Keynote address delivered September, 2010, to the LACLO 2010 conference in Sao Paulo, Brazil.

1. Openness and Analytics: The Future of Learning Objects David Wiley, PhD Instructional Psychology & Technology Brigham Young University

2. “Curricular units can be made smaller and combined, like standardized Meccano parts, into a great variety ofparticular programscustom-made foreach learner.”Gerard (1969)

3. Wayne Hodgins(1994) “Learning Objects”

4. “Learning objects are like Legos” Wayne Hodgins

5. The Big Question We must all ask ourselves…

6. Why???

7. They’re cool!To help students learn better!To save money!That’s what funders want!

8. The Answer Changes our work significantly

9. My Own Answer Let me tell you a few stories…

10. Once upon a time (1996)

11. There was a JS calculator

13. Something Had Changed Something deeply important…

14. Expertise Is Nonrivalrous Can be given without being given away

15. “He who receives ideas from me, receives instruction himself without lessening mine; as he who lights his taper at mine receives light without darkening me.” Thomas Jefferson

16. External Expressions Aren’t To give a book you must give it away

19. Digital Expressions Are Magical They are nonrivalrous expressions

20. E.g., Online Book We can all read simultaneously

21. An Indescribable Advance The first time in human history

22. Expertise and Its Expressions Give without giving away

23. Unprecedented Capacity The Two Paths

24. The First Path Create once, then sell infinite perfect copies at $0 cost!

25. The Second Path Create once, then give away infinite perfect copies at $0 cost!

26. My Initial Interest Was Financial Learning objects could give us Increased affordability and access

27. 40 / 15 years Several years have passed,and where are we?

28. Approx 300 journal articles Literature review http://www.citeulike.org/user/opencontent

29. Conclusion? This research is a mess

30. Computer Science “Invasion”Educator “Evacuation”

32. Technical Standards SoupIMS, SCORM, IEEE, LOM, DC, ARIADNE, Etc.

33. Ontology Proliferation Multiple definitions and approaches to design and function

34. “If only everyone would do itmyway...” Has a certain arrogance

35. What if, instead… We asked what everyone does?

36. Repositories Google

37. IEEE LOM Tagging

38. OAI-PMH RSS / Atom

39. IMS-CP / SCORM HTML / Zip / etc.

40. Ontologies Folksonomies

41. LMSs Wikis / Blogs / Social Networks

42. Learning Objects Resources

43. What’s different?

44. Complex Simple

45. Little use Massive use

46. “Simple Wins”

51. We can spend our energy

52. Trying to change behavior

53. Trying to leverage behavior

54. The Terrible Questions With apologies

55. What is a learning object?

56. “Any digital resource that can be reused to facilitate learning” Wiley (2000)

57. Four components

58. “Resource”

59. “Digital”

60. “Learning”

61. “Reuse”

62. Lots of (1) digital (2) resources for (3) learning

63. Very little (4) reuse

64. Why is reuse so hard?

65. Two reasons

66. First

67. Context suggests meaning

68. What is context?

69. Things next to each other In space or time

70. http://www.quest-online.com/NewFiles/images/barak-obama.jpg

71. http://www.sonofthesouth.net/slavery/abraham-lincoln/pictures/abraham-lincoln-625.jpg

72. http://www.britannica.com/blogs/wp-content/uploads/2007/10/bin-laden.jpg

73. http://digitalheadbutt.files.wordpress.com/2006/11/michael-jordan.jpg http://hunternuttall.com/blog/wp-content/uploads/2008/07/tiger-woods.jpg

74. More things together=More complex meaning

75. More complex meanings=More “fit” problems

78. What big should a learning object be?

79. What does “size” mean?

80. Size Amount of context inside the learning object

81. The Reusability Paradox

82. Teaches Effectively Easy to Reuse

84. Hard to reuse (don’t “fit”)

85. SO…

87. Don’t teach well

88. So…

90. Scope and Sequence Instructional Design

92. Flickr / net_efekt

93. What if you had this…

95. But needed this?

97. Or this?

99. Just Adapt It, Right?

101. Complete (c) Assumed Ambient, Ubiquitous Copyright

102. Reuse => Exactly As is When does “as is” suffice?

103. Not Very Often We always want to tinker

104. Adaptability is Critical But missing from our normal conception of learning objects

105. Copyright Permissions Are… Too hard to get Too expensive to get

106. So We Give Up Either we choose a different LO, orwe break the law, orwe do a poor job

107. Reuse is hard because:(1) context, reusability paradox(2) copyright issues

108. What do we do?

109. Learning Objects + Open Source LicenseOpen Educational Resources

110. What is an Open License?

111. Grants4 Rs Permissions Reuse - verbatim copies Redistribute - share copies Revise - make adaptations Remix - combinations / mashups

