Abstract Prediction models that underlie “early warning systems” need improvement. Some predict outcomes using entrenched, unchangeable characteristics (e.g., socioeconomic status) and others rely on performance on early assignments to predict the final grades to which they contribute. Behavioral predictors of learning outcomes often accrue slowly, to the point that time needed to produce accurate predictions leaves little time for intervention. We aimed to improve on these methods by testing whether we could predict performance in a large lecture course using only students’ digital behaviors in weeks prior to the first exam. Early prediction based only on malleable behaviors provides time and opportunity to advise students on ways to alter study and improve performance. Thereafter, we took the not-yet-common step of applying this model and testing whether providing digital learning support to those predicted to perform poorly can improve their achievement. Using learning management system log data, we tested models composed of theory-aligned behaviors using multiple algorithms and obtained a model that accurately predicted poor grades. Our algorithm correctly identified 75% of students who failed to earn the grade of B or better needed to advance to the next course. We applied this model the next semester to predict achievement levels and provided a digital learning strategy intervention to students predicted to perform poorly. Those who accessed advice outperformed classmates on subsequent exams, and more students who accessed the advice achieved the B needed to move forward in their major than those who did not access advice.

中文翻译：

---

在 STEM 专业开始失败之前预测成就并提供支持

摘要 作为“预警系统”基础的预测模型需要改进。有些人使用根深蒂固的、不可改变的特征（例如，社会经济地位）来预测结果，而其他人则依靠早期作业的表现来预测他们贡献的最终成绩。学习结果的行为预测因子通常会缓慢累积，以至于产生准确预测所需的时间几乎没有时间进行干预。我们旨在通过测试我们是否可以仅使用学生在第一次考试前几周的数字行为来预测大型讲座中的表现来改进这些方法。仅基于可塑性行为的早期预测提供了时间和机会，就改变学习和提高表现的方法向学生提出建议。此后，我们采取了一个尚不常见的步骤来应用该模型，并测试为那些被预测表现不佳的人提供数字学习支持是否可以提高他们的成就。使用学习管理系统日志数据，我们使用多种算法测试了由理论一致的行为组成的模型，并获得了一个可以准确预测低分的模型。我们的算法正确识别出 75% 未能获得 B 或更高成绩的学生需要升读下一门课程。我们在下学期应用这个模型来预测成绩水平，并为预计表现不佳的学生提供数字学习策略干预。那些获得建议的人在随后的考试中表现优于同学，