1. Overview of the Space of
Learning Analytics and
Educational Data Mining
George Siemens (University Of Texas, Arlington And
SoLAR) & Ryan Baker (Columbia University)
Presented by Xiao Hu @ University of Hong Kong

2. Scholarly Communication
Venues Provided by SoLAR
• LAK, JLA, LASI, LAMP
• The mailing-lists
• LA Google group:
https://groups.google.com/forum/#!forum
/learninganalytics
• EDM-Discuss
http://www.educationaldatamining.org/IE
DMS/mailinglists

3. Types of EDM Methods
• Prediction
• Structure Discovery
• Relationship Mining
• Distillation of Data for Human Judgment
• Discovery with Models
• Bring SoL and analytic geeks together

4. What's Going on in Learning
Analytics
• Research methods are becoming finely
turned
• Growing impact on practice
• Increased Computer Science presence

5. What's Going on in EDM
• Lots of classification and regression
• Increased emphasis on latent
knowledge estimation and knowledge
structure discovery
•figure out what students doing
• Reduced emphasis on relationship
mining (association rules)

6. What's Going on in EDM
(Con’d)
• Constructs studied are broadening
• Meta-cognition
• Affect
• Engagement
• Motivation
• Long-term participation (cross-years)
• Increased number of studies with models
analysis, particularly on more generalized
models

7. What's going on in EDM
(Con’d)
• Basic research
• Automated intervention
• Getting into "reporting“
•understand the meaning of the model,
tell people what we found
• Increased participation of industry

8. Going Forward…
• New tools, techniques and people (disciplines)
• Data: openness, ethics and scope
• Target of analytics activity
• Connections to related fields and practitioners
(collaborations, LAMP)
• Challenges: creating strategic links to related
communities

9. Coming Up
• A MOOC on EdX "Data, Analytics and
Learning", Starting Oct. 20th, 2014
• LAK2015: in NY (deadline: Dec. 24th)
• ICEDM2015 in Madrid (deadline: Dec. 24th)

10. Q1: Friction between Computer
Scientists and Learning Analytics ?
• LAK vs EDM papers?
•EDM don't use huge datasets
• Around what is knowledge, fairness,
equality
• Will see more and more studies using CS
methods, but the attributes of learning
experience are also important

11. Q2: Collaboration with Start-
ups?
• Certainly open to building connections to
start-ups, as there is so much activity going
on
• But such connections are not yet explicit
• Companies are scattered, connections are
much appreciated!

12. Q3: Research Ethics : Consent
form, Privacy etc. ?
• Do students have to give consent?
• Not clear, no legal architecture in this area
• Privacy: does it apply to MOOCs?
• As a community, we need to educate the IRB
about the nature of our research;
• Improve the ways of anonymizing data;
• Educate ourselves about how to manage data
(for long-term access and re-use)

13. Lightening Talks by
Doctoral Student Attendees

14. Questions for Clarifications?
•Discussions will be after three
presentations!