Self-explanation prompts in example-based learning are usually directed backwards: Learners are required to self-explain problem-solving steps just presented ("retrospective" prompts). However, it might also help to self-explain upcoming steps ("anticipatory" prompts). The effects of the prompt type may differ for learners with various expertise levels, with anticipatory prompts being better for learners with more expertise. In an experiment, we employed extensive modelling examples and different types of self-explanations prompts to teach 78 automotive apprentices a complex and job-relevant problem-solving strategy, namely the diagnosis of car malfunctions. We tested the effects of these modelling examples and self-explanation prompts on problem-solving strategy knowledge and skill, self-efficacy, and cognitive load while learning. In two conditions, the apprentices learned with modelling examples and received either retrospective or anticipatory prompts. The third condition was a control condition receiving no modelling examples, but the respective open problems. In comparison with the control condition, modelling examples did not promote learning. However, we observed differential effects of the self-explanation prompts depending on the learner's prior knowledge level. Apprentices with higher prior knowledge learned more when learning with anticipatory prompts. Apprentices with less prior knowledge experienced a greater increase in self-efficacy and a higher germane cognitive load when learning with retrospective prompts. These findings suggest using different self-explanation prompts for learners possessing varying levels of expertise.