Abstract

This article demonstrates how recent advances in technology allow fine-grained analyses of candidate-produced essays, thus providing a deeper insight on writing performance. We examined how essay features, automatically extracted using natural language processing and keystroke logging techniques, can predict various performance measures using data from a large-scale and high-stakes assessment for awarding high-school equivalency diploma. The features that are the most predictive of writing proficiency and broader academic success were identified and interpreted. The suggested methodology promises to be practically useful because it has the potential to point to specific writing skills that are important for improving essay writing and academic performance for educationally at-risk adult populations like the one considered in this article.

摘要

本文展示了最新的技术进步如何允许对候选人撰写的论文进行细粒度分析，从而对写作表现提供更深入的了解。我们研究了使用自然语言处理和击键记录技术自动提取的论文特征如何使用大规模和高风险评估的数据来预测各种绩效指标，以授予高中同等文凭。确定并解释了最能预测写作能力和更广泛的学术成功的特征。建议的方法有望在实际中有用，因为它有可能指出特定的写作技巧，这些技巧对于提高论文写作和学业成绩很重要，如本文所考虑的教育风险成年人群。