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Personalization through adaptivity or adaptability? A meta-analysis on simulation-based learning in higher education
Olga Chernikova (Ludwig-Maximilians-Universität München), Daniel Sommerhoff (Leibniz Institute for Science and Mathematics Education), Matthias J. Stadler (Institut für Didaktik und Ausbildungsforschung in der Medizin, Ludwig-Maximilians-Universität München), Doris Holzberger (Technical University of Munich), Michael Nickl (Leibniz Institute for Science and Mathematics Education, Technical University of Munich), Tina Seidel (Technical University of Munich), Enkelejda Kasneci (Technical University of Munich), Stefan Küchemann (Ludwig-Maximilians-Universität München), Jochen Kühn (Ludwig-Maximilians-Universität München), Frank Fischer (Ludwig-Maximilians-Universität München), Nicole Heitzmann (Ludwig-Maximilians-Universität München)
Résumé : This meta-analysis builds on 217 empirical studies in higher education and investigates the role of the different forms of adaptivity and adaptability as personalization strategies in simulation-based learning environments for complex skills in higher education. The strategies used to personalize scaffolding and task progression were the central point in this meta-analysis. We identified conditions under which personalization advances complex skills in higher education. The results indicate that whereas adaptivity (i.e., computer makes decisions) is more effective for scaffolding, adaptability (the decisions made by individual learners) seem more beneficial for task progression. We conclude that adaptivity and adaptability can be effectively used to personalize simulation-based learning environments in higher education to better address needs of learners with different learning needs. We also discuss the potential of artificial intelligence for empowering personalization in simulation-based learning. • Personalization can be highly effective in simulation-based learning environments in higher education. • Adaptivity and adaptability in simulations allow addressing needs of learners with different levels of prior knowledge. • Artificial intelligence can empower adaptivity and adaptability in simulations.