Scripting computer-supported collaborative learning has been shown to greatly enhance learning, but is often criticized for hindering learners' agency and thus undermining learners' motivation. Beyond that, what makes some CSCL scripts particularly effective for learning is still a conundrum. This meta-analysis synthesizes the results of 53 primary studies that experimentally compared the effect of learning with a CSCL script to unguided collaborative learning on at least one of the variables motivation, domain learning, and collaboration skills. Overall, 5616 learners enrolled in K-12, higher education, or professional development participated in the included studies. The results of a random-effects meta-analysis show that learning with CSCL scripts leads to a nonsignificant positive effect on motivation (Hedges' g = 0.13), a small positive effect (Hedges' g = 0.24) on domain learning and a medium positive effect (Hedges' g = 0.72) on collaboration skills. Additionally, the meta-analysis shows how scaffolding single particular collaborative activities and scaffolding a combination of collaborative activities affects the effectiveness of CSCL scripts and that synergistic or differentiated scaffolding is hard to achieve. This meta-analysis offers the first counterevidence against the widespread criticism that CSCL scripts have negative motivational effects. Furthermore, the findings can be taken as evidence for the robustness of the positive effects on domain learning and collaboration skills.

Purpose To advance the learning of professional practices in teacher education and medical education, this conceptual paper aims to introduce the idea of representational scaffolding for digital simulations in higher education. Design/methodology/approach This study outlines the ideas of core practices in two important fields of higher education, namely, teacher and medical education. To facilitate future professionals’ learning of relevant practices, using digital simulations for the approximation of practice offers multiple options for selecting and adjusting representations of practice situations. Adjusting the demands of the learning task in simulations by selecting and modifying representations of practice to match relevant learner characteristics can be characterized as representational scaffolding. Building on research on problem-solving and scientific reasoning, this article identifies leverage points for employing representational scaffolding. Findings The four suggested sets of representational scaffolds that target relevant features of practice situations in simulations are: informational complexity, typicality, required agency and situation dynamics. Representational scaffolds might be implemented in a strategy for approximating practice that involves the media design, sequencing and adaptation of representational scaffolding. Originality/value The outlined conceptualization of representational scaffolding can systematize the design and adaptation of digital simulations in higher education and might contribute to the advancement of future professionals’ learning to further engage in professional practices. This conceptual paper offers a necessary foundation and terminology for approaching related future research.

In their daily practice, physicians with different professional backgrounds often diagnose a patient’s problem collaboratively. In those situations, physicians not only need to be able to diagnose individually, but also need additional collaborative competences such as information sharing and negotiation skills (Liu et al., 2015), which can influence the quality of the diagnostic outcome (Tschan et al., 2009). We introduce the CDR model, a process model for collaborative diagnostic reasoning processes by diagnosticians with different knowledge backgrounds. Building on this model, we develop a simulation in order to assess and facilitate collaborative diagnostic competences among advanced medical students. In the document-based simulation, learners sequentially diagnose five patients by inspecting a health record for symptoms. Then, learners request a radiological diagnostic procedure from a simulated radiologist. By interacting with the simulated radiologist, the learners elicit more evidence for their hypotheses. Finally, learners are asked to integrate all information and suggest a final diagnosis.

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