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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

Background. Inquiry learning environments increasingly incorporate simulation and modeling facilities. Students acquire knowledge through systematic experimentation with the simulations and express that knowledge in runnable computer models. Aim. As inquiry and modeling activities are new and demanding for students, support for learning is needed. This article reports three experimental studies that examine how students’ inquiry and modeling activities can be supported. Need for support. Study 1 was an empirical assessment of students’ support needs. It compared a group of domain novices to two more knowledgeable reference groups in order to determine the novices’ support needs. Model progression and worked examples. In Studies 2 and 3, the need for support was addressed by model progression (gradually increasing task complexity) and worked-out examples, examining the effect of those interventions on students’ performance and learning. Results suggest positive effects due to both increasing model complexity and providing worked examples that show what the activities in each model progression phase entail and how they should be performed. Implications. The pattern of results across the three studies are discussed with regard to students’ use of available resources, influence of prior knowledge, and the relationship between performance and learning.

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Learning from computer models is a promising approach to learning. This study investigated how three types of learning from computer models can be applied to teach high-school students (aged 14-17) about the process of glucose-insulin regulation. Two traditional forms of learning from models (i.e. simulating a predefined model and constructing a model) were compared to learning from an erroneous model. In this innovative form of learning from computer models, students are provided with a model that contained errors to be corrected. As such, students do not have to engage in the difficult task of constructing a model. Rather, they are challenged to work with and correct the model in order for the simulation to generate correct output. As predicted, learning from erroneous models enhances learning of domain-specific knowledge better than running a simulation or constructing a model.