Over the last decades, there has been significant interest in using computer modelling tools to support students’ learning of science (Khine & Saleh, 2011; Seel, 2014). Computer models and model-based reasoning have been seen as potentially beneficial for learning complex scientific knowledge for several reasons. First, studies on scientific discovery and conceptual change have shown that the process of manipulating visual models provides scientists with very significant means through which change in conceptual understanding and scientific discovery occur (Magnani et al., 2002; Nersessian, 2008). Thus, similarly, it is expected that computer simulations and models could provide students with essential mediating tools through which the learning of complex scientific phenomena might occur. In particular, by manipulating model parameters, actively observing, and interpreting emerging patterns, students could grasp the principles of complex, often invisible and counterintuitive emerging phenomena, such as carbon cycle and greenhouse effect (Goldstone & Wilensky, 2008; Kelly et al., 2012). Second, studies on science learning and scientific beliefs have generally reported that students have impoverished views about the nature of scientific work and limited understanding about scientific inquiry strategies (Gobert et al., 2010; Lederman, 1992; Sadler et al., 2010). Authentic exploration of computer models provides students with first-hand experiences of model-based reasoning and inquiry, which presents students with opportunities to improve their inquiry strategies and supports deeper understanding of the scientific work (Lindgren & Schwartz, 2009; Nersessian, 2008).