EAGER: Collaborative Research: Framing Learning for MOOC Student Success: Using Pre-Course Survey Interventions to Support Student Persistence and Performance in MOOCs

众所周知，大规模在线公开课(MOOC)的完成率非常低。研究表明，学生给学习体验带来的心态会对坚持和成绩产生深远的影响。在一项小规模的试点研究中，PIs已经证明，在关键的学术转折点，如新课程的开始，实施小型、低成本的干预措施，大大提高了学生的毅力和表现。该项目将在大规模测试这些小型、低成本的干预措施。该项目将利用在哈佛大学、MITX大学和斯坦福大学OpenEdX大学注册课程的大量学生，并将使用不同的学生集合来测试干预措施。这项研究的结果将使教育政策制定者和学校管理人员了解这些干预措施的有效性和成本效益。这个项目将解决一系列相互关联的研究问题，涉及选择架构、不同干预措施的相加效应、不同课程和学生之间治疗效果的异质性，以及学生对干预措施的非结构化文本的预测能力。对单个干预条件和组合条件的平均治疗效果的直接分析将用于分析学生表现、坚持性和后续课程注册方面的收益。这项研究将预先指定与异质性治疗效果有关的重要协变量的有限数量的理论上知情的假设，然后使用多元回归模型进行更广泛的事后探索性调查，以检查其他治疗效果。最后，学生的回答将使用文本分析预处理方法进行分析，如词干和去掉停用词，以创建n元语法特征矩阵，该矩阵将使用套索正则化逻辑回归进行检验。

Completion rates in Massive Open Online Courses (MOOCs) are known to be notoriously low. Research has shown that the frame of mind that a student brings to a learning experience can have a profound impact on persistence and achievement. In a small-scale pilot study, the PIs have demonstrated that small, low-cost interventions administered at key academic transition points, such as the beginning of a new course, substantially improve student persistence and performance. This project will test these small, low-cost interventions at a large-scale. The project will take advantage of the large numbers of students registering for courses at HarvardX, MITx, and Stanford OpenEdX and will test the interventions using a heterogeneous collection of students. The results of this study will inform education policymakers and school administrators about the effectiveness and cost-effectiveness of these interventions. This project will address a set of linked research questions about choice architecture, the additive effects of diverse interventions, the heterogeneity of treatment effects across diverse courses and students, and the predictive power of student unstructured text produced in response to the interventions. A straight-forward analysis of average treatment effects across the individual intervention conditions and the combined condition will be used to analyze gains in student performance, persistence, and subsequent course registration. The study will pre-specify a limited number of theoretically-informed hypotheses about important covariates linked to heterogeneous treatment effects, and then conduct a broader post-hoc exploratory inquiry using multiple regression modeling to examine other treatment effects. Finally, student responses will be analyzed using text analysis pre-processing methods, such as stemming words and removing stop words, to create an n-gram feature matrix that will be examined using LASSO regularized logistic regression.