Constructing arguments is essential in science subjects like chemistry. For example, students in organic chemistry should learn to argue about the plausibility of competing chemical reactions by including various sources of evidence and justifying the derived information with reasoning. While doing so, students face significant challenges in coherently structuring their arguments and integrating chemical concepts. For this reason, a reliable assessment of students' argumentation is critical. However, as arguments are usually presented in open-ended tasks, scoring assessments manually is resource-consuming and conceptually difficult. To augment human diagnostic capabilities, artificial intelligence techniques such as machine learning or natural language processing offer novel possibilities for an in-depth analysis of students' argumentation. In this study, we extensively evaluated students' written arguments about the plausibility of competing chemical reactions based on a methodological approach called computational grounded theory. By using an unsupervised clustering technique, we sought to evaluate students' argumentation patterns in detail, providing new insights into the modes of reasoning and levels of granularity applied in students' written accounts. Based on this analysis, we developed a holistic 20-category rubric by combining the data-driven clusters with a theory-driven framework to automate the analysis of the identified argumentation patterns. Pre-trained large language models in conjunction with deep neural networks provided almost perfect machine-human score agreement and well-interpretable results, which underpins the potential of the applied state-of-the-art deep learning techniques in analyzing students' argument complexity. The findings demonstrate an approach to combining human and computer-based analysis in uncovering written argumentation.

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探索新深度：应用机器学习分析学生化学论证

在化学等科学学科中，构建论据至关重要。例如，有机化学专业的学生应该学会通过提供各种证据来源并用推理来证明派生信息的合理性，从而争论竞争化学反应的合理性。在此过程中，学生在连贯地构建论点和整合化学概念方面面临着重大挑战。因此，对学生的论证进行可靠的评估至关重要。然而，由于论点通常在开放式任务中提出，因此手动对评估进行评分既耗费资源，而且在概念上也很困难。为了增强人类的诊断能力，机器学习或自然语言处理等人工智能技术为深入分析学生的论证提供了新的可能性。计算扎根理论。通过使用无监督聚类技术，我们试图详细评估学生的论证模式，为学生书面记录中应用的推理模式和粒度级别提供新的见解。基于此分析，我们通过将数据驱动的集群与理论驱动的框架相结合，开发了一个整体的 20 类评价标准，以自动分析已识别的论证模式。预训练的大型语言模型与深度神经网络相结合提供了近乎完美的效果机器与人类的分数一致性和易于解释的结果，支撑了应用最先进的深度学习技术在分析学生论证复杂性方面的潜力。研究结果展示了一种将人类和计算机分析相结合来揭示书面论证的方法。