When students are unsure of the correct answer to a multiple-choice question (MCQ), guessing is common practice. The availability heuristic, proposed by A. Tversky and D. Kahneman in 1973, suggests that the ease with which relevant instances come to mind, typically operationalised by the mere frequency of exposure, can offer a mental shortcut for problems in which the test-taker does not know the exact answer. Is simply choosing the option that comes most readily to mind a good strategy for answering MCQs? We propose a computational method of assessing the cognitive availability of MCQ options operationalised by concepts' prevalence in large corpora. The key finding, across three large question sets, is that correct answers, independently of the question stem, are significantly more available than incorrect MCQ options. Specifically, using Wikipedia as the retrieval corpus, we find that always selecting the most available option leads to scores 13.5% to 32.9% above the random-guess baseline. We further find that LLM-generated MCQ options show similar patterns of availability compared to expert-created options, despite the LLMs' frequentist nature and their training on large collections of textual data. Our findings suggest that availability should be considered in current and future work when computationally modelling student behaviour.

翻译：当学生对多项选择题（MCQ）的正确答案不确定时，猜测是常见做法。A. Tversky 和 D. Kahneman 于1973年提出的可得性启发式认为，相关实例在脑海中浮现的容易程度（通常通过单纯的接触频率来操作化）可以为应试者在不知道确切答案的问题上提供一种心理捷径。那么，仅仅选择脑海中最先浮现的选项是否是回答多项选择题的良好策略？我们提出了一种计算方法来评估多项选择题选项的认知可得性，该方法通过概念在大型语料库中的普遍性来操作化。在三个大型问题集中的关键发现是，无论题干如何，正确答案的可得性都显著高于错误选项。具体而言，使用维基百科作为检索语料库，我们发现总是选择最易得的选项可使得分比随机猜测基线高出13.5%至32.9%。我们进一步发现，尽管大型语言模型（LLM）具有频率主义特性且训练于大量文本数据，但其生成的多项选择题选项与专家创建的选项相比，呈现出类似的可得性模式。我们的研究结果表明，在当前及未来通过计算建模学生行为的工作中，应充分考虑可得性的作用。