replayEDU Platform Modules

Review of LRS usageDiscussion of new modules

Overview of deployment @McGill

Overview of Usage @McGill

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Enabled Courses

Enabled Courses

Levelling off in increase primarily due to less XO listed courses being enabled

Overview of Usage @McGill

One of the largest collections of Lecture Recordings in the world (> 80,000 individual recordings)

Streaming between 700,000 GB to 1.4 TB of data a day (Videotron/Bell quotas are 50 gigs/month)

Equivalent to between 8,000 to 14,000 hours of content consumed / day

In use/accessed by > 20,000 enrolments

LRS / COOL Platform :Focus as a Pedagogical tool

ped·a·go·gy   [ped-uh-goh-jee, -goj-ee]  Show IPAnoun, plural -gies.1.the function or work of a teacher; teaching.2.the art or science of teaching; education; instructional methods.

Pain Points

Classroom attendance Am I being recorded ? End of

recording ?Med / Dent / non regular classroomsFlashing light @back of lecture hall

Confidence Monitor? No Sound

Edit / Change Instructor Names, metadata

Edit/Trim Start/End of recordings

Classroom attendance

Is a decreased attendance such bad thing? Trends towards smaller classroom size

with more interaction between instructors/teachers

Students who are present are more focused/dedicated to being there. Less distracters

Delayed/Selective publishing of recordings

Focus with tools like clickers for attendance

ITools: Instructor Tools

Course Configuration parameters Empowering the instructors to manage

their own course configuration parameters. Default Publishing state Enabling of Downloads, etc... Notification settings on recording availability

Recordings Editor Enable/Disable individual recordings once

published into the IMMS

Recording Editor

Metadata based editing of all fields related to an individual recording Enabled/Disabled Recording Name Recording Type (Lecture, Tutorial) Change/Edit Instructor Name Description of recording (searchable

parameters)

Web Editor

Provides the ability to set in and out points on a recording to trim a set recording too.

Requires Server Side processing capabilities

Require User/Instructor intervention

Requirements on role management

Publisher

Allow recordings to be published into a disabled state, so in the course context, but NOT viewable/accessible to students

Captions

cap·tion   [kap-shuhn]  Show IPANoun1.a title or explanation for a picture or illustration, especially in a magazine.2.a heading or title, as of a chapter, article, or page.3.Movies, Television . the title of a scene, the text of a speech, etc., superimposed on the film and projected onto the screen.4.Law . the heading of a legal document stating the time, place, etc., of execution or performance.verb (used with object)5.to supply a caption or captions for; entitle: to caption a photograph.

Objectives

Determine Captioning need and expectations Target audience Accuracy rating Level of involvement (hands on/off approach)

Demonstrate Cool Platform Captioning Capabilities and associated Modules

Discuss Implementation options and strategies

Close Captions : CC

For TV/Movie content : CRTC / FCC mandated Typically embedded in TV signal Typically done in post-production, or near real-

time

For the WEB ? No one standard, many different players

options (Flash, QuickTime, Silverlight, HTML5) Many different caption formats: ▪ DFXP, SRT, SAMI,

Caption Module (CC)

Native CC file integration

Automated workflow to integrate ASR data in order to jump start crowd-sourced closed captioning.

Web Based Caption Creator Web based CrowdCaptionCorrector

(CCC)

Caption : Benefits

Accessibility compliance (508 Standards) captioning for students with disabilities as

Provides an additional/complementary learning modality for foreign language students.

Captions : Anecdotal

“Very good recording program! What I like about it is the captions. Sometimes, when I re-listen to the recording, I have to rewind to verify what the professor said. With the captions in this program, I can simply pause the recording and read them. This helps me to save more time. As for the searching option, believe that it is very useful when I need to look up for a specific topic from the class lectures. Thank you.” - Han Julie Do

Captioning/Transcription Workflow

ASR Challenges

Recognition accuracy 65->95% Approval Finalization Deployment

Key Factors which drive accuracy and recognition success: Raw audio source quality (microphone) speaker clarity (tone/elocution) vocabulary

ASR Challenges: Cool Platform Solutions

Vocabulary augmentation via OCR module ASR feedback mechanism : Submission of

corrected captions to improve accuracy over time

Web Based Instructor Training Tool, to generate customized speaker profiles

By captions not being static files, rather living in a database and available to the Crowd-Captioning Interface, constantly getting better.

