Facilitating diagnostic competences with simulations

Simulations have found their way into higher education as “approximations of practice” (Grossman et al., 2009) for facilitating professional skills development across multiple domains like teacher or medical education. The strengths of simulations are (a) that they allow students to practice authentic problems specifically designed to enable learning (e.g., by integrating scaffolding in the simulation design) and (b) that they facilitate systematically investigating the learning process and possibilities for its assessment. Although different research areas often address cross-domain questions – for example, about the effects of training measures (Chernikova et al., 2020), essential simulation features such as authenticity (e.g., Codreanu et al., 2020), and students' individual differences in using simulations (e.g., Theelen et al., 2019) – they are currently investigating determinants of effective learning environments primarily within their field but seldom interconnected.

Our Special Section aims to create synergies between the research traditions by discussing knowledge transfer possibilities across domains. Therefore, this Special Section is a collection of theoretical and empirical articles from medical and teacher education stemming from interdisciplinary research groups and examining the determinants of simulation effectiveness. We focus on diagnostic competencies, which comprise collecting, integrating, and correctly assessing information to make professional decisions (e.g., Heitzmann et al., 2019). Methodologically, we begin with theoretical considerations, followed by a meta-analysis that classifies studies based on these considerations and identifies research desiderata. Finally, we present three experimental studies that address these desiderata.

The first article of this Special Section introduces a guiding theoretical model of designing diagnostic situations and essential features of this design while also attempting to classify existing studies into this model (Machts et al., 2024). Furthermore, the study emphasizes the role of salience (i.e., the notability) of relevant information within simulations (e.g., content-related information or feedback about performance) to explain the effectiveness of simulations. This systematic conceptualization aims to better understand how and why simulations work but also creates a common language for researchers from different traditions to facilitate knowledge transfer in creating authentic diagnostic situations, measuring diagnostic competencies, and providing learning environments for facilitating these competencies in higher education.

Bridging the gap from theory to empirical studies, the role of salience and some other features in building up authentic simulation experiences was also explored through a meta-analysis in the second article of this Special Section (Chernikova et al., 2024). This meta-analysis emphasizes the role of authenticity and salience as simulation features, as well as learners' prior knowledge as a learning prerequisite in facilitating a range of complex skills (including diagnosing) in higher education. The model with the features mentioned above can explain around 8.5% of the variance in the effectiveness of simulations and provide insights for designing effective simulations. Authenticity and salience can be assessed independently, and both can be considered essential design features in creating effective simulation-based learning environments. Higher salience, particularly when increased through instructional support, and higher levels of authenticity contribute to higher effects associated with simulation-based learning environments implementing those design features (Chernikova et al., 2024).

In the last three articles of this Special Section, the theoretical considerations building on ideas in teacher and medical education are incorporated into primary studies, which explore the role of feedback and scaffolding in facilitating diagnostic competencies with simulation-based learning. The first examined the effects of adaptive feedback using artificial intelligence to increase the salience of relevant information as a means to foster relations between different facets in diagnostic argumentation (Bauer et al., 2024). The second investigated the effects of conceptual knowledge prompts and a motivational intervention on acquiring diagnostic competencies using a simulation for pre-service mathematics teachers (Nickl et al., 2024). The third explored if expert feedback could facilitate teachers' assessment accuracy and motivation in assessing student texts (Jansen et al., 2024).

Overall, short interventions incorporating static expert feedback (e.g., a comparison between the learners' assessments and the correct assessments for the simulated assessment task) can facilitate interest during long periods of assessment tasks but showed no effect on assessment accuracy (Jansen et al., 2024). However, adaptive feedback fostered the quality of pre-service teachers' diagnostic argumentation (Bauer et al., 2024). Therefore, facilitating diagnostic argumentation in teacher education programs using case-based simulations with adaptive feedback is a promising start to facilitate the professionalization of future teachers, especially if feedback increases the salience of information needed to make accurate professional decisions. Furthermore, Nickl et al. (2024) emphasizes the differential effects when different aspects of the simulated scenarios are being prompted and are therefore made more salient for the learners. Their paper compares the effects of a utility value intervention and conceptual knowledge prompts. The results indicated that conceptual prompts (i.e., enhancing salience of relevant information) provide support for promoting pre-service teachers' judgment accuracy, whereas the utility value intervention yielded only descriptive improvements that need to be substantiated in future research, also using longer-term interventions.

Summarizing the lessons learned from the studies, we see that cross-domain theoretical models are useful to develop training measures for simulation-based learning environments. We suggest for further studies to better link the research on simulation-based learning in different domains of higher education. In doing so, we aim to facilitate the development and use of a common language across different domains, enable comparisons between studies in different research areas, and contribute to the transfer of good practices.