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

• 批准号: 1646976
• 负责人: Justin Reich
• 金额: $ 12.48万
• 依托单位: Massachusetts Institute of Technology
• 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=1646976&HistoricalAwards=false
• 关键词: EAGER; Collaborative; Research; Framing; Learning

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，大规模在线开放课程（MOOCs）的完成率非常低。研究表明，学生在学习过程中所表现出的心态对坚持不懈和取得成就有着深远的影响。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）实施小型、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型、低成本的干预措施。该项目将利用在HarvardX、MITx和Stanford OpenEdX注册课程的大量学生，并将使用不同类型的学生来测试干预措施。本研究的结果将为教育政策制定者和学校管理者提供有关这些干预措施的有效性和成本效益的信息。该项目将解决一系列相关的研究问题，包括选择结构、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，以及学生响应干预措施产生的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的直接分析将用于分析学生表现、坚持和随后的课程注册方面的收益。该研究将预先指定与异质性治疗效果相关的重要协变量的有限数量的理论上知情假设，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，例如词干提取和删除停止词，以创建n-gram特征矩阵，该特征矩阵将使用LASSO正则化逻辑回归进行检查。

项目成果

Scaling up behavioral science interventions in online education

• DOI: 10.1073/pnas.1921417117
• 发表时间: 2020-06-30
• 期刊: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• 影响因子: 11.1
• 作者: Kizilcec, Rene F.;Reich, Justin;Tingley, Dustin
• 通讯作者: Tingley, Dustin

Open Education Science

• DOI: 10.1177/2332858418787466
• 发表时间: 2018-07-01
• 期刊: AERA OPEN
• 影响因子: 2.8
• 作者: van der Zee, Tim;Reich, Justin
• 通讯作者: Reich, Justin