We show a method to auto-select reading passages in English assessment tests and share some key insights that can be helpful in related fields. In specifics, we prove that finding a similar passage (to a passage that already appeared in the test) can give a suitable passage for test development. In the process, we create a simple database-tagger-filter algorithm and perform a human evaluation. However, 1. the textual features, that we analyzed, lack coverage, and 2. we fail to find meaningful correlations between each feature and suitability score. Lastly, we describe the future developments to improve automated reading passage selection.
翻译:我们展示了一种在英语评估测试中自动选择读取段落的方法,并分享了在相关领域有用的一些关键洞察力。具体地说,我们证明找到一个相似的段落(在测试中已经出现的段落)可以为测试开发提供一个合适的通道。在此过程中,我们创建了一个简单的数据库定位器过滤算法,并进行人类评估。然而,1. 我们分析的文本特征,覆盖面不足,以及2. 我们没有找到每个特征和适合性分数之间有意义的关联。最后,我们描述了改进自动读取选择的未来动态。