Self-generation of knowledge can activate deeper cognitive processing and improve long-term retention compared to the passive reception of information. It plays a distinctive role within the concept of inquiry-based learning, which is an activity-oriented, student-centered collaborative learning approach in which students become actively involved in knowledge construction by following an idealized hypothetico-deductive method. This approach allows students to not only acquire content knowledge, but also an understanding of investigative procedures/inquiry skills – in particular the control-of-variables strategy (CVS). From the perspective of cognitive load theory, generating answers and solutions during inquiry-based learning is inefficient as it imposes an intrinsic and extraneous load on learners. Previous research on self-generation of content knowledge in inquiry-based learning has demonstrated that (1) a high cognitive load impairs retention of the generated information, (2) feedback is a fundamental requirement for self-generation of complex content knowledge, (3) self-generation success is key to long-term retention, and (4) generating and rereading place different demands on learners. However, there is still no research on the self-generation of scientific reasoning skills (procedural knowledge) and no knowledge of interaction between the (long-term) retention of these skills with prior knowledge, feedback and self-generation success. That is why this experiment was conducted. The focus of this research is to analyze the distinctive role of self-generation of scientific reasoning skills within the concept of inquiry-based learning and to identify the influence of prior knowledge and self-generation success on short-term and long-term retention. For this purpose, an experiment involving 133 6th and 7th graders was conducted. An inquiry activity that included the self-generation of scientific reasoning skills was compared to an inquiry task that had students simply read information about the experimental design. We used both an immediate and a delayed test to examine which treatment better developed a deeper understanding of CVS and an ability to apply this knowledge to novel problems (transfer). Direct instruction was clearly superior to self-generation in facilitating students’ acquisition of CVS immediately after the inquiry task. However, after a period of 1 week had elapsed, both treatment conditions turned out to be equally effective. A generation effect was only found among students with high self-generation success after a 1-week delay.

Inquiry-based learning can be considered a critical component of science education in which students can assess their understanding of scientific concepts and scientific reasoning skills while actively constructing new knowledge through different types of activity levels (Klahr and Dunbar, 1988; Bell et al., 2005; Hmelo-Silver et al., 2007; Mayer, 2007). However, engaging in inquiry activities can be cognitively demanding for students, especially those with low prior knowledge of scientific reasoning skills (reasoning ability). Learning new information when preexisting schemata are absent entails more interacting elements and thus imposes a high working memory load, resulting in lower long-term learning effects (Paas and van Merriënboer, 1994; Kirschner et al., 2006). Borrowing knowledge from others via video modeling examples before carrying out an inquiry task provides learners with more working memory capacity to focus on problem-solving strategies and construct useful cognitive schemata for solving subsequent (virtual) inquiry tasks (Kant et al., 2017). The goal of the present study (N = 174 6/7th graders) is to investigate the benefits of combining example-based learning with physical, hands-on investigations in inquiry-based learning for acquiring scientific reasoning skills. The study followed a 2 (video modeling example vs. no example) × 2 (guided vs. structured inquiry) × 2 (retention interval: immediate vs. delayed) mixed-factorial design. In addition, the students' need for cognition (Preckel, 2014), cognitive abilities (Heller and Perleth, 2000) (intrinsic, extraneous, and germane) cognitive load (Cierniak et al., 2009) and performance success were measured. Although the results of an intermediate test after the first manipulation were higher among students who watched a video modeling example (d = 0.97), combining video modeling examples with inquiry was not found to benefit performance success. Furthermore, regardless of manipulation, all students achieved equal results on an assessment immediately following the inquiry task. Only in the long run did a video modeling example prove to be advantageous for guided inquiry (η p 2 = 0.023). A video modeling example turned out to be a crucial prerequisite for the long-term effectiveness of guided inquiry because it helped create stable problem-solving schemata; however, the long-term retention of structured inquiry did not rely on a video modeling example.