This project combines results from small group research and learning process research in order to enhance experimental methods as a crucial element of the science classroom.  
 
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The learner-generated drawing strategy requires learners to actively self-construct pictorial representations from text. Re-sults from the first and second project phases revealed several impact factors on the effectiveness of the learner-generated drawing strategy:  
(a) Quality of the learner-generated drawing  
(b) Type of instructional support  
(c) Presentation mode (paper- vs. computer-based)  
(d) Cognitive load  
 
 
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At the end of their primary school education, pupils still show a high interest for physics-related issues, as well as positive self concepts in this regard. However, a considerable decline in their interest in physics and affected self-concept are well established for secondary school pupils.    
In addition, evidence suggests that the physics-related teaching in primary- and secondary school differ with regard to their focus on comprehension and student autonomy.  
Up to now, it is unclear how such changes in the physics-related teaching are perceived by pupils and how these perceptions impact the development of motivational- and self-related variables.  
 
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Worked-out examples enable the learning of scientific concepts. Furthermore, they foster problem solving skills (Pirolli & Recker, 1995; Renkl, 2002; Mackensen-Friedrichs, 2004). The learning outcomes are mainly affected by the instructional design (Atkinson et al., 2000; Renkl, 2005) and the students’ self-explanations (Chi et al., 1989; Renkl, 1999; Lind, Friege, & Sandmann, 2005). Self-explanations differ in intensity and quality depending on the extent of prior content knowledge, i.e. students with high prior content knowledge (experts) vs. students with low prior content knowledge (novices) (Kroß & Lind, 2001; Lind, Friege & Sandmann, 2005).  
The goal of the project is the research of how biological worked-out examples with prior content knowledge adjusted and non-adjusted prompts influence the learning outcomes.  
The project is subdivided into two project parts. The first part deals with the impact of worked-out examples on individual learning outcomes. The second part focuses on their impact on dyad learning outcomes. Both project parts are based on the extent of prior content knowledge. Additionally, the communication will be investigated in order to clarify any possible correlation.  
In this study students will be subdivided into experts and novices on the basis of a pre-test. After learning from biological worked-out examples the learning outcomes will be measured by means of a post-test. To analyse the communication the learning sessions will be audio- and videotaped.  
Findings on the influence of prior content knowledge adjusted and non-adjusted prompts in biological worked-out examples in dependency of the prior content knowledge and social forms on the learning outcomes and the communication related to a specific field and issue are expected. In addition, findings on the quality of the communication related to a specific field and issue and its influence on the learning outcomes shall be existent at the end of the project. The study will yield recommendations on the design of prompts in learning exercises and field-tested worked-out examples for the individual advancement.  

Self-regulation of learning occurs on a metacognitive as well as on a motivational level (cf. Boekaerts, 1997; Zimmerman, 2000). The role of metacognition and motivation during learning has already been comprehensively investigated (i.a. Thillmann, 2008). In contrast, research on the regulation of motivation is still restricted to its assessment and the support of motivational regulation received almost no research attention so far (cf. Wolters, 1998, 2003). Thus, the purpose of this project is to experimentally investigate the effect of motivational regulation prompts in comparison to metacognitive regulation prompts in order to support the self-regulation of learning in a computer-based learning environment on experimentation. Metacognitive regulation prompts are expected to support the use of experimentation strategies (Thillmann, 2008), while motivational regulation prompts are expected to support the use of adequate motivational strategies. Dependent variables are knowledge about and use of cognitive experimentation strategies, as well as motivational strategies and acquired knowledge about the content of the learning environment. From a theoretical point of view, new insights into the role of motivational regulation in the learning process are expected. From a practical point of view, we expect a contribution to the evaluation of alternative approaches for the support of self-regulated learning in learning by experimenting.

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