Thiemo Wambsganss, Andreas Janson, Matthias Söllner, Ken Koedinger, J. Leimeister
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引用次数: 0
Abstract
This study explores the potential of dynamic, machine learning (ML)-based modeling to enhance students’ argumentation skills—a crucial component in education and professional success. Traditional educational tools often rely on static modeling, which does not adapt to individual learner needs or provide real-time feedback. In contrast, our research introduces an innovative ML-based system designed to offer dynamic, personalized feedback on argumentation skills. We conducted three empirical studies comparing this system against traditional methods such as scripted and adaptive support modeling. Our results show that dynamic behavioral modeling significantly improves learners’ objective argumentation skills across domains, outperforming all established methods. The results further indicate that, compared with adaptive support, the effect of the dynamic modeling approach holds across complex (large effect) and simple tasks (medium effect) and supports learners with lower and higher expertise alike. This research has important implications for educational policy and practice; incorporating such dynamic systems could transform learning environments by providing scalable, individualized support. This would not only foster essential skills but also cater to diverse learner profiles, potentially reducing educational disparities. Our work suggests a shift toward integrating more adaptive technologies in educational settings to better prepare students for the demands of the modern workforce.
本研究探讨了基于机器学习(ML)的动态建模在提高学生论证技能方面的潜力--论证技能是教育和职业成功的重要组成部分。传统的教育工具通常依赖于静态建模,无法适应学习者的个性化需求或提供实时反馈。相比之下,我们的研究引入了一种基于 ML 的创新系统,旨在为论证技能提供动态、个性化的反馈。我们进行了三项实证研究,将该系统与脚本化和自适应支持建模等传统方法进行了比较。我们的研究结果表明,动态行为建模显著提高了学习者在各个领域的客观论证技能,优于所有既有方法。结果进一步表明,与自适应支持相比,动态建模方法的效果在复杂任务(大效果)和简单任务(中效果)中都能保持,并能为专业技能较低和较高的学习者提供支持。这项研究对教育政策和实践具有重要意义;纳入这种动态系统可以提供可扩展的个性化支持,从而改变学习环境。这不仅能培养基本技能,还能满足不同学习者的需求,从而缩小教育差距。我们的工作表明,教育环境应向整合更多适应性技术的方向转变,使学生更好地适应现代劳动力的需求。
期刊介绍:
ISR (Information Systems Research) is a journal of INFORMS, the Institute for Operations Research and the Management Sciences. Information Systems Research is a leading international journal of theory, research, and intellectual development, focused on information systems in organizations, institutions, the economy, and society.