Videos are used extensively in cyber learning. Analyzing video data and using interactive videos in cyberlearning are emerging areas in learning technologies and big data analytics. Novel video analytics tools can transform traditional (linear) videos into interactive learning objects; therefore improve the classroom interactions and students’ engagements. Data from cybersecurity program at University of Maryland show that students’ engagements improved six times after a video analytics tool (inVideo) was introduced. The presenter will discuss the latest development of inVideo, a video analytics tool that is able to analyze video content automatically in both language and frames. In addition, the presenter will discuss correlations between low accuracy in automatic transcripts with early recording methods that produce huge ambient noises and echo. The research finding is helpful for curricula developments in cyberlearning so that newly produced videos can be indexed, searched and annotated. Using the video data analytics technologies, long videos can be easily “cropped” and annotated so that learners can easily focus on important concepts during their study. Though tested in cybersecurity education, this technology can be easily applied to math and other STEM subjects in cyberlearning setting.

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)

23. Online Shopping
A=90%
Individual 22
A=10%
rfeb07
A=10%
phone.p2
A=90%

24. Phone.p2
A=90%
r002
A=10%
edX. ajax
A=60%
edX.EDM
A=50%

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