Outcome Predictions of Students in Massive Open Online Courses (OPSMOOC)

这个探索性项目旨在开发评估大规模开放式在线课程（MOOC）产品的方法。 MOOC专门为网络上的互动学习而设计，在学生的时间承诺和时间安排方面具有相当大的灵活性，在改善高等教育的获得方面具有巨大的潜力。 然而，到目前为止，MOOC往往表现出高流失率。 由于入学人数达数万或数十万，即使是提高毕业率的微小变化，也可能对受益于这种新教学形式的学生的绝对人数产生重大影响。该项目涉及对通过edX平台提供的非常受欢迎的哈佛MOOC的深入研究。 它将主要数据（关于参与学生的特征和他们参加课程的动机）与与分析中的学生课程活动相关的数据（包括描述性统计，风险建模和多元线性回归）相结合，以探索传统的课程结果（例如，学生成绩的测量）和持久性/损耗。通过关注MOOC开发早期的课程完成指标，该项目有可能将注意力集中在MOOC的那些方面，这些方面对学生的成功及其对STEM领域的持续兴趣至关重要。预计研究结果和分析方法都可以纳入教学平台，以帮助教师监测困难和快速改进课程，并且朝着更统一的数据收集的进展将允许在各种MOOC之间进行比较，特别是那些具有相似目标或内容领域的MOOC。

This exploratory project seeks to develop methods to evaluate Massive Open Online Course (MOOC) offerings. Designed specifically for interactive study over the web, with considerable flexibility in terms of students' time commitment and scheduling, MOOCs have tremendous potential for improving access to higher education. To date, however, MOOCs have tended to exhibit high attrition rates. With enrollments in the tens or hundreds of thousands, even modest changes that increase completion rates could have a major impact on the absolute numbers of students benefiting from this new form of instruction. This project involves in-depth examination of a highly popular Harvard MOOC, offered through the edX platform. It combines primary data (on participating students' characteristics and their motivations for taking the course) with data pertaining to students' course activities in analyses (including descriptive statistics, hazard modeling, and multiple linear regression) to explore both traditional course outcomes (e.g., measures of student achievement) and persistence/attrition. By focusing on the metric of course completion early in the development of MOOCs, this project has potential to direct attention to those aspects of MOOCs that appear critical to student success and their continuing interest in STEM fields. It is anticipated that both research findings and methods of analyses can be incorporated into the instructional platform to aid instructors in monitoring difficulties and in rapid course improvement and that progress toward more uniform data collection will allow comparisons across a variety of MOOCs, particularly those with similar goals or content domains.