1. David Joyner April 5th, 2018 Hasso Plattner Institut
Understanding the Relationship between MOOC Viewing Behavior and Completion Rates
David Joyner
April 5th, 2018
Hasso Plattner Institut

2. Talk Outline
Personal Background Georgia Tech Research Community Research Aggregated Guidelines Questions

3. Personal Background About David:
Senior Research Associate in Georgia Tech’s College of Computing
Associate Director of Student Experience for the OMSCS Program
Director, LucyLabs
Creator and Instructor, CS6460: Educational Technology and CS6750: Human-Computer Interaction
Creator and Instructor, CS1301: Introduction to Computing
Course Co-Instructor: CSE6242: Data & Visual Analytics and CS7637: Knowledge-Based AI

4. Talk Outline
Personal Background Georgia Tech Research Community Research Aggregated Guidelines Questions

5. Study #1: Predicting OMSCS SuccessBy
Question
Findings
Guidelines
Nate Payne, Daniel Hegberg, and David Joyner
Can video-watching data help predict success rates?
Course success can be predicted in part by video-watching: pro-active, on-pace watchers get better grades.
Authentically incentivize keeping up with video lectures.

6. Study #2: Video-viewer TypologyBy
Question
Findings
Guidelines
Georgia Tech VIP Team
In what patterns do students watch videos and complete exercises?
Student approaches can be distilled down to a small number of types: linear- watchers, exercise-jumpers, video- skippers, performance-optimizers, “slackers”.
Ensure assessments are comprehensive.

7. Study #3: mOOCs vs. For-CreditBy
Question
Findings
Guidelines
Georgia Tech College of Computing
How do completion rates differ between for-credit online classes and not-for-credit MOOCs?
Not-for-credit MOOCs have completion rates of 1% to 10%. For-credit online classes have completion rates of 80% to 85%.
Investigate and understand student goals.

8. Talk Outline
Personal Background Georgia Tech Research Community Research Aggregated Guidelines Questions

9. Study #4: Early Course EngagementBy
Question
Findings
Guidelines
Suhang Jiang, Adrienne Williams, Katerina Schenke, Mark Warschauer, Diane O’Dowd
Can early-course behaviors and performance predict success?
Week 1 viewership and performance patterns predict course success; however, deliberate investment in week 1 experience may not work.
Use early course performance data for rapid revision.

10. Study #5: Videos & EngagementBy
Question
Findings
Guidelines
Philip Guo, Juho Kim, Rob Rubin
How do specific video production decisions affect student engagement?
Students engage more with short, headshot, hand-drawn, custom-produced, or informal videos. High production values have relatively little effect on engagement.
Keep videos short and personal. Produce custom online material. Understand the role of production value.

11. Study #6: In-Video Dropout ratesBy
Question
Findings
Guidelines
Juho Kim, Philip Guo, Daniel Seaton, Piotr Mitros, Krzysztof Gajos, Robert Miller
What predicts peak moments of student video interaction?
Interaction peaks occur with first-time viewership, missed content, tutorial steps, visual transitions, and audio explanations.
Vary delivery modalities for the same content.

12. Study #7: Information ProcessingBy
Question
Findings
Guidelines
Tanmay Sinha, Patrick Jermann, Nan Li, Pierre Dillenbourg
Can principles of information processing theory help predict attrition rates?
Clickstream interactions can be abstracted into information-processing behaviors. Deliberative information processing predicts course completion.
Evaluate clickstream data to find the rate of deliberate investigation.

13. Study #8: Fine-Grained Video BehaviorBy
Question
Findings
Guidelines
Nan Li, Lukasz Kidziński, Patrick Jermann, and Pierre Dillenbourg
What do fine-grained video interactions (fat-forward, rewind, change speed, pause)
Pausing, slowing, and repeating are indicative of difficult videos. Speeding up or skipping are indicative of easy videos.
Pay attention to changes in video interaction patterns. Provide quick access to review videos.

14. Study #9: Overall MOOC AttritionBy
Question
Findings
Guidelines
Hanan Khalil & Martin Ebner
What predicts MOOC attrition rates?
MOOC attrition is best explained by student-specific criteria: student time, student motivation, student feelings of isolation, lack of interactivity, and lack of correct background.
It’s not just about video.

15. Study #10: Completion RatesBy
Question
Findings
Guidelines
Andrew Ho, Justin Reich, Sergiy Nesterko, Daniel Seaton, Tommy Mullaney, Jim Waldo, Isaac Chung
Are completion rates the right measure for MOOC success?
Probably not.
Stratify drop-out points. When there is no barrier to entry, there is no barrier to exit.

16. Talk Outline
Personal Background Georgia Tech Research Community Research Aggregated Guidelines Questions

17. Talk Outline
Authentically incentivize keeping up with video lectures. Ensure assessments are comprehensive. Investigate and understand student goals. Use early course performance data for rapid revision. Keep videos short and personal. Produce custom online material. Understand the role of production value.
Vary delivery modalities for the same content.
Evaluate clickstream data to find the rate of deliberate investigation.
Pay attention to changes in video interaction patterns.
Provide quick access to review videos.
It’s not just about video.
Stratify drop-out points.
When there is no barrier to entry, there is no barrier to exit.

18. Questions? For more: omscs.gatech.edu www.DavidJoyner.net
LucyLabs.gatech.edu
Contact me: