OER Survival of the fittest
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The slides from my defense of my master thesis where I applied artificial intelligence techniques to automatic selection of sequences of Open Educational Resources based on the measured impact they have on learning. I used Genetic Algorithms and UCB-1 selection, it was evaluated in the setting of an online course. Contact me if you want to read the thesis. The defense was successful.
![Open Educational Resources [1]
Learning objects that can freely be
reused, revised, remixed and redistributed.
[1] Daniel E Atkins, John S Brown and Allen L Hammond. Creative Common, 2007. A review of the
open educational resources (OER) movement: Achievements, challenges, and new opportunities.](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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OER Survival of the fittest
- 1. Open Survival of the fittest Open in the jungle of Open Educational Resources 15th of August 2014 a Master Thesis by Sander Latour
- 2. Open Educational Resources [1] Learning objects that can freely be reused, revised, remixed and redistributed. [1] Daniel E Atkins, John S Brown and Allen L Hammond. Creative Common, 2007. A review of the open educational resources (OER) movement: Achievements, challenges, and new opportunities.
- 3. Open Educational Resources Learning objects that can freely be reused, revised, remixed and redistributed. Textual objects Video objects Interactive objects You should not focus on every detail. Stick to the bigger picture Example: You are reading this. Stick to the bigger picture
- 4. OER Sequence
- 8. ( )NLGE
- 10. NLG2( ( NLG3( ( NLG4( ( NLG5( ( NLG6( ( NLG7( ( NLG8( ( NLG1( ( ( )NLGE
- 11. NLG2( ( NLG3( ( NLG4( ( NLG5( ( NLG6( ( NLG7( ( NLG8( ( NLG1( ( ( )NLGE
- 12. NLG2( ( NLG3( ( NLG4( ( NLG5( ( NLG6( ( NLG7( ( NLG8( ( NLG1( ( ( )NLGE I regret trying this
- 13. Exploration Exploitation regretminimize online “while learning” ( )NLGE
- 14. Survival of the fittest UCB + a Genetic Algorithm [2] A.E. Eiben and J.E. Smith. Natural Computing, 2007. Introduction to Evolutionary Computing. [2]
- 15. ( ) 2 ln(n) n NLG average total nr. of evaluations nr. of times tried UCB-1[3] [3] P. Auer, N. Cesa-Bianchi and P. Fischer. Machine learning, 2002. Finite-time analysis of the multiarmed bandit problem.
- 16. The impacts of these sequences are not independent If these two are effective then it makes sense to try this
- 17. Genetic Algorithms Population containing a subset of candidates Candidates have a “fitness” value, i.e. how good is it? Higher fitness means higher chance of reproduction Produced offspring is a combination of both parents Inspired by Darwinian evolution
- 18. ( ) 2 ln(n) n NLG Current population T1 T2 Selecting most promising sequence Evaluation of impact
- 19. NLG1 NLG3 NLG2 NLG4 NLG5 Roulette selection of parents 1 2 3 4 Crossover & Mutation Crossover & Mutation Offspring Offspring Generational replacement with elite preservation elite offspring Current generation Next generation
- 20. Genes chromosome Permutation encoding … with varying length … with partial permutations One-point crossover Append crossover Swap mutation Addition mutation Deletion mutation
- 21. Evaluation & Results Experiment with online “course”
- 22. Nim game Curriculum Low High student groups 4lessons 4OER T1 T2 7sequences in 1 generation 10evaluations in 1 generation 2elite members 5%mutation 237total usable participants Algorithm Participants voluntary participation could stop at any moment diverse crowd not just students 3MC 3MC
- 23. Does the system learn to pick sequences with more learning impact over those with less impact? Figure: Regret in Rules - Low Figure: Regret in Intuition - Low Built-up regret students The system worked for the “Low” student groups In “High” groups there was either too little data or a technical issue It’s unknown how good the apparently best sequences really are
- 24. Possible explanation limited pre- and post-test coarse division of students independence assumption Variance in the observed learning impact learning impact students Figure: Best sequence in “Rules” lesson for student group “Low”
- 25. In conclusion, A possible approach for using learning impact in the assessment of OER has been presented and tested. Many lessons can be drawn from the results, but the principle works. I recommend others to continue on the path of using learning impact in the assessment of OER.
- 26. Invited for drinks at:
- 27. Diversity of individuals in each population Diversity was low with few novelties Parameters were set to converge more quickly Exploration was often not possible by means of crossover