Embed Size (px)
Citation preview
Collaboration and Education Group
Formed about 12 months ago
Mission: To explore novel technologies and applications that
enhance collaboration and education / training
Current work focuses on streaming media
Research model
Evaluation: Laboratory and Field Studies
BuildPrototype
Evaluation /Publication
RefinePrototype
ProductImpact
Technology and Education
Two broad facets: Technology for improved content
deep models of subject matter and studentactive exploration of subject (simulations)relate to students context/environment (situated
learning)MOSTLY DOMAIN DEPENDENT
Technology infrastructure for:course and student managementcontent creationdelivery / distributioncollaborationMOSTLY DOMAIN INDEPENDENT
Both aspects are important and complementary
Technology Adoption Phases
Phase-1: digital version of non-digital process
Phase-2: value-added features appear in digital version
Phase-3: process and technology re-design
Why Consider Multimedia?
Network, processor, memory capability changing quickly Reasoning about exponential growth
Simultaneous emergence of live and on-demand capability
Shift in the definition of scholarship
Ongoing Projects
MSTE and MURL: Online Seminars
Time Compression, Skimming, Indexing, Browsing
MRAS: Multimedia Annotations and Authoring
Flatland: Telepresentations
MSTE Presentations
Logs of ~10,000 sessions by over 2000 users
Some results: On-demand audience about 40% of live audience 60% < 5 minutes Viewers jump around video Initial portions much more likely to be watched
Presentations will be designed differently in future Present key messages early in talk Present key messages early in slide Use meaningful slide titles Reveal talk structure in slide titles Consider post-processing talk for on-line viewers
Analysis of Online Presentation Viewing
Logs of ~10,000 sessions by over 2000 users
Some results: On-demand audience about 40% of live audience 60% < 5 minutes Viewers jump around video Initial portions much more likely to be watched
Presentations will be designed differently in future Present key messages early in talk Present key messages early in slide Use meaningful slide titles Reveal talk structure in slide titles Consider post-processing talk for on-line viewers
Ongoing Projects
MSTE and MURL: Online Seminars
Time Compression, Skimming, Indexing, Browsing
MRAS: Multimedia Annotations and Authoring
Flatland: Telepresentations
Time Compression, Skimming, Indexing
While text documents are easy to skim, that is not true for audio-video
Ability to skim can be a key advantage of web-video time-compression: up to ~2-fold; nothing thrown away
skimming: > 2-fold; some content thrown away
indexing: adding navigable structure
Also useful in “live” broadcast scenarios e.g., late joiners can catch up to live talk
Time Compression: Synchronized Audio and Video
To preserve pitch: throw away portion of each 100ms chunk, then stitch together
Basic signal processing well known, but several systems issues
Results of lab studies: People choose ~1.4 speed, don’t adjust much They like it
“I think it will become a necessity… Once people have experienced it they will never want to go back. Makes viewing long videos much, much easier.”
Comprehension may go up
Time-Compression Demo
Skimming: Compression Goes Nonlinear
To beat 2x speedup, must throw away content
Sources of information audio: pauses, intonation, speech-to-text and NLP video: scene changes other: slide-changes, previous viewers’ patterns
Lab studies of 4x-5x speedup Viewers learn from automatic summaries Viewers like and learn more when author-edited Perception of quality increases over time
Mixed-initiative summarization is promising
Indexing
Vanilla video provides no structure for navigation
Indexing provides navigable structure; examples: textual table of contents (slide titles) video shots / scenes speech-to-text => NLP => topic detection
Ongoing Projects
MSTE and MURL: Online Seminars
Time Compression, Skimming, Indexing, Browsing
MRAS: Multimedia Annotations and Authoring
Flatland: Telepresentations
Multimedia / Temporal Annotations
Motivating scenarios: a virtual university
all students are remote, asynchronously watching lecture videos
a standard universitymaking better use of in-class time
Temporal annotations: annotations associated with streaming media
each annotation is linked to the media time-line
annotations stored separately from the media files
Ability to annotate can add significant value shared notes for asynchronous collaboration
question-answers linked to a streaming-video lecture archived feedback for the instructor
personal notes on audio-video found on the web
personal/shared table of contents; summarizations
annotations may be computer generateduse speech-to-text providing search and seek abilitycaptured strokes from electronic white-boardcaptured questions, slide-flips, from “live” broadcast
...
Results from Preliminary User Studies
Personal note-taking study (MRAS vs. Paper) similar # of notes (~1 / minute)
positioning: none in paper; ~10-15s later in MRAS
all subjects preferred MRAS (although more time), and thought more useful for future reference
Shared notes study text preferred to audio
14/18 stated more participation than in “live” session
auto-tracking particularly useful
Currrent Work
MSTE class to use MRAS and recorded lectures Can we increase instructor productivity? Can we emulate live-classroom discussion / community
formation in an asynchronous environment using MRAS?
Ongoing Projects
MSTE and MURL: Online Seminars
Time Compression, Skimming, Indexing, Browsing
MRAS: Multimedia Annotations and Authoring
Flatland: Telepresentations
Flatland Tele-presentation System
Joint project with the Virtual Worlds Group Flexible architecture for distributed collaborative
applications
Target scenarios: presentations to remote audience
online conferences
distributed tutored-video-instruction
...
The Flatland Project
Do We Need to Sacrifice Quality?
The goal is to improve it
Stanford Tutored Video Instruction (TVI) Process:
video tapes of un-rehearsed live lecturessmall group of students watch along with a para-
professional tutor
Results from 1978-86 All MSEE: 1800 students, avg. GPA 3.40 TVI-MSEE: 89 students, avg. GPA 3.62
Similar observations recently for D-TVI version
Stanford TVI Experiments: 10/73 - 3/74
remote TVI students with tutor do best it helped “at-risk” students even more
Source: J.F. Gibbons, et al. Science, Vol. 195, No. 4283, 18 March 1977
2.4
2.7
3
3.3
3.6
3.9
302Campus
55Live Video
6Tape: No
Tutor
27Tape: With
Tutor
Gra
de
Po
int
Av
era
ge
Flatland Experiences
Initial use in 3 multi-session MSTE classes Presentations from desktop to remote audience
Students: Liked the convenience Liked ability to multitask Did not think learning suffered
Instructors: Missed familiar sources of feedback Comfort level rose over time for 2 of 3
Overall: Lack of awareness of others a key problem
Issues Being Explored Creating presence and awareness
representing attendees; gaze; activity level; ...
Providing for interactivity; protocols for online talks types of widgets; floor control; multiple back channels
Complexity of interface for speaker / audience use of channels over time; different physical contexts; …
Capture and replay of tele-presentations capture “all” activity; time-compression; annotations
Activity Surrounding Teaching/Learning
Pre-authoring Slides, web notes, reference material, exercises, …
Content delivery Synchronous delivery to local/remote audience Archived for on-demand audience and review
On-demand access by students Watch content; personal notes; TOC; index; …
Discussion around content Synchronous: small group; one-on-one Asynchronous
Post-lecture work by instructor / tutor Answer questions; discussions; feedback & redesign; … Student evaluation
…
Concluding Remarks Key drivers of change
market needs technology
Key new directions learner-centric asynchronous; small-group synchronous
Key challenges concrete studies to indicate effectiveness technology/products taking value beyond cost business model and bootstrapping issues
For More Information:
http://www.research.microsoft.com
Watching Behavior Within a Session
010203040506070
0 10 20 30 40 50 60 70 80 90
Nth minute into the talk
Use
r co
un
t
