Richard Baraniuk Connexions Rice University

1. Personalized Learning via Connexions Richard BaraniukConnexions Rice University

3. learning today inefficient developmentand feedback concept silos poor access to high-quality opportunities

6. nanotubes knowledge forms a network algebra geometry art history proteomics linguistics

7. knowledge forms a network networks enable new means to produce and exploit knowledge

8. open standards for networks of knowledge

10. open education enablers technology web, internet, databases, … intellectual property open-source licenses for content make content easy and safe to share

11. Connexions (cnx.org) • 1200 open textbooks/collections • 20000 Lego modules • from contributors worldwide • in 40+ languages • millions of users per monthfrom 190 countries • 90 million uses of • STEM content

12. free online: 6million uses to date iPad/iPhone/Androidvia ePub $26 in print(627 pages)

15. CNXpersonalized learning

16. openness can disrupt the entire educational enterprise move beyond factory mindset to individualized, personalized education Sir Ken Robinson

17. GOAL: personalize the learning experience

18. educational assessment lack of timely feedback lack of diverse remedial/enrichment materials automated feedback systems expensive and fragile

19. GOAL: personalize the learning experience • exploit global community of authors/teachers/learners (OER/CNX) • replace top-down rules based systems with bottom-up machine learning algorithms • bake in cognitive science best practices

20. Connexions Connexions

21. Connexions+ Quadbaseopen sourceassessments database supporting infrastructure for assessment, interactivity, peer review Connexions interactivesimsLablets Focuspeer review system Videotutorials

23. versions, comments, tags, licenses, roles, publishing

24. solutions

25. projects for collaboration

26. tags!

27. PLS – personalized learning system QuADopen sourceassessments database machine learning algs community Connexions interactivesimsLablets Focuspeer review system Videotutorials

28. instructor view: course

29. instructor view: lesson

30. student view: section

31. student view: assignment

32. student view: working a problem

33. student feedback

34. viewing a student’s work

35. log of student’s activities http://cnx.org/m34921/latest http://cnx.org/m34921/latest

36. cognitive science cog sci principles baked into PLS • retrieval practice • timely and relevant feedback • spacing of practice and feedback cog sci collaborators • Elizabeth Marsh, Andy Butler, Duke U • Henry Roediger, Wash U

37. machine learning graphical models learn and encode relationships among content, questions, answers, potential feedback, … adaptivity optimize each student’s “learning path” through the graph

38. PLS architecture Machine Learning Researchers Duke Cog Sci PLS Backend PLS Quadbase Connexions Lablets Linkify

39. alpha testing Rice U ELEC301 Signals and Systems • homework replacement w/ cog sci (feedback, retrieval practice, repetition, spacing) but no machine learning based adaptivity preliminary findings • using the PLS for homework promoted better retention and transfer of knowledge on an end-of-semester assessment relative to standard practice • magnitude of the benefit was almost equivalent to one letter grade considering completely accurate use of knowledge (no partial credit) and about half of one letter grade considering giving credit for partial knowledge summary • PLS > standard practice • effect size ≈ 1/2 to 1 letter grade

40. summary open architecture for personalized learning Connexions, OpenStax CollegeQuadbase PLS built-in machine learning and cognitive science domain and level agnostic promising alpha test results coming soon: PLS integration with Moodle, Sakai, … collaborations in college, K-12

41. Connexionscnx.org OpenStax College openstaxcollege.org Quadbasequadbase.org PLS pls.ricedsp.org The Personalized Learning System