Xiao Hu "Overview of the Space of Learning Analytics and Educational Data Mining"


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Xiao Hu "Overview of the Space of Learning Analytics and Educational Data Mining"

  1. 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. 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. 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. 4. What's Going on in Learning Analytics • Research methods are becoming finely turned • Growing impact on practice • Increased Computer Science presence
  5. 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. 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. 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. 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. 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. 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. 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. 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. 13. Lightening Talks by Doctoral Student Attendees
  14. 14. Questions for Clarifications? •Discussions will be after three presentations!