1. 大数据科学与人才培养的互利关系
Education for Big Data and Big Data for Education: Towards Integration of Big Data and Education
ChengXiang Zhai (翟成祥)
Department of Computer Science
University of Illinois at Urbana-Champaign
USA
BDSE2016, May 25, 2016, Guiyang, China

2. The Big Data revolution: “DataScope” enhances human perception
(数据镜)
Microscope
Telescope

3. of Real World Variables Joint Mining of Non-Text
DataScope enables prediction & optimal decision making
Predicted Values
of Real World Variables
Change the World
Multiple
Predictors
(Features) …
Predictive
Model
Teacher
Student
Joint Mining of Non-Text
and Text
Real World
Sensor 1 …
Non-Text
Data
Sensor k …
Text
Data

4. Big Data creates both challenges and opportunities for education
Challenges for education:
Educate many data scientists & engineers quickly and affordably
Opportunities for education:
Leverage Big Data technology to scale up and improve education
Big Data and education are mutually beneficial  Integration!
Education supplies workforce for developing innovative Big Data technology and applications
Big Data supplies technology for scaling up and improving quality of education
Education for Big Data
Big Data for Education

5. Rest of the talk Education for Big Data Big Data for Education
Integration of Big Data and Education

6. Part 1: Education for Big Data
“….(in the next few years) we project a need for 1.5 million additional analysts in the United States who can analyze data effectively…“, -- McKinsey Big Data Study, 2012
The need is global …

7. Educating workforce for Big Data
Question 1: What to teach in Big Data?
Question 2: How to teach Big Data effectively at large scale with low cost?
PhD, MS, BS in Data Science
Massive Open Online Courses (MOOCs)

8. What to teach? New degrees in Data Science?
Application
Cloud computing
Artificial intelligence
Operations research
Human-computer interactions …
+ Health, Medicine, Finance,
Smart City, Education, …
Analysis
Highly interdisciplinary!
Data mining
Machine learning
Statistical modeling
Scalable systems …
Acquisition
Aggregation
Databases
Information retrieval
NLP, Computer vision …
Sensor network
Internet of things
Statistical sampling …

9. How to teach? Emergency of Massive Open Online Courses (MOOCs)
Many platforms: Coursera, Edx, Udacity, 清华大学慕课平台,…
Characteristics
Free/affordable education at large scale on all kinds of topics
Limited assessment support, but strong online community support
Partnership with universities
Early stage of “education revolution” enabled by IT & Big Data (more later)

10. My experience with MOOCs
Taught 2 MOOCs in 2015 = CS410 Text Info Systems at UIUC
Text Retrieval and Search Engines
Text Mining and Analytics
Coordinated Data Mining Specialization: 5 courses + Capstone
Pattern Discovery
Cluster Analysis
Text Retrieval
Text Mining
Visualization
Capstone Project

11. Text Retrieval & Text Mining MOOCs
Each lasted 4 weeks
Modularized video lectures
Weekly quizzes
Programming assignment (open challenge with a leaderboard) with auto grading
Enrollment
~50,000 signed up
> 10,000 seriously watched lecture videos
1,000~1,500 completed the course
700~900 did programming assignments

12. Students are from all over the world!
64,651 Learners
181 Countries

13. The majority of learners are 25~44 years old

14. US, India, and China have most of the learners
United States
India
China

15. Most learners have full-time job and {BS, MS} degree

16. Challenges in teaching “big data” at large scale
General challenges in MOOCs
Variable student background
Variable student needs
Reliability of assessment
Special challenges to “big data”
Programming assignments are essential: variable student resources & background
Availability of interesting real-world data sets
Automated grading of programming assignments

17. Programming Assignments for Text Retrieval & Text Mining MOOCs
Coursera provides no computing resource
Students must work on programming assignments on their own computers
Students download assignments to their own computers
Auto grading is necessary
Grading the output of a program, not the code
Help students learn complex algorithms with minimum effort
We provide a sophisticated toolkit to every student (through a virtual box image)
Students only touch the key algorithm components in the toolkit
Students can experiment with existing algorithms and explore new algorithms
Leverage students to create data sets
Crowdsource annotation of data sets to the students in the course
Open competition with public leaderboard to encourage creative exploration

18. Self-Sustaining Data Set Annotations & Open Challenge
Test Collection
Open Challenge
Competition
Assignment
...
Annotation
Assignment
...
Auto
Grader
Annotations
...
Leaderboard
#1 Team
#2 Team …
Raw Data Set

19. Example of a new data set (for online course retrieval)
High grades  More reliable annotations

20. Search Engine Contest: Leaderboard

21. Overall lessons from the MOOCs
Learners of MOOCs are a different crowd than the on-campus students
Practical mindset, self-motivated, but less background and less time
Pre-quiz is necessary for such technical courses (set realistic expectation)
Learners form self-supporting online communities
Short modularized lecture videos are preferred
Programming assignments are very much appreciated
Crowdsourcing annotations and open competition worked well  MOOC goes beyond education to support research!
Limitations of current MOOCs
Lack of “individual care” (students don’t all get the needed help)
Solely rely on peer grading of sophisticated assignments (unreliable grading & ineffective feedback to students)

