Combining retrieval practice and generative learning in educational contexts

Engaging learners in practicing the retrieval of learned information fosters the consolidation of learners’ mental representations and hence long-term retention. Retrieval practice research has enriched the instructional design literature by providing a wealth of evidence for these benefits of retrieval-based learning and thus emphasizing the value of means to consolidate knowledge. The present article makes the case that a fruitful next step could be to focus on the interplay between retrieval practice and generative activities. Rather than consolidating mental representations, generative activities should have as their main function the construction of coherent mental representations. Hence, from a theoretical perspective, generative activities and retrieval practice should functionally complement each other; hence, combinations of both activities might be particularly suitable to promote lasting learning. Given the challenge to beneficially combine these activities, we discuss open questions that could substantially advance both the retrieval practice and the generative learning field.Das Üben des Abrufs von zuvor gelernten Informationen aus dem Gedächtnis fördert die Konsolidierung mentaler Repräsentationen und damit das langfristige Behalten. Durch eine Fülle an Belegen für diese Vorteile des abrufbasierten Lernens und die Betonung des Wertes von Maßnahmen zur Konsolidierung von Wissen, hat die Forschung zu Abrufübungen die Instruktionsdesignforschung deutlich bereichert. In dem vorliegenden Artikel argumentieren wir, dass ein sinnvoller nächster Schritt darin bestehen könnte, das Zusammenspiel zwischen Abrufübungen und generativen Lernaktivitäten zu untersuchen. Anstatt mentale Repräsentationen zu konsolidieren, besteht die Hauptfunktion von generativen Lernaktivitäten im Aufbau kohärenter mentaler Repräsentationen. Aus theoretischer Sicht sollten sich also generative Aktivitäten und Abrufübungen funktional ergänzen, so dass Kombinationen beider Arten von Aktivitäten besonders geeignet sein könnten, um nachhaltiges Lernen zu fördern. Angesichts der Herausforderung, diese Aktivitäten sinnvoll zu kombinieren, diskutieren wir offene Fragen, die sowohl die Forschung zu Abrufübungen als auch das Feld des generativen Lernens wesentlich voranbringen könnten

Representational scaffolding in digital simulations – learning professional practices in higher education

To advance the learning of professional practices in teacher education and medical education, this conceptual paper introduces the idea of representational scaffolding for digital simulations in higher education. We outline 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, we emphasize the role of approximation of practice through the use of digital simulations, as they offer multiple options for selecting and adjusting representations of practice situations. Furthermore, we introduce the idea of representational scaffolding to adjust the demands of the learning task in simulations by selecting and modifying representations of practice to match relevant learner characteristics. Building on research on problem-solving and scientific reasoning, we identify leverage points for employing representational scaffolding. We suggest four sets of representational scaffolds that target relevant features of practice situations in simulations: 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. 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. https://doi.org/10.1108/ILS-06-2022-007