Computational thinking and assignment resubmission predict persistence in a computer science MOOC

被引：14
作者：
Chen, Chen [1 ]
Sonnert, Gerhard [1 ]
Sadler, Philip M. [1 ]
Malan, David J. [1 ,2 ]
机构：
[1] Harvard Univ, Harvard Smithsonian Ctr Astrophys, Sci Educ Dept, Cambridge, MA 02138 USA
[2] Harvard Univ, John A Paulson Sch Engn & Appl Sci, Cambridge, MA 02138 USA
来源：
JOURNAL OF COMPUTER ASSISTED LEARNING | 2020年 / 36卷 / 05期
基金：
美国国家科学基金会;
关键词：
OPEN ONLINE COURSES; SELF-EFFICACY; STUDENTS; MOTIVATIONS; ENGAGEMENT; VARIABLES; PATTERNS;
D O I：
10.1111/jcal.12427
中图分类号：
G40 [教育学];
学科分类号：
040101 ; 120403 ;
摘要：
Massive open online course (MOOC) studies have shown that precourse skills (such as precomputational thinking) and course engagement measures (such as making multiple submission attempts with assignments when the initial submission is incorrect) predict students' grade performance, yet little is known about whether these factors predict students' course retention. In applying survival analysis to a sample of more than 20,000 participants from one popular computer science MOOC, we found that students' precomputational thinking skills and their perseverance in assignment submission strongly predict their persistence in the MOOC. Moreover, we discovered that precomputational thinking skills, programming experience, and gender, which were previously considered to be constant predictors of students' retention, have effects that attenuate over the course milestones. This finding suggests that MOOC educators should take a growth perspective towards students' persistence: As students overcome the initial hurdles, their resilience grows stronger.
引用
收藏
页码：581 / 594
页数：14
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