| Computer-based simulations effectively support the acquisition of scientific knowledge when combined with a guided learning approach. Active learning drives complex cognitive processes that enable the integration of new information with existing knowledge. The iCAP (Interactive, Constructive, Active, Passive) Framework provides a conceptual model to describe different types of active learning. Computer-based simulations fit neatly within this framework. Similarly, self-explanation is a generative learning strategy that fits within this framework. Promoting self-explanation using instructional prompts is an effective method for driving application of the strategy. This study compared three combinations of self-explanation prompt and learner activity (closed prompts---overt activity, open prompts---overt activity, open prompts---non-overt activity) when using an instructional simulation to acquire knowledge related to scientific principles. Outcome measures included pretest-posttest comparisons, cognitive load, and self-efficacy.;Results of the study indicated that closed prompts were more effective in driving application of the self-explanation learning strategy and learning outcomes when used within the context of an instructional simulation. Findings were less conclusive in terms of the type of activity (overt/non-overt). Only the closed prompts---overt activity treatment supported the attainment of greater learning outcomes when compared to the other treatments. No significant difference in learning outcomes was found for the open prompts---overt activity, and the open prompts---non-overt activity. In relation to cognitive load, no significant difference was revealed between treatments. In relation to self-efficacy, no significant difference was revealed between treatments or between measures recorded pre-instruction and post-instruction. |
