Conceptual and simulation models can function as useful pedagogical tools, however it is important to categorize different outcomes when evaluating them in order to more meaningfully interpret results. VERA is a ecology-based conceptual modeling software that enables users to simulate interactions between biotics and abiotics in an ecosystem, allowing users to form and then verify hypothesis through observing a time series of the species populations. In this paper, we classify this time series into common patterns found in the domain of ecological modeling through two methods, hierarchical clustering and curve fitting, illustrating a general methodology for showing content validity when combining different pedagogical tools. When applied to a diverse sample of 263 models containing 971 time series collected from three different VERA user categories: a Georgia Tech (GATECH), North Georgia Technical College (NGTC), and ``Self Directed Learners'', results showed agreement between both classification methods on 89.38\% of the sample curves in the test set. This serves as a good indication that our methodology for determining content validity was successful.

本研究通过层次聚类和曲线拟合的方法，对263个模型中的971个时间序列进行了分类，以展示在生态建模领域中显示内容有效性的通用方法。结果显示，这一方法在测试集中89.38％的样本曲线上两种分类方法达成了一致，表明我们的内容有效性确定方法是成功的。

利用学生创作数据进行教育工具内容验证的分析