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

EAGER：协作研究：为 MOOC 学生成功构建学习框架：利用课前调查干预措施支持学生在 MOOC 中的坚持和表现

基本信息

批准号：
1646978
负责人：
Dustin Tingley
金额：
$ 17.52万
依托单位：
Harvard University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-12-01 至 2018-11-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1646978&HistoricalAwards=false
关键词：
EAGER Collaborative Research Framing Learning

项目摘要

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.

众所周知，大规模开放式在线课程（MOOC）的完成率非常低。研究表明，一个学生在学习过程中的心态会对坚持和成就产生深远的影响。在一项小规模的试点研究中，PI已经证明，在关键的学术过渡点（如新课程的开始）进行的小规模，低成本的干预措施大大提高了学生的坚持和表现。该项目将大规模测试这些小规模、低成本的干预措施。该项目将利用大量注册哈佛X、麻省理工学院和斯坦福大学OpenEdX课程的学生，并将使用不同类型的学生来测试干预措施。这项研究的结果将告知教育政策制定者和学校管理人员这些干预措施的有效性和成本效益。该项目将解决一组相关的研究问题，关于选择架构，不同干预措施的叠加效应，不同课程和学生的治疗效果的异质性，以及学生非结构化文本的预测能力。对单个干预条件和组合条件下的平均治疗效果进行直接分析，以分析学生表现、持久性和后续课程注册的收益。本研究将预先规定关于与异质性治疗效应相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，以检查其他治疗效应。最后，学生的回答将使用文本分析预处理方法进行分析，例如词干提取和删除停用词，以创建一个n-gram特征矩阵，并使用LASSO正则化逻辑回归进行检查。