putting the world to work for its n.
Skip this Video
Loading SlideShow in 5 Seconds..
Putting the world to work for ITS: PowerPoint Presentation
Download Presentation
Putting the world to work for ITS:

Loading in 2 Seconds...

play fullscreen
1 / 33

Putting the world to work for ITS: - PowerPoint PPT Presentation

Download Presentation
Putting the world to work for ITS:
An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. ITS 2008 Open community authoring of targeted worked example problems Putting the world to work for ITS: Aleahmad, Aleven and Kraut

  2. Current situation in tutoring systems • Development is very laborious • (e.g. estimates of 200-300 hrs for 1 hr instruction) • Small groups with much effort per person • Distribute the development • Open source • Open content • How to make a “Wikipedia” for ITS?

  3. Wikipedia not the right model

  4. Towards a collaborative community

  5. Broad research questions • If you make it, will they come? • Can the wheat be separated from the chaff? • How to structure and support authoring? • For quality • For diversity to engage students • Contextualization, personalization, and provision of choices can improve student motivation and engagement in learning (Cordova and Lepper, 1996 ) • Personalization improves performance gains and even at start (Anand and Ross, 1987; Ku and Sullivan 2002; López and Sullivan 1992)

  6. Overview of the study • Web site where people contribute worked example problems • In registering, indicated their professional status • Tested a mechanism to increase quality and diversity • Asked some authors to target to a specific person • Increase their effort? • Increase diversity/adaptivity of corpus?

  7. Task • Artifact: Worked example problem • Leads to better and more efficient learning when added to interactive tutoring (McLaren et al., 2006; Schwonke et al., 2007) • Instruct and foster self-explanation (Renkl and Atkinson, 2002) • Customizability – both to the student and the interaction • Domain: Pythagorean Theorem • Most difficult skill on the Massachusetts Comprehensive Assessment System curriculum standards (ASSISTment data) • Diagram drawing difficult to computer generate

  8. Zack and Slater want to build a bike jump. They have two parts of the ramp constructed but they need to know the length of the final piece of the jump. They have two parts of the ramp built, one is 3 ft long and the other is 4 ft long and they are constructed as shown in the diagram. What is the length of the missing section that Zack and Slater still need to construct? Problem Statement + Work Explanation Solution steps The unknown is the hypotenus which is represented by c in the equation. Therefore I input both a and b into the equation first. Following the equation I square both of these numbers. 3^2 + 4^2 = These two numbers are added together first because of the parenthesis. 9 + 16 = 25 Whole worked example To complete the equation I take the square root of 25 which is five. This problem also demonstrates the common Pythagoras triangle. Square root of 25 is 5 and this is the solution.

  9. Authoring tool

  10. Open authoring hypotheses • H1: Identifying the good from the bad contributions is easy. We expect that all contributions are good, easily fixed, or easily filtered. • H2: Math teachers submit the best contributions.

  11. Student profiles • Goal of realism • Varied on social and cognitive attributes • 16 profiles • 4 Hobbies x 4 Homes • 4 realistic skill profiles distributed • 2 genders distributed

  12. Profile hypotheses Profiles in experimental condition versus generic control condition • H3: Student profiles lead to tailored contributions. • H4: Student profiles increase the effort of authors. • H5: Student profiles lead to higher quality contributions.

  13. Participants and contributions • Participation URL posted on web sites (educational and otherwise) offering $4-12 • 1427 people registered, of which 570 used the tool to submit 1130 contributions • After machine filtering, 281 participants were left having submitted 551 contributions

  14. Machine filtered Some have just a worthless drawing. Or nothing at all.

  15. Quality ratings Human experts rated the machine vetted submissions

  16. Quality rating examples • Excellent statement with poor solution (1124) • Worthy statement with excellent solution (337)

  17. Open authoring

  18. Quality of pool

  19. Quality by contributor expertise Statement quality Solution quality

  20. Student profiles

  21. Tailoring to social attributes Probabilities of authoring matching an attribute †p<.10 *p<.05 **p<.001

  22. For profile with a home outside town

  23. For profile who lives in tall apartment building

  24. Tailoring to cognitive attributes Verbal skill in profile General math skill in profile Correspondence of verbal and math skill levels with the authoring interface

  25. Shakespeare for profile in “top of English class”

  26. Effects of profiles On effort On quality • Problem statements in profile condition were 25% longer • No significant difference in time spent (median 5 each minutes on statement and solution) • No main effect of profiles on quality • No interaction with teacher status either

  27. Conclusions

  28. Recap of Hypotheses

  29. Current and future work

  30. Current and future work

  31. Current and future work

  32. Acknowledgements • Thanks to ASSISTment project, Ken Koedinger and Sara Kiesler for data and feedback • Work supported by IES and NSF • It’s going to take a lot of connected work to build a scalable shared ITS for the world • Let’s talk more about how • http://OpenEducationResearch.org

  33. Gratis participants • Still 93 submissions from 92 participants • Of these 38 submissions from 21 participants pass machine vetting • 41% pass rate of machine vetting compared to 49% rate in experiment • Not significantly different by Fisher's Exact Test (p=0.16)