22. Current Trend: Integration of MOOCs and Traditional Education
Flipped/Blended classroom +
Traditional Classrooms
Quality
LOW cost
Online Degree
High Engagement
component +
HIGH cost
Campus Degree
MOOC
MINUM cost
Specialization Certificate
MINUM cost
Course Certificate
FREE
No Certificate
Scalability

23. A new online MOOC-based program: MCS-DS at UIUC
MCS-DS = Master of Computer Science in Data Science
Tuition = $20,000
Courses =MOOCs + High Engagement Components
Interdisciplinary
Courses mostly offered by Computer Science Department
Data Mining Specialization
Cloud Computing Specialization
Machine Learning
Other units include School of Information Science & Statistics Department

24. Part 2: Big Data for Education
Quality
Scalable Intelligent MOOC
Small Classrooms
Towards
Intelligent
MOOC
“Big Data Technology”
MOOC
Automate grading with machine learning
Automate question answering on forums
Scalability

25. Traditional Manual Grading
Submitted Assignments
Graded Assignments
Grade: 93 85 ….
Proposed Automated Grading
Graded
Assignments
Submitted
Assignments
Grade
Verification
Multi-dimensional Grade Predictor
Detailed
Grading Results
Batch
grading
Clustering
Improvement
Performance &
Behavior Analysis

26. Preliminary results on grading medical case assignments are promising [Geigle et al. 2016]
Chase Geigle, ChengXiang Zhai, Duncan Ferguson, An Exploration of Automated Grading of Complex Assignments, ACM Learning at Scale 2016.

27. Towards Intelligent MOOC: Limitations of Current MOOC
Instruction materials limited to those pre-defined by an instructor  can’t take advantage of useful materials on the Web
Limited search capability inside a course  can’t easily find the most relevant video clip or discussion posts about a topic
No understanding of students  can’t personalize the instruction and learning experience
Limited support for collaborative learning  can’t leverage massive student behavior data to recommend materials for individual students
Limited support for interactions with students  can’t engage students in a natural dialogue

28. Novel Features of an Intelligent MOOC
Seamless integration of MOOC and Web search  enable students to learn from the Web
Concept/Topic search, navigation, and summarization  enable students to quickly find all materials about a concept or topic
Dynamic and adaptive student modeling  enable deep understanding of student state of knowledge
Lifetime learning from student behavior data enable effective support of collaborative learning
Interactive personalized teaching  enable personalized natural conversations between students and the system

29. Traditional MOOC Platform
Current MOOC
Student
Record
Traditional MOOC Platform
MOOC
Course
Content …
MOOC
Activity Log

30. … An Intelligent MOOC Open Web MOOC Course Content Student Model
Modeler
Open Web
Concept
Recommender
Personalized
Search agent
Topic/Concept
Graph generator
Interactive
Teaching
Interface …
Concept/Topic
Search agent
Concept
Navigator
MOOC
Course
Content
MOOC
Activity Log

31. Many existing technologies can be applied
Algorithms for intelligent information retrieval
Interactive personalized search technologies
Algorithms for dynamic topic map generation
Algorithms for topic discovery, summarization, and analysis
Algorithms for search log analysis
Algorithms for opinion integration and summarization
Algorithms for collaborative filtering and recommender systems …

32. Part 3: Integration of Big Data and Education
Educate
Intelligent
MOOC Platform ?
Scalability
& Quality
Improve
Research
& Develop
Applied to
MOOC Log
Education Big Data
Big Data Technology

33. Toward a Cloud-based Big Data Virtual Lab…
Leaderboard
#1 Team
#2 Team …
Log
Data
Leaderboard
#1 Team
#2 Team …
App Data 1
App Data N …
Big Data Tool 1
Big Data Tool 2
Big Data Tool 1
Big Data Education System …

34. Unification of education, research, and applications!
4. Industry data sets not released to students & researchers
 Privacy-preserving Big Data education & research
3. Well-archived interaction history
 Reproducibility of research
2. Encourage open exploration (research)
 Remove gap between education & research
1. Directly work on industry data sets and problems
 Remove gap between education & applications

35. Final Thoughts: Education Revolution & Automation
Big Data and IT enable education revolution and automation toward more affordable high-quality education
IT enables one teacher to teach many more students than before (efficiency)
Big Data technology would enable “automated” TA/instructor (scalability)
Intelligent MOOC would improve quality of education at low cost
Implications: Many traditional boundaries will likely disappear!
No strict distinction between a teacher and a student (everyone learns from each other)
No strict distinction between grade levels or age groups (learn at your own pace)
No inherent boundaries between different courses (due to high modularization)
No boundaries of subject areas (due to high modularization)
No boundaries of institutions (MOOCs unify all institutions!)

36. Thank You!
Questions/Comments?