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Starting from difficulties that students of education display when they interpret empirical findings and generate scientific arguments, a problem-based e-learning environment was developed. Based on first evaluation data, an elaboration tool was integrated into the learning environment. The tool consisted of a modelling and an explanation part. In order to investigate the effectiveness of this elaboration support, a quasi-experimental field study was carried out in two seminars on scientific methods for advanced students of education. Students in seminar 1 (n 032) worked within the learning environment without the elaboration tool, students in seminar 2 (n 020) worked with a version that contained the elaboration support. The seminars did not differ with respect to various student characteristics. The students' absolute performance on a posttest that included argumentation and statistics tasks was rather poor. However, the elaboration tool significantly enhanced performance and motivational aspects (acceptance of the learning environment and subjective learning outcomes). By cluster analytical methods three student profiles were identified that substantially differed concerning posttest performance.

To examine the effects of reflection prompts, elaborated feedback and cooperation on learning and reflection, two experimental studies were conducted. For both studies, an example-and problem-based e-learning environment on correlation was used. In Study 1, 57 university students were randomly assigned to two conditions: with reflection prompts that asked students to give reasons for their decisions and without reflection prompts. The intervention promoted learning, and the students' reasons indicated substantial reflective processes. In Study 2, 137 university students were randomly assigned to four conditions: individual learning with or without feedback intervention and dyadic learning with or without feedback intervention. The feedback intervention clearly enhanced learning outcomes, whereas cooperative learning had no significant effect on learning. Perceived reflection was high in all groups, differences were nonsignificant.

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The aim of the present study was to introduce a general theoretical model of scientific competencies in higher education and to adapt it to three social sciences, namely psychology, sociology, and political science, by providing evidence from expert interviews and program regulations. Within our general model, we distinguished and specified four building blocks of scientific competencies: input, operations, and output, as well as personal characteristics. We defined input as content students are exposed to in their respective domains, operations as cognitive processes stated by Anderson et al. (2001), and output as content students create as a result of applying operations on input. We considered scientific competencies to be the constructive use of operations on input and the creation of output thereby.