VidWiki: enabling the crowd to improve the legibility of online educational videos

Videos are becoming an increasingly popular medium for communicating information, especially for online education. Recent efforts by organizations like Coursera, edX, Udacity and Khan Academy have produced thousands of educational videos with hundreds of millions of views in their attempt to make high quality teaching available to the masses. As a medium, videos are time-consuming to produce and cannot be easily modified after release. As a result, errors or problems with legibility are common. While text-based information platforms like Wikipedia have benefitted enormously from crowdsourced contributions for the creation and improvement of content, the various limitations of video hinder the collaborative editing and improvement of educational videos. To address this issue, we present VidWiki, an online platform that enables students to iteratively improve the presentation quality and content of educational videos. Through the platform, users can improve the legibility of handwriting, correct errors, or translate text in videos by overlaying typeset content such as text, shapes, equations, or images. We conducted a small user study in which 13 novice users annotated and revised Khan Academy videos. Our results suggest that with only a small investment of time on the part of viewers, it may be possible to make meaningful improvements in online educational videos.

[1]  Peter Bächtold,et al.  Bottom-Up Approach , 2013 .

[2]  James Fogarty,et al.  Amplifying community content creation with mixed initiative information extraction , 2009, CHI.

[3]  Michael Kipp Spatiotemporal Coding in ANVIL , 2008, LREC.

[4]  Celine Latulipe,et al.  The choreographer's notebook: a video annotation system for dancers and choreographers , 2011, C&C '11.

[5]  Michael Riegler,et al.  Annotation of endoscopic videos on mobile devices: a bottom-up approach , 2013, MMSys.

[6]  R. Stuart Geiger,et al.  The work of sustaining order in wikipedia: the banning of a vandal , 2010, CSCW '10.

[7]  Oded Nov,et al.  Determinants of wikipedia quality: the roles of global and local contribution inequality , 2010, CSCW '10.

[8]  Antonio Torralba,et al.  LabelMe video: Building a video database with human annotations , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[9]  Stéphane Ayache,et al.  The LIGVID System for video retrieval and concept annotation , 2010, 2010 International Workshop on Content Based Multimedia Indexing (CBMI).

[10]  Edward Cutrell,et al.  "Yours is better!": participant response bias in HCI , 2012, CHI.

[11]  Oded Nov,et al.  Technology-mediated contributions: editing behaviors among new wikipedians , 2012, CSCW.

[12]  David Salesin,et al.  Video object annotation, navigation, and composition , 2008, UIST '08.

[13]  Matthew Reid,et al.  Quality control mechanisms for crowdsourcing: peer review, arbitration, & expertise at familysearch indexing , 2013, CSCW '13.

[14]  Derek Lackaff,et al.  An Analysis of Topical Coverage of Wikipedia , 2008, J. Comput. Mediat. Commun..

[15]  Stefanie Müller,et al.  Using temporal video annotation as a navigational aid for video browsing , 2010, UIST '10.

[16]  J. Giles Internet encyclopaedias go head to head , 2005, Nature.

[17]  James M. Purtilo,et al.  Measuring the wikisphere , 2009, Int. Sym. Wikis.

[18]  Pablo César,et al.  Creating and sharing personalized time-based annotations of videos on the web , 2010, DocEng '10.

[19]  Anant Agarwal,et al.  TypeRighting: combining the benefits of handwriting and typeface in online educational videos , 2013, CHI.

[20]  Scott R. Klemmer,et al.  Shepherding the crowd yields better work , 2012, CSCW.

[21]  Aaron Halfaker,et al.  Making peripheral participation legitimate: reader engagement experiments in wikipedia , 2013, CSCW.

[22]  Didier Stricker,et al.  CoVidA: pen-based collaborative video annotation , 2012, VIGTA '12.

[23]  K. Saravanan,et al.  wikiBABEL: community creation of multilingual data , 2008, Int. Sym. Wikis.

[24]  Aaron Halfaker,et al.  Using edit sessions to measure participation in wikipedia , 2013, CSCW.