1. Big Data in Education
Rachel Hogue

2. Overview Big Data and Education Communities
Why Collect Educational Data?
Learning Theories
eLearning
What Data Can We Collect?
Examples of eLearning Companies and their Use of Big Data
Data Analysis
Existing implementations using educational data
Methods that work well for educational data
MOOCdb
Privacy concerns

3. Communities International Educational Data Mining Society
Founded July 2011
EDM workshop in 2005 (at Association for Advancement of Artificial Intelligence)
EDM conference in 2008
Journal of Educational Data Mining (JEDM) since 2009
Society for Learning Analytics Research
First conference: Learning Analytics and Knowledge (LAK) 2011
Journal of Learning Analytics, founded 2012

4. Why Collect Educational Data?

5. Why Collect Educational Data?
Personalize education
Better assessment of learners
Multiple dimensions: social, cognitive, emotional, meta- cognitive
Multiple levels: individual, group, institutional levels
To promote new scientific discoveries and to advance learning sciences
Many theories; little hard data to support them
Opportunity to discover new learning patterns

6. Why Collect Educational Data?
“Not only can you look at unique learning trajectories of individuals, but the sophistication of the models of learning goes up enormously.”
Arthur Graesser, Editor,
Journal of Educational Psychology

7. A Look Backwards
Collecting educational data was highly resource-intensive and difficult to scale
Much of the data that was easily collectible was purely summative in nature
Getting data on learning processes and learner behaviors, in field settings, required methods like
Quantitative field observations
Video recordings
Think-Aloud studies
None of which scale easily

8. Learning Types

9. Learning Types
Visual (spatial)
Auditory
Kinesthetic / haptic

10. Learning Theories Problem-Based Learning Anchored Instruction
Cognitive Apprenticeship
Situated Learning

11. eLearning

12. eLearning WBI – Web Based Instruction Learning technology
Networking and computing technologies are used to improve educational practices

13. Massive Online Open Course
eLearning
WBI – Web Based Instruction
Learning technology
Networking and computing technologies are used to improve educational practices
MOOC
Massive Online Open Course

14. eLearning

15. What Data Can We Collect?

16. What Data Can We Collect?
Administrative data - who are you?
Address, name, birth date
Content data – inferred properties about material
Difficulty, subject
Longitudinal data - data from a long period of time
Grades
Standardized testing results
Time on task
Attendance
Click patterns
How long a student holds a mouse pointer over a particular answer

17. What Data is Available Already?
PSLC DataShop
a central repository to secure and store research data
a set of analysis and reporting tools
>250,000 hours of students using educational software
>30 million student actions, responses & annotations
Actions: entering an equation, manipulating a vector, typing a phrase, requesting help
Responses: error feedback, strategic hints
Annotations: correctness, time, skill/concept

18. Online Education Formats
Video
Online modules
Written documents
Audio files
Instructions for activity or task

19. CourseSmart Embeds technology directly into digital textbooks
Provides an “engagement index score”, which measures how much students are interacting with their eTextbooks (viewing pages, highlighting, writing notes, etc.).
Researchers have found that that the engagement index score helps instructors to accurately predict student outcomes more than traditional measurement methods, such as class participation.

20. duoLingo
Site and smartphone app to help people learn foreign languages
Luis von Ahn
Professor at Carnegie Mellon
CAPTCHA and reCAPTCHA
“twofer”

21. Data from duoLingo
How long does it take someone to become proficient in a certain aspect of a language?
How much practice is optimal?
What is the consequence of missing a few days?
There are theories about learning languages, such as the idea that adjectives should be taught before adverbs, but previously, there was little hard data to support these theories

22. Conclusions from duoLingo Data
The best way to teach a language depends on the students’ native tongue and the language they’re trying to acquire
Example: Spanish -> English
“it” tends to confuse and create anxiety for Spanish speakers, since the word doesn’t easily translate into their language
Women do better at sports terms
Men do better at cooking and food terms
In Italy, women as a group learn English better than men

23. Learning Analytics Implementations
Still very few
Knewton :
Signals project at Purdue University: academic-analytics
Ellucian Degree Works, “a comprehensive academic advising, transfer articulation, and degree audit solution that aligns students, advisors, and institutions to a common goal: helping students graduate on time.”
Blackboard Analytics - x
Signals – predicts which students are falling behind. As early as 2 weeks, based on student success algorithm. Sends , positive messages and suggestions on dashboard

24. Analysis Methods
Prediction
Structure Discovery
Relationship Mining

25. Prediction
Develop a model which can infer a single aspect of the data (predicted variable) from some combination of other aspects of the data (predictor variables)
Which students are off-task?
Which students will fail the class?

26. Structure Discovery
Find structure and patterns in the data that emerge “naturally”
No specific target or predictor variable

27. Relationship Mining
Discover relationships between variables in a data set with many variables
Correlation or causation

28. MOOCdb Collaborative, online learning research Una-May O’Reilly
Ph.D., Principal Research Scientist MIT CSAIL
Leader of ALFA (Any Scale Learning for All)

29. Different Formats of Data
SQL Dump
Student state information
XML files
Course information
s and Surveys
JSON lines
Clickstream data
EdX Platform

30. Multiple Platforms and Data Control
EdX and Coursera
Controlled by MIT and Stanford, separate entities

31. Data model to organize raw data streams
Unifies different platforms

32. MOOCdb Each class: Student Information Tables Observations Tables
Submissions Tables
Collaboration Tables
Feedback Tables

33. Benefits of MOOCdb Public, shared data model; avoid redundant work
Foster analytic consistency
Engage more people

34. MOOCviz

35. MOOCviz
Resource use compared by country

36. Privacy Concerns
Hardcopy records were phased out in favor of district-based hard drive storage some time ago, but the advent of cloud computing has seen a trend toward the creation of third- party data silos (or clouds).
Teachers and parents are concerned about privacy breaches by hackers and marketers
InBloom
Gates-funded nonprofit that houses student data in the cloud
Closed its doors after parental protest

37. Privacy Concerns
This past May, the Obama administration released an 85-page report on big data and its use in the US among consumers and businesses
"Big data and other technological innovations, including new online course platforms that provide students real time feedback, promise to transform education by personalizing learning. At the same time, the federal government must ensure educational data linked to individual students gathered in school is used for educational purposes, and protect students against their data being shared or used inappropriately."

38. History of Educational Big Data Policies
1974: The Family Educational Rights and Privacy Act of 1974 (FERPA)
2000: The Children's Online Privacy Protection Act of 1998 (COPPA)
2005: New initiative granting money to states that implement Statewide Longitudinal Data Systems (SLDS)
2008: FERPA law is expanded: contracted vendors and school volunteers now have access to the data, with or without parental input
2011: FERPA law is amended once again, granting "authorized representatives" of state authorities access to student data
2011: The Shared Learning Collaborative (SLC) —​ which will later become inBloom —​ is created

39. Questions or Comments?
me at with any questions.