technique for promoting deep learning (de Jong, 2005; Tobias and Duffy, 2009). For example, there is no compelling evidence that beginners deeply learn science concepts or processes by freely exploring a science simulation or game (National Research Council, 2011b), but including guidance in the form of advice, feedback, prompts, and scaffolding (i.e., completing part of the task for the learner) can promote deeper learning in beginners (de Jong, 2005; Azevedo and Aleven, 2010).

Providing guided exploration and metacognitive support also enhances learning for transfer in informal settings. Based on its review of the research on informal science learning, a National Research Council committee (2009a) recommended that science exhibits and programs be designed with specific learning goals in mind and that they provide support to sustain learners’ engagement and learning. For example, exhibits and programs should “prompt and support participants to interpret their learning experiences in light of relevant prior knowledge, experiences, and interests” (p. 307). There is emerging evidence that designing simulations to enable guided exploration, with support, enhances deeper learning of science (National Research Council, 2011b).

Teaching with Examples and Cases

A worked-out example is a step-by-step modeling and explanation of how to carry out a procedure, such as how to solve probability problems (Renkl, 2005, 2011). Under appropriate conditions, students gain deep understanding when they receive worked-out examples as they begin to learn a new procedural skill, both in paper-based and computer-based venues (Sweller and Cooper, 1985; Renkl, 2005, 2011). In particular, deep learning is facilitated when the problem is broken into conceptually meaningful steps which are clearly explained and when the explanations are gradually taken away with increasing practice (Renkl, 2005, 2011).

Priming Student Motivation

Deep learning occurs when students are motivated to exert the effort to learn, so another way to promote deep learning is to prime student motivation (Schunk, Pintrich, and Meece, 2008; Summers, 2008; Wentzel and Wigfield, 2009). Research on academic motivation shows that students learn more deeply when they attribute their performance to effort rather than to ability (Graham and Williams, 2009), when they have the goal of mastering the material rather than the goal of performing well or not performing poorly (Anderman and Wolters, 2006; Maehr and Zusho, 2009), when they expect to succeed on a learning task and value the learning task (Wigfield, Tonks, and Klauda, 2009), when they have the belief that they

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