Personalizing simulation-based learning in higher education

IF 3.8 1区心理学 Q1 PSYCHOLOGY, EDUCATIONAL

Learning and Individual Differences Pub Date : 2025-06-30 DOI:10.1016/j.lindif.2025.102746

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 , Frank Fischer

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Abstract

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 the simulation environment is outlined in a conceptual framework which may guide systematic research on personalized learning support in digital simulations.

Educational relevance statement

This paper introduces a conceptual framework, which aims to advance personalized simulation-based learning in higher education. Digital simulations can provide tailored learning experiences that adapt to students' individual differences and needs, using artificial intelligence and other technological advances. This approach might have the potential to transform learning in higher education by increasing student engagement and the effectiveness of learning professional knowledge and skills. The framework is discussed along five central questions of personalized learning, which may guide systematic research on how simulations can accommodate learners' diverse prerequisites and processes. In doing so, the framework provides a starting point for interdisciplinary research collaborations aimed at developing design principles for personalized simulation-based learning in higher

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高等教育中个性化模拟学习

随着数字化的发展和技术的迅速进步，数字模拟为医学或教师高等教育等背景下的专业实践学习提供了巨大的潜力。技术进步日益促进学习支持的个性化，以满足学习者的个性化需求，学习者的不同先决条件影响他们的学习过程、活动和结果。然而，将技术与教育理论和证据相结合的系统方法却很少。在本文中，我们建议使用相关学习先决条件和学习过程的数据作为个性化反馈和脚手架的基础，以促进模拟实践表征的学习。我们将理论概念与方法和技术方法（例如，使用人工智能）联系起来，为重要的学习者变量建模，作为个性化学习支持的基础。学习者和模拟环境之间的相互作用在一个概念框架中被概述，这个概念框架可以指导数字模拟中个性化学习支持的系统研究。教育相关性陈述本文介绍了一个概念框架，旨在促进高等教育中基于个性化模拟的学习。数字模拟可以利用人工智能和其他先进技术，提供量身定制的学习体验，以适应学生的个体差异和需求。这种方法有可能通过提高学生的参与度和学习专业知识和技能的有效性来改变高等教育的学习方式。该框架围绕个性化学习的五个核心问题进行了讨论，这可能指导系统研究模拟如何适应学习者不同的先决条件和过程。在此过程中，该框架为跨学科研究合作提供了一个起点，旨在为高等教育中基于个性化模拟的学习开发设计原则

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来源期刊

Learning and Individual Differences PSYCHOLOGY, EDUCATIONAL-

CiteScore

6.60

自引率

2.80%

发文量

期刊介绍： Learning and Individual Differences is a research journal devoted to publishing articles of individual differences as they relate to learning within an educational context. The Journal focuses on original empirical studies of high theoretical and methodological rigor that that make a substantial scientific contribution. Learning and Individual Differences publishes original research. Manuscripts should be no longer than 7500 words of primary text (not including tables, figures, references).