Computer science students are expected to be able to look at a problem and select an appropriate algorithm design paradigm to use to produce a solution. However, there is little research on how students determine which algorithmic paradigm to use. Historically, researchers have relied on free-response questions or interviews to assess students' knowledge of algorithmic paradigm selection. To successfully evaluate and scale teaching interventions for selecting an algorithmic design paradigm, we need to efficiently test a student's ability to select among different design paradigms. Here, we present the first attempts to assess student knowledge to select an algorithm design paradigm using multiple-choice questions. We present the construction of the \textit{algorithmic paradigm selection assessment} (APSA) and preliminary data demonstrating its effectiveness as an assessment. We discuss the key points we learned during this process to write multiple-choice questions for Algorithm Design Paradigms. We tested the internal consistency of our assessment using Cronbach's $α$ and obtained a score of $0.73$, which is above the required threshold of $0.7$. APSA can be used across institutions as a standardized way to assess students' ability to select different algorithm design paradigms. APSA will assist researchers in evaluating whether a theory helps students improve their knowledge of different Algorithm Design Paradigms.

翻译：计算机科学专业的学生应具备分析问题并选择合适的算法设计范式以生成解决方案的能力。然而，关于学生如何确定使用哪种算法范式的研究尚不充分。历史上，研究者主要依赖自由回答题或访谈来评估学生对算法范式选择的知识掌握程度。为有效评估并推广针对算法设计范式选择的教学干预措施，我们需要高效测试学生区分不同设计范式的能力。本文首次尝试通过多项选择题评估学生选择算法设计范式的知识水平。我们介绍了《算法范式选择评估》（APSA）的构建过程，并展示了证明其有效性的初步数据。同时，阐述了在编写算法设计范式相关选择题过程中总结的关键经验。采用Cronbach's α检验评估的内部一致性，得分为0.73，高于0.7的阈值要求。APSA可作为跨机构标准化评估工具，用于衡量学生选择不同算法设计范式的能力，从而帮助研究者检验相关理论是否有助于提升学生对各类算法设计范式的认知水平。