Paul Wang SOED 2016
- 1. Video-based Big Data Analytics in Cyberlearning Shuangbao (Paul) Wang, Ph.D. Professor, Director Center for Security Studies
- 2. 1913 1920 * 1080 * 50 * 60 -- 3000 * 2M -- 6G 2016
- 3. Videos in Cyberlearning – big data • Video use is growing rapidly in education (and elsewhere) • MOOCs (ex. EdX, Coursera, Udacity) rely on videos • Huge repositories (ex. RBDIL, NSDL) contain extraordinary amounts of valuable video data • Videos are big data, unstructured – Hardly being analyzed by current data analytics tools • Cyberlearning requires more interactions.
- 4. RBDIL – Rutgers University
- 5. NSDL – National Science Digital Library
- 8. Using Videos Effectively in Learning • Interactive? (DoD) • Assessments ? (adaptive) • Easy for instructors to use? (course development) • Accessibility? (Mac, mobile) • Track students’ growth? (over multiple years) • Long videos vs. short ones? (crop) • Recording methodology? (noises and echo)
- 9. inVideo - A Novel Big Data Analytics Tool for Video Data Analytics • Analyzing Video by Keywords • Content Based Image Retrieval (CBIR) • Pattern Recognition (PR) • Multiple Languages
- 10. inVideo: Analyzing Video by Keywords • Audio is stripped and used to generate a transcript • Transcript is indexed back to original media • Video is now searchable/mineable by keyword Result shows that 7 video clips from three videos were retrieved for keyword “online”
- 11. inVideo: Content Based Image Recognition (CBIR) • Provide a picture reference • Search video content (frames) that contains the reference picture • Return the video clips Result shows that the match is at 0.05th sec. in video named “student”
- 12. inVideo: Pattern Recognition (PR) • Provide a keyword reference • Search video content (frames) that contains the object described as “keyword” The results shows three videos were retrieved that contain objects look like the keyword “credit card”
- 13. inVideo: Analyzing Different Languages • Input keywords in other languages • Search transcript for keywords in that language • Retrieve video clips that match The results shows two video clips in one video contain the keyword “学生” (the word “student”in Chinese).
- 15. Clip#0 Introduction Clip#1 Pwd cracking Clip#2 Port scan Clip#3 Encryption Clip#4 Forensics Clip#5 Cyber weapon Video: Linear – to -- Interactive Before: A 46-minute long video After: 2-3 minute video clips with assessments in between and at the end Assessments
- 16. • Learning objects composed of short video clips • Assessment of learning outcomes of studying video content • Teachers: selecting a video segment and assign Q&As Dragthe stage bar and click “From”button; continue draggingand then click “To” button. Add a question and answers. Define “Learning Objects” (Instructors)
- 17. Learning and Assessments • View the whole video, and take a quiz • Review the video clip corresponding to the question Click “Review” button to review the video clip; click the speak icon to speak out the question; click “Confirm”to check your answer
- 18. Case Study: Cybersecurity Program Student Engagement for the 24 Classrooms inVideo: Turn videos into interactive learning contents
- 19. Low Accuracy Video1: 45 Video Clips Video1: 29 Video Clips Video3: 29 Video Clips Video1: Individual Video 2: Small Class Video 3: Full Classroom
- 20. Accuracy of transcripts of 9 video clips from three original videos
- 21. SDLC Accuracy comparison: • “hits rate” before – standard parameters; • “hits rate” after – revised parameters No improvement!
- 22. Correlations? low accuracy vs. recording methods • Low accuracy – 10% or less – Individual22,(45+31+29 video clips) • Medium accuracy – 40 to 60% – EdX_EDM, EdX_ajax, (20 video clips) • High accuracy – 90% or higher – Phone.p2, online_shopping, (30 video clips)
- 25. Voice-over re-Recording • Re-recorded voices on videos • Merge audio track with original videos • Signal analyzing while recording • Accuracy significantly improved!
- 26. Correlations • Low accuracy is expressed in high quefrency – A measurement of ambient noises – echo • Recording methods – One microphone (per person) – Used condenser microphone instead of dynamic one • Recording setting could affect the audio quality (for digital processing) – Experiments – Guide to digital recording
- 29. Transcript Time-stamping System (TTS) • Adding timestamps for already transcribed videos • Fuzzy Search
- 30. Further Discussions • Web API • Search progression (over the years) • Voice cancellation/reduction • Automatic Time-stamping • Curriculum Development • Build community - collaborations
- 31. Publications
- 32. Shuangbao (Paul) Wang, Ph.D. paul.wang@computer.org William Kelly, Metonymy Corporation Xiaolong Cheng, Doctoral Candidate, George Washington University