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Understanding In-Video Dropouts
and Interaction Peaks in
Online Lecture Videos
Juho Kim (MIT CSAIL)
Philip J. Guo (MIT CSAIL, U of Rochester)
Daniel T. Seaton (MIT Office of Digital Learning)
Piotr Mitros (edX)
Krzysztof Z. Gajos (Harvard EECS)
Robert C. Miller (MIT CSAIL)

2014.03.04
Learning at
Scale
Video Lectures in MOOCs
Classrooms: rich, natural interaction data

Maria Fleischmann / Worldbank on Flickr | CC by-nc-nd
Love Krittaya | public domain

armgov on Flickr | CC by-nc-sa
unknown author | from pc4all.co.kr
liquidnight on Flickr | CC by-nc-sa
How do learners use videos?

Data-Driven Approach:
Analyze learners’ interaction
with the video player
Why does data matter?
• detailed understanding of video usage
• design implications for
– Instructors
– Video editors
– Platform designers

• new video interfaces and formats

Improved video learning experience
How do learners use videos?
• Watch sequentially
• Pause
• Re-watch
• Skip / Skim
Collective Interaction Traces
Student #7888
Student #7887
......
Student #4
Student #3
Student #2
Student #1
video time
Collective Interaction Traces
into Interaction Patterns
interaction
events

video time

second-by-second activity tracking
~40M video interaction events
from 4 edX courses

Learners
127,839

Mean Video Processed
Videos
Length
Events
862
7:46
39.3M

Courses: Computer science, Statistics, Chemistry
Analyzing Clickstream
• Events: play / pause
• In-video time and absolute time
• Learner ID: first-time or re-watching
Clickstream
interaction log

Per-learner
watching
segments

learner xxx

learner xxx

0:00 play
0: 34 pause
0:57 play
1:47 pause

Segment 1 - 0:00-0:34
Segment 2 - 0:57-1:47

Per-second stats
for views,
re-watches,
plays, & pauses
Collective Interaction Patterns
1. In-video dropout
viewership

video time

2. Interaction peaks
interaction
events

video time
1. In-video dropout
viewership

video time

2. Interaction peaks
interaction
events

video time
In-video Dropout
% watching sessions that
end before the video finishes.

viewership

video time
36%: dropouts withinrate few seconds
55%: overall dropout first
viewership

36%
19%

55%

video time

Why?
• Auto-play playing unwanted videos
• Misleading video titles / interfaces
Longer videos lead to more dropouts.
Re-watching sessions lead to more dropouts
than first-time sessions.
Tutorial videos lead to more dropouts
than lecture videos.
Lecture
• introduction to concepts
• continuous flow

Tutorial
• supplementary examples
• step-by-step demos
1. In-video dropout
viewership

video time

2. Interaction peaks
interaction
events

video time
Interaction Peaks
Temporal peaks in the number of interaction
events, where a significant number of learners
show similar interaction patterns

interaction
events

video time
Analytic Workflow
Bin data into
per-second
segments

Apply a
kernel
smoother

Detect peaks
Re-watching sessions show stronger and
more peaks than first-time sessions.
Tutorial videos show stronger and
more peaks than lecture videos.
Lectures

Tutorials
What causes an interaction peak?
Observation: Visual transitions in the
video often coincide with a peak.
lecture
video

number of
re-watching
sessions
Analytic Workflow
Step 1. Visual Analysis
• second-by-second pixel differences
between adjacent frames

head
slide

head
slide

head
slide
Analytic Workflow
Step 2. Peak Categorization
• Manually inspected 80 videos
• Interaction peak <-> Visual transition
Five Explanations for
an Interaction Peak
Type 1. Beginning of new material
Type 2. Returning to content
Type 3. Tutorial step
Type 4. Replaying a segment
Type 5. Non-visual explanation
Type 1. Beginning of new material

interaction
Type 2. Returning to content

interaction
Type 3. Tutorial step

interaction
Type 4. Replaying a segment

interaction
Type 5. Non-visual explanation

interaction
61% of interaction peaks
involved a visual transition.
Peak Category

Frequency

Type 1. Beginning of new material

25%

Type 2. Returning to content

23%

Type 3. Tutorial step

7%

Type 4. Replaying a segment

6%

Type 5. Non-visual explanation

39%

61%
Can interaction data improve
the video learning experience?
Lessons for instructors, video editors,
and platform designers
1. Make shorter videos.
2. Add informative titles and easy navigation.
3. Avoid abrupt visual transitions.
Lessons for instructors, video editors,
and platform designers
4. Make interaction peaks more accessible.

5. Enable one-click access for tutorial steps.
Next Steps: More Data Streams
• What would transcript / text add to the
analysis? How about acoustic data?
Next Steps: Scalability
• Reliably & automatically detect peak types?
• How much data is needed until we see patterns?
viewership

5 learners
video time

viewership

5,000 learners
video time
For instructors & editors: Video Analytics
For learners: Data-Driven Video UI
Contributions
• A first MOOC-scale in-video dropout rate
analysis
• A first MOOC-scale in-video interaction peak
analysis
• Categorization of learner activities
responsible for an interaction peak
• Data-driven design implications for video
authoring, editing, and interface design
Understanding In-Video Dropouts and
Interaction Peaks in Online Lecture Videos

Juho Kim
MIT CSAIL
juhokim@mit.edu
juhokim.com
Backup slides
Domain
educational videos

Theory
interactive learning

How-to videos
MOOC videos

Role of interactivity
Learner control
Subgoal labeling

Method
scalable data collection to realize theory
Crowdsourcing
Learnersourcing
Vision in learnersourcing
• Feedback loop between
– Learners: natural, pedagogically useful activities
– System: improve interaction using learner data

• Visualize and analyze large-scale video
learning activities
• Use data to inform learning platform design
How can we design
online video learning platforms
that are as effective as
in-person classrooms?

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