LaVoie Noelle, Parker James, Legree Peter J, Ardison Sharon, Kilcullen Robert N
Parallel Consulting, Petaluma, CA, USA.
U.S. Army Research Institute for the Behavioral and Social Sciences, Fort Belvoir, VA, USA.
Educ Psychol Meas. 2020 Apr;80(2):399-414. doi: 10.1177/0013164419860575. Epub 2019 Jul 9.
Automated scoring based on Latent Semantic Analysis (LSA) has been successfully used to score essays and constrained short answer responses. Scoring tests that capture open-ended, short answer responses poses some challenges for machine learning approaches. We used LSA techniques to score short answer responses to the Consequences Test, a measure of creativity and divergent thinking that encourages a wide range of potential responses. Analyses demonstrated that the LSA scores were highly correlated with conventional Consequence Test scores, reaching a correlation of .94 with human raters and were moderately correlated with performance criteria. This approach to scoring short answer constructed responses solves many practical problems including the time for humans to rate open-ended responses and the difficulty in achieving reliable scoring.
基于潜在语义分析(LSA)的自动评分已成功用于短文和受限简答题的评分。对开放式简答题进行评分的测试给机器学习方法带来了一些挑战。我们使用LSA技术对“后果测试”的简答题答案进行评分,“后果测试”是一种衡量创造力和发散性思维的测试,鼓励各种可能的回答。分析表明,LSA评分与传统的“后果测试”分数高度相关,与人工评分者的相关性达到0.94,与表现标准呈中等程度相关。这种对简答题构建答案进行评分的方法解决了许多实际问题,包括人工评分开放式答案所需的时间以及获得可靠评分的难度。