Computational thinking and assignment resubmission predict persistence in a computer science Mooc
Bibliographic Data
| ID | 21499686 |
|---|---|
| Authors | Chen Chen (0000-0002-9786-8003, Science Education Department, Harvard Smithsonian Center for Astrophysics Harvard University Cambridge Massachusetts, corresponding author), Gerhard Sonnert (0000-0003-4138-2044, Science Education Department, Harvard Smithsonian Center for Astrophysics Harvard University Cambridge Massachusetts), Philip M Sadler (0000-0001-7578-4047, Science Education Department, Harvard Smithsonian Center for Astrophysics Harvard University Cambridge Massachusetts), David J Malan (0000-0001-5338-2522, Science Education Department, Harvard Smithsonian Center for Astrophysics Harvard University Cambridge Massachusetts) |
| Year | 2020 |
| Volume | 36 |
| Issue | 5 |
| Pages | 581-594 |
| Publication date | 2020-10-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Computer Assisted Learning (JOURNAL) |
| Journal identifiers | ISSN: 0266-4909 • E-ISSN: 1365-2729 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1111/jcal.12427 |
| OpenAlex | W3008971766 |
| Language | EN |
| Citations received | 6 |
| References cited | 59 |
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
Mathematics education · Psychological resilience · Computer Science · Engineering · Machine Learning and ELM · Online Learning and Analytics · Psychology · Social Psychology · Teaching and Learning Programming · Artificial Intelligence
Learners’ Performance in a Mooc on Programming
Bilgisayımsal Düşünme Becerilerinin Oyun Programlama Aracılığıyla Geliştirilmesi
Women's participation in Moocs in the IT area
From the learner's perspective
How do thinking styles and STEM attitudes have effects on computational thinking? A structural equation modeling analysis
A systematic review of online persistent learning
Applied Longitudinal Data Analysis
The New Division of Labor
Scratch
Learning in Moocs
Academic self-efficacy and first year college student performance and adjustment.
True Grit
Understanding the massive open online course (Mooc) student experience
Computational thinking for youth in practice
Learning Engagement and Persistence in Massive Open Online Courses (Moocs)
Relation of self-efficacy beliefs to academic outcomes
Bringing computational thinking to K-12
In search of higher persistence rates in distance education online programs
Examination of relationships among students' self-determination, technology acceptance, satisfaction, and continuance intention to use K-Moocs
Self-efficacy
Ambient belonging
Changing “Course”
Promoting engagement in online courses
Predictors of Retention and Achievement in a Massive Open Online Course
Grit
| Unique citing works | 6 |
|---|---|
| Citations per year | 1,2 |
| Citation span | 2021 - 2026 (6) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 6 |