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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
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The Big Data revolution:
“DataScope” enhances human perception
Microscope
Telescope
DataScope
(数据镜)
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DataScope enables prediction & optimal
decision making
Predicted Values
of Real World Variables
Predictive
Model
Teacher
Change the World
Real World
Student
Sensor 1
…
Sensor k
…
Non-Text
Data
Multiple
Predictors
(Features)
…
Joint Mining
of Non-Text
and Text
Text
Data
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Big Data creates both challenges
and opportunities for education
• Challenges for education:
Education for Big Data
– Educate many data scientists & engineers quickly and affordably
• Opportunities for education: Big Data 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
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Rest of the talk
1. Education for Big Data
2. Big Data for Education
3. Integration of Big Data and Education
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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 …
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Educating workforce for Big Data
• Question 1: What to teach in Big Data?
PhD, MS, BS in Data Science
• Question 2: How to teach Big Data effectively at large scale with
low cost?
Massive Open Online Courses (MOOCs)
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What to teach? New degrees in Data Science?
Cloud computing
Artificial intelligence
Operations research
Human-computer interactions
…
+ Health, Medicine, Finance,
Smart City, Education, …
Application
Highly interdisciplinary! Analysis
Acquisition
Sensor network
Internet of things
Statistical sampling
…
Aggregation
Data mining
Machine learning
Statistical modeling
Scalable systems
…
Databases
Information retrieval
NLP, Computer vision
…
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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)
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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
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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
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Students are from all over the world!
64,651 Learners
181 Countries
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The majority of learners are 25~44 years old
25~44 years old
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US, India, and China have most of the learners
United States
India
China
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Most learners have full-time job and {BS, MS} degree
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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
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Self-Sustaining Data Set Annotations & Open Challenge
Annotations
Annotations
...
Annotation
Assignment
Auto
Grader
...
Raw Data Set
...
Annotations
Open Challenge
Competition
Assignment
Leaderboard
#1 Team1 0.81
#2 Team 2 0.75
…
Test Collection
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Example of a new data set (for online course retrieval)
High grades  More reliable annotations
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Search Engine Contest: Leaderboard
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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)
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Current Trend:
Integration of MOOCs and Traditional Education
Quality
Traditional Classrooms
HIGH cost
Campus Degree
+
Flipped/Blended classroom
LOW cost
Online Degree
+
High Engagement
component
MINUM cost
Specialization Certificate
MINUM cost
Course Certificate
MOOC
FREE
No Certificate
Scalability
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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
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Part 2: Big Data for Education
Quality
Scalable Intelligent MOOC
Small Classrooms
Towards
Intelligent
MOOC
“Big Data Technology”
Automate grading with machine learning
MOOC
Automate question answering on forums
Scalability
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Traditional Manual Grading
Submitted Assignments
Graded Assignments
Grade:
93
85
….
Proposed Automated Grading
Submitted
Assignments
Multi-dimensional Grade Predictor
Clustering
Improvement
Graded
Assignments
Grade
Verification
Batch
grading
Detailed
Grading Results
Performance &
Behavior Analysis
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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.
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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
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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
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Current MOOC
Student
Record
…
Traditional
MOOC
Platform
MOOC
Course
Content
MOOC
Activity Log
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An Intelligent MOOC
Student
Model
Open Web
Concept
Recommender
…
Student
Modeler
Interactive
Teaching
Interface
MOOC
Activity Log
Personalized
Search agent
Topic/Concept
Graph generator
Concept
Navigator
Concept/Topic
Search agent
MOOC
Course
Content
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Part 3: Integration of Big Data and Education
Intelligent
MOOC Platform
Improve
Scalability
& Quality
Educate
?
Applied to
MOOC Log
Education Big Data
Research
& Develop
Big Data Technology
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Toward a Cloud-based Big Data Virtual Lab
Leaderboard
#1 Team1 0.81
#2 Team 2 0.75
…
App Data 1
Big Data Tool 1
…
Log
Data
Big Data Tool 2
…
App Data N
Leaderboard
#1 Team1 0.5
#2 Team 2 0.3
…
Big Data Tool 1
Big Data Education System
…
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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
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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!)
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Thank You!
Questions/Comments?
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