ASR Hardware Partners

Integration of third party tools/appliance

DocSoft AV platform Single 1RU appliance can generate

speaker independent ASR text Can process 22-24 hours content / day

(1:1 ratio)

Testing Integration options from MAVIS (MS Research) as well as Dragon Dictate from Nuance

Crowd-sourcing : What is it ?

CrowdSourcingDesignbyDemocracy

Crowd-sourcing : Examples

Demos

Enhanced Lecture Recording Viewer

Dedicated Editor App

Caption-It

Search Functionality

Crowd Sourcing Captions ?

Why use students: Students are in effect “subject matter

experts” They have an understanding of the

context better than any 3rd party translator

Know the vocabulary, and the speaker Turn around time, distributed across a

number of students, getting high accuracy can happen fairly quickly.

Incentives for generating captions

For PayFor GradeFor RecognitionFor Benefit...For “Play”

Incentives : For Pay

As a service offered by OSD for students with learning difficulties, the University or department could hire “reviewer” or assign dedicated editors to review and ascertain the accuracy of the caption data.

PRO : You know it will get done, in predetermined/predictable time frame.

CONS : Could be expensive, time consuming, accuracy from non Subject mater experts.

Incentives : For Grades

Participation marks (akin to the use of clickers for presence grade)

As “assignments” in language/linguistics departments:- captioning “segments” of recordings, (*correcting translations, language depatments)

For Recognition / For Benefit

By peers / Instructors, by ...- posted as participators- community recognition, corrector score (level 1 for 500 corrections, levelling up)

Leveraging Student group NTC Writers who are typically doing this already, empowering them as “Editors”.

For “Game” : Edutainment / Gamification

“Gamification typically involves applying game design thinking to non-game applications to make them more fun and engaging.”

Based on core Crowd-sourced framework: Ongoing, and always available as part of the

enhanced lecture recording player. Dedicated Caption-It/Review It Interface

For example: Loading a 5 caption revision window before being able to get access to recordings.

Caption-It

Segments of the transcript, or caption chunks are stored in the database with their timing information. The Caption-It web app finds segments which have not been reviewed and assign them randomly to different users.

Caption-It

Correcting 5 captions typically takes a little under 1-2 minutes.

There are approximately 500 caption segments per hour of recording

So it would take < 100 students “playing” the Caption-It 5 revisions to cover an entire 1 hour recording

OCR Module

OCR : Optical Character Recognition Ability to search/recognize visual

material presented / captured in a recording for keywords.

Works across different mediums, from web pages, to PDFs, PPTs, event in some case hand written acetates.

OCR Pros/Cons

PROS

Works very well, with high accuracy on clearly legible font

User Independent, no interaction required to get results

Will attempt to recognize everythingHand writing, figures, menus

CONS

Tries to recognize anything, including text on desktop, menus etc...

Can’t recognize everything

Can give redundant results

Search Module

Basic search ability, on standard recordings metadata (date, type, descriptions)

Return results for keywords on a per recording or course wide basis

Pluggable expandability to return results across index-able data sources

Caption data (what was said) Slide content data (what was shown)

ASR/CCC Pilot Project

Deploy CC, ASR/OCR on a number of courses

Deploy CCC module to increase accuracy and evaluate student engagement

Look into : Evaluate methods of incenting students,

departments (linguistics/language) Evaluate methods of “participation marks”

Translate Caption

Ability to do machine based automated translation of captioned data

FR / DE / ES Possibility to have that integration

with language department

Dr. Harpp / OSS results from Poll

Additional Modules

Search Trifecta

Ability to search Visual content presented (via OCR) Spoken content (ASR / time synched

transcript data) User generated content, comments,

attachments

Interaction Module

Record Bookmarks into a recording Questions, Review Items, Answer to

a Question Allow students to interact socially on

the lecture recordings Ability to see clusters in the user

activity on the timeline

Stats/Reporting Module

Visualize recording usage Historical data integration

SP: User Identity

Required for user tracking Required for authentication Required for authorization

CP: Role Management

Roles and Responsibilities

Administrators Instructors Teaching Assistants Students Anonymous

Appendix : Crowd-source captioning

http://www.onlineuniversities.com/blog/2011/11/the-future-of-e-learning-is-crowdsourcing/

http://www.learningsolutionsmag.com/articles/107/teacher-presence-using-introductory-videos-in-online-and-hybrid-courses

http://training.qld.gov.au/individuals/students/training-and-work/live-remote-captioning/live-remote-auslan/index.html