Elisabeth Bauer, Nicole Heitzmann, Maria Bannert, Olga Chernikova, Martin R. Fischer, Anne C. Frenzel, Martin Gartmeier, Sarah I. Hofer, Doris Holzberger, Enkelejda Kasneci, Jenna Koenen, Christian Kosel, Stefan Küchemann, Jochen Kuhn, Tilman Michaeli, Birgit J. Neuhaus, Frank Niklas, Andreas Obersteiner, Jürgen Pfeffer, Michael Sailer, Ralf Schmidmaier, Bernhard Schmidt-Hertha, Matthias Stadler, Stefan Ufer, Andreas Vorholzer, Tina Seidel, Frank Fischer
- As digitalization progresses and technologies advance rapidly, digital simulations offer great potential for learning professional practices in contexts such as medical or teacher higher education. The technological advancements increasingly facilitate the personalization of learning support to meet the individual needs of learners, whose diverse prerequisites influence their learning processes, activities, and outcomes. However, systematic approaches to combining technologies with educational theories and evidence are scarce. In this article, we propose to use data on relevant learning prerequisites and learning processes as a basis for personalizing feedback and scaffolding to facilitate learning with simulated practice representations. We connect theoretical concepts with methodological and technical approaches (e.g., using artificial intelligence) for modeling important learner variables as a basis for personalized learning support. The interplay between the learner and theAs digitalization progresses and technologies advance rapidly, digital simulations offer great potential for learning professional practices in contexts such as medical or teacher higher education. The technological advancements increasingly facilitate the personalization of learning support to meet the individual needs of learners, whose diverse prerequisites influence their learning processes, activities, and outcomes. However, systematic approaches to combining technologies with educational theories and evidence are scarce. In this article, we propose to use data on relevant learning prerequisites and learning processes as a basis for personalizing feedback and scaffolding to facilitate learning with simulated practice representations. We connect theoretical concepts with methodological and technical approaches (e.g., using artificial intelligence) for modeling important learner variables as a basis for personalized learning support. The interplay between the learner and the simulation environment is outlined in a conceptual framework which may guide systematic research on personalized learning support in digital simulations.…