112. Creative Commons

113. Redistributable Remixable 4Rs Revisable Reusable Reusable Accessible Accessible RAID RAID Interoperable Interoperable Durable Durable LO OER

114. OER = Learning Objects 2.0

116. Legal to revise / adapt

117. So…

119. (Not context-free repositories)

121. Is “Open” Financial? Some thoughts about OER and improving learning

122. Legally Open vs Technically Open Can OER be adpated?

123. ALMS Analysis Framework Access to editing tools?Level of expertise required?Meaningfully editable?Self-sourced?

124. Access to Editing Tools?Are editing tools practically available (platform / cost?)

125. Access to Editing Tools?Example: html filesNon: MS OneNote files

126. Level of Expertise?Can a file be adapted withlittle / no training?

127. Level of Expertise?Example: Word or odt filesNon: 3d models, flash quizzes

128. Meaningfully Editable?Can the content of the file even be changed?

129. Meaningfully Editable?Example: Word or html fileNon: Scanned pdf of notes

130. Self-Sourced?Is the file preferred for editing also the file preferred for using?

131. Self-Sourced?Example: html fileNon: Flash fla / swf files

132. Open... but not

136. Less Expensive- Reuse - verbatim- Redistribute - share- Revise - derivatives- Remix - combinations

137. More Expensive- Reuse - verbatim- Redistribute - share- Revise - derivatives- Remix - combinations

138. How Can Open Improve Learning? An example

139. http://openhighschool.org/

140. Bloom’s 2 Sigma Challenge Bloom, 1984

141. One-to-One Tutoring And other methods compared to 30 students in the classroom

142. Average Tutored Student by 2 SD In other words, the average student is capable of much more

143. Tutoring is Expensive So we teach class instead!

144. If the research on the 2 sigma problem yields practiced methods (methods that the average teacheror school faculty can learn in a brief periodof time and use withlittle more cost or timethan conventional instruction), it would be an educationalcontribution of the greatest magnitude. (p. 5) Bloom, 1984

145. To Tutor Or Not to Tutor? That is the (false) question

146. “Strategic Tutoring” What if we could do one-on-one tutoring just-in-time and just-on-topic?

147. Requires Great Insight We’d have to know who needs help, when, and what they need help with

148. Data Are the Key You’d need live, fine-grained data about students, assessment, and curriculum

149. Good Data Are Expensive! Who gathers and analyzes data like this?

154. Each and Every Interaction Recorded and stored for analysisto improve quality of service / experience

156. Even the Grocer! Almost every industry uses data more effectively than education

157. How Can We Get the Data? One option is to follow Google, Amazon, Netflix, WoW, etc.

158. Do School Online Where each and every interaction can be captured and stored for analysis

159. What Kind of Data? When they logged in, read, and worked How long they logged in, read, and worked Pathway information, Item-by-item analytics &c.

160. OHSU Teaching Model OER teach as much as possible,teachers do proactive “strategic tutoring”

161. Another Benefit of Data Engage in continuous improvement of curriculum materials

162. Can You Improve Curriculum? Data aren’t sufficient – you need permission

163. OHSU Charter Requires OER Founders’ way of “burning the ships”

164. Conjoint Continuous Improvement Student learning and curriculum effectiveness grow simultaneously

165. FeedbackLoop Curriculum Redesign Data Describing Curriculum Performance Data Supporting Strategic Tutoring Curriculum Use Student Performance Data

166. Visualizing Educational Data Creating new visualization techniques to support teaching and learning

168. Some Conclusions Finally!

169. Difficulties with Learning Objects LOs have yet to fulfill their potential, because of copyright and technical problems

170. Open Licenses Solve Problems When “reuse” can also mean “revise” and “remix,” LOs can more fully realize their potential

171. OER = Learning Objects 2.0 When “reuse” can also mean “revise” and “remix,” LOs can realize their potential

172. OER => Google Searchability Google, Bing, and others support specialized OER search

173. OER = Lower Cost Freely available

174. OER => Increased Learning Continuous quality improvement

175. Thank You david.wiley@byu.edu http://davidwiley.org/ http://slideshare.net/opencontent/

176. Utah Open Textbooks Project Replacing expensive textbooks with remixed OER textbooks from CK12.org Funded by the Hewlett Foundation

177. 2010-2011 1800 students in Earth Systems, Biology, and Chemistry

178. Traditional Context $100 textbooks replaced every 7 years

179. < $7 per book And remixed specifically to students’ needs

180. Textbook as Consumable Students can highlight, annotate,and keep their textbooks

181. At Least 50% Cost Savings We believe levels of learning will be constant or improve slightly

182. Huge Policy Implications If we can increase learning and decrease costs, then what?