), an extended partial model that also contained a set of variables students could use to complete the model (PMþ condition; n ¼ 21), or no support (control condition; n ¼ 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 PMþ 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 Keywords: learning by modelling; completion problems; worked-out examples Dutch summary (Nederlandse samenvatting): Modelleren is een veelbelovende manier voor het leren van natuurwetenschappelijke principes en verschijnselen. Leren door te modelleren is echter niet eenvoudig: het maken van een werkend computermodel blijkt voor veel leerlingen een struikelblok te zijn. Het aanbieden van een gedeeltelijk uitgewerkt model zou deze problemen kunnen doen verminderen. In een gedeeltelijk uitgewerkt model wordt de algemene modelstructuur gegeven, wat de leerlingen kan helpen om een beter model te maken en de inhoud ervan beter te begrijpen. Deze hypotheses zijn onderzocht tijdens een biologiepracticum waarin leerlingen uit de bovenbouw van het VWO een computermodel van het glucoseinsuline regulatieproces moesten maken. Een deel van de leerlingen had hierbij de beschikking over een gedeeltelijk uitgewerkt model (PM conditie; n ¼ 26). De tweede groep kreeg hetzelfde uitgewerkte model plus een lijst met variabelen die zij aan het model konden toevoegen (PMþ conditie; n ¼ 21), terwijl de