Second language (L2) teachers may shy away from self-assessments because of warnings that students are not accurate self-assessors. This information stems from meta-analyses in which self-assessment scores on average did not correlate highly with proficiency test results. However, researchers mostly used Pearson correlations, when polyserial could be used. Furthermore, self-assessments today can be computer adaptive. With them, nonlinear statistics are needed to investigate their relationship with other measurements. We wondered, if we explored the relationship between self-assessment and proficiency test scores using more robust measurements (polyserial correlation, continuation-ratio modeling), would we find different results? We had 807 L2-Spanish learners take a computer-adaptive, L2-speaking self-assessment and the ACTFL Oral Proficiency Interview – computer (OPIc). The scores correlated at .61 (polyserial). Using continuation-ratio modeling, we found each unit of increase on the OPIc scale was associated with a 131% increase in the odds of passing the self-assessment thresholds. In other words, a student was more likely to move on to higher self-assessment subsections if they had a higher OPIc rating. We found computer-adaptive self-assessments appropriate for low-stakes L2-proficiency measurements, especially because they are cost-effective, make intuitive sense to learners, and promote learner agency.

第二语言 (L2) 教师可能会因为学生不是准确的自我评估者的警告而回避自我评估。该信息源于荟萃分析，其中自我评估分数平均与能力测试结果没有高度相关。然而，当可以使用多序列时，研究人员大多使用 Pearson 相关系数。此外，今天的自我评估可以是计算机自适应的。有了它们，就需要非线性统计来研究它们与其他测量的关系。我们想知道，如果我们使用更稳健的测量（多序列相关、连续比率建模）来探索自我评估和能力测试分数之间的关系，我们会发现不同的结果吗？我们有 807 名 L2-Spanish 学习者参加了计算机自适应，L2-speaking 自我评估和 ACTFL 口语能力面试 - 计算机 (OPIC)。得分相关性为 0.61（多序列）。使用连续比率模型，我们发现 OPIc 量表每增加一个单位，通过自我评估阈值的几率就会增加 131%。换句话说，如果学生的 OPIc 评分较高，他们更有可能进入更高的自我评估小节。我们发现计算机自适应自我评估适用于低风险的 L2 熟练程度测量，特别是因为它们具有成本效益，对学习者具有直觉意义，并促进学习者代理。我们发现 OPIc 量表每增加一个单位，通过自我评估阈值的几率就会增加 131%。换句话说，如果学生的 OPIc 评分较高，他们更有可能进入更高的自我评估小节。我们发现计算机自适应自我评估适用于低风险的 L2 熟练程度测量，特别是因为它们具有成本效益，对学习者具有直觉意义，并促进学习者代理。我们发现 OPIc 量表每增加一个单位，通过自我评估阈值的几率就会增加 131%。换句话说，如果学生的 OPIc 评分较高，他们更有可能进入更高的自我评估小节。我们发现计算机自适应自我评估适用于低风险的 L2 熟练程度测量，特别是因为它们具有成本效益，对学习者具有直觉意义，并促进学习者代理。