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Review of LRS usage Discussion of new modules. Levelling off in increase primarily due to less XO listed courses being enabled.

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Presentation on theme: "Review of LRS usage Discussion of new modules. Levelling off in increase primarily due to less XO listed courses being enabled."— Presentation transcript:

1 Review of LRS usage Discussion of new modules


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

4  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

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

6  Classroom attendance  Am I being recorded ? End of recording ? Med / Dent / non regular classrooms Flashing light @back of lecture hall  Confidence Monitor?  No Sound  Edit / Change Instructor Names, metadata  Edit/Trim Start/End of recordings

7  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

8  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

9  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)

10  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

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

12 cap·tion [kap-shuhn] Show IPA Noun 1.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) supply a caption or captions for; entitle: to capti on a photograph.

13  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

14  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,

15  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)

16  Accessibility compliance (508 Standards) captioning for students with disabilities as  Provides an additional/complementary learning modality for foreign language students.

17  “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


19  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

20  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.

21  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

22 CrowdSourcingDesignbyDemocracy


24  Enhanced Lecture Recording Viewer  Dedicated Editor App  Caption-It  Search Functionality

25  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.

26  For Pay  For Grade  For Recognition  For Benefit...  For “Play”

27  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.

28  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)

29  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”.

30  “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.

31  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.

32  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

33  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.

34 PROS  Works very well, with high accuracy on clearly legible font  User Independent, no interaction required to get results  Will attempt to recognize everything Hand writing, figures, menus CONS  Tries to recognize anything, including text on desktop, menus etc...  Can’t recognize everything  Can give redundant results

35  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)

36  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”

37  Ability to do machine based automated translation of captioned data  FR / DE / ES  Possibility to have that integration with language department



40  Ability to search  Visual content presented (via OCR)  Spoken content (ASR / time synched transcript data)  User generated content, comments, attachments

41  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

42  Visualize recording usage  Historical data integration

43  Required for user tracking  Required for authentication  Required for authorization


45  Administrators  Instructors  Teaching Assistants  Students  Anonymous

46  learning-is-crowdsourcing/ learning-is-crowdsourcing/  using-introductory-videos-in-online-and-hybrid-courses using-introductory-videos-in-online-and-hybrid-courses  remote-captioning/live-remote-auslan/index.html remote-captioning/live-remote-auslan/index.html

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