Classroom-based Experiments in Productive Failure

Classroom-based Experiments in Productive Failure Manu Kapur & Katerine Bielaczyc National Institute of Education, Singapore Abstract We present evidence from three quasi-experimental studies on productive failure. In Experiment 1, students experienced either direct instruction (DI) or productive failure (PF), wherein they were first asked to generate a quantitative index for variance before receiving direct instruction on the concept. Experiment 2 examined if it was necessary for students to generate solutions or can these solutions be simply given to the students to study and evaluate. Experiment 3 examined if it was necessary for students to generate solutions before receiving the critical features of the targeted concept, or would simply telling the critical features without any such generation work just as well. In Experiment 1, PF students performed on par with DI students on procedural fluency, and significantly outperformed them on data analysis and conceptual insight items. In Experiment 2, only the effects on conceptual insight and near transfer were significant. In Experiment 3, only the effect on conceptual insight remained significant. Overall, these results challenge the claim that that direct instruction alone is the most effective approach for teaching novel concepts to learners. Introduction Proponents of direct instruction bring to bear substantive empirical evidence against un-guided or minimally-guided instruction to claim that there is little efficacy in having learners solve problems that target novel concepts, and that learners should receive direct instruction on the concepts before any problem solving (Kirschner, Sweller, & Clark, 2006). Kirschner et al. (2006) argued that “Controlled experiments almost uniformly indicate that when dealing with novel information, learners should be explicitly shown what to do and how to do it” (p. 79). Based on cognitive load theory, commonly-cited problems with un-guided or minimally-guided instruction include increased working memory load that interferes with schema formation (Tuovinen & Sweller, 1999; Sweller, 1988), encoding of errors and misconceptions (Brown & Campione, 1994), lack of adequate practice and elaboration (Klahr & Nigam, 2004), as well as affective problems of frustration and de- motivation (Hardiman et al., 1986). Klahr & Nigam’s (2004) often-cited study compared the relative effectiveness of discovery learning and direct instruction approaches on learning the control of variable strategy (CVS) in scientific experimentation. On the acquisition of basic CVS skill as well as ability to transfer the skill to evaluate the design of science experiments, their findings suggested that students in the direct instruction condition who were explicitly taught how to design un- confounded experiments outperformed their counterparts in the discovery learning condition who were simply left alone to design experiments without any instructional structure or feedback from the instructor. Further experiments by Klahr and colleagues (e.g., Strand-Cary & Klahr, 2008), and others as well have largely bolstered the ineffectiveness of discovery learning compared with direct instruction (for reviews, see Kirschner et al., 2006). However, we question whether there is little efficacy in having learners solve problems that target concepts they have not learnt yet. To determine if there such an efficacy, a stricter comparison for direct instruction would be to compare it with an approach where students first generate representations and methods on their own followed by direct instruction. As it can be expected, the generation process will invariably lead to failure, that is, students are rarely able to solve the problems and discover the canonical solutions by themselves. Yet, this very process can be productive for learning provided direct instruction on the targeted concepts is subsequently provided (Schwartz & Martin, 2004). As a case in point, we present evidence from our research program on productive failure (Kapur, 2008). Designing for Productive Failure Productive failure focuses on engaging students in processes that serve two critical cognitive functions, which in turn, prepare students for subsequent direct instruction: a) activating and differentiating prior knowledge in relation to the targeted concepts, and b) affording attention to critical features of the targeted concepts. PF comprises two phases—a generation and exploration phase followed by a direct instruction phase. In the generation and exploration phase, the focus is on affording students the opportunity to leverage their formal as well as intuitive prior knowledge and resources to generate a diversity of solutions for a complex problem; a problem that targets concepts that they have not yet learnt. Research suggests that students do have rich constructive resources (diSessa & Sherin, 2000) to generate a variety of solutions for novel problems. At the same time, research also suggests that one cannot expect students, who are novices to the target content, to somehow generate or discover the canonical representations and domain-specific methods for solving the problem (Kirschner et al., 2006). However, the expectation for the generation and exploration phase is not for students to be able to solve the problem successfully. Instead, it is to generate and explore the affordances and constraints of a diversity of solutions for solving the problem. Our hypothesis is that this process both activates and differentiates prior knowledge (as evidenced in the diversity of student-generated solutions). Furthermore, a comparison and contrast between the various solutions affords opportunities to attend to critical features of the targeted concept. Consequently, the generation and exploration phase provides the necessary foundation for developing deeper understanding of the canonical concept during direct instruction (Kapur, 2009, 2010a/b; Schwartz & Martin, 2004).