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Discount Expertise Metrics for Augmenting Community Interaction

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Slides presented at the ACM International Conference on Communities and Technologies (C&T '15), Limerick, Ireland, June 27–30, 2015

Expertise identification is important for various kinds of online and offline organizations, with practical applications such as supporting question answering, problem-solving, and team formation. Using developers as the target population, we demonstrate that it is possible to identify novices and experts of programming by examining the types of programming related websites they visit.

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Discount Expertise Metrics for Augmenting Community Interaction

  1. 1. Discount Expertise Metrics for Augmenting Community Interaction Pei-Yao Hung1 , Mark S. Ackerman1, 2 School of Information1 and Dept. of EECS 2 , University of Michigan, USA 1
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  4. 4. 4 He has done almost nothing…
  5. 5. Estimating Expertise • Profile: curation => needs maintenance ! (Farrell, Lau, Nusser, Wilcox, & Muller, 2007) • Artifact: production => needs contribution ! (Nam & Ackerman, 2007) • Interaction: participation => needs contribution ! (Hanrahan, Convertino, & Nelson, 2012; Zhang & Ackerman, 2005; Zhang, Ackerman, Adamic, & Nam, 2007) 5
  6. 6. Estimating Expertise • Profile: curation => needs maintenance ! (Farrell, Lau, Nusser, Wilcox, & Muller, 2007) • Artifact: production => needs contribution ! (Nam & Ackerman, 2007) • Interaction: participation => needs contribution ! (Hanrahan, Convertino, & Nelson, 2012; Zhang & Ackerman, 2005; Zhang, Ackerman, Adamic, & Nam, 2007) 6 You need to do/contribute a lot of work before we can estimate your expertise!
  7. 7. ! A lot of people consume, but do not contribute. 7
  8. 8. Q: Can we use the browsing history to estimate levels of technical expertise? 8 docs.python.org tutorialspoint.com docs.ggplot2.org cyclismo.org/tutorial/R ... github.com stackoverflow.com pypi.python.org ruby-doc.org ...
  9. 9. How do we analyze browsing history? • Intuition: programmers at different levels might visit different type of web pages. • Library/Repository: https://github.com/ • Tutorial: http://www.tutorialspoint.com/python/ • Q&A: http://stackoverflow.com/ • … 9
  10. 10. Q: Can we use the browsing history to estimate levels of technical expertise? 10 Document Tutorial Document Tutorial ... Library/Repository Q&A Library/Repository Document ...
  11. 11. Recruiting Participants • Recruiting: presentations, email lists, and snowball referrals • 26 participants who are ‘actively’ programming • 11 male and 15 female. • 24 students (undergrad ~ Ph.D.) • Diverse majors (e.g., Russian, economics, to computer science) 11
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  13. 13. Rating Scheme for Expertise ! ! ! ! ! ! Level Experience +1 Learning programming for the first time/year +2 Electrical Engineering (EE) training, 1 year professional programming experience, or 3 - 4 years assistant/part- time programming experience +3 Computer Science (CS) training, or 2 - 3 years professional programming experience +4 4+ year professional programming experience +5 6+ year professional programming experience 13
  14. 14. Analysis Programming Relevant Web Page Visits (distribution) Level of Expertise Level 1 ~ 5Tutorial, Library/Repository, Q&A, Document, Blog, Forum, Search, Other Logistic Regression InterviewBrowsing History 14
  15. 15. If everything goes as expected… 15
  16. 16. 16 Tutorial
  17. 17. 17 Library/Repository
  18. 18. Logistic Regression (N=26): the relationship between page visits and expertise isn’t that straight forward. 18
  19. 19. Conservative Classifiers using Heuristics • Beginner (Lv 1 or 2) <- over 80% of programming relevant visits on “Tutorial” • Expert (Lv 4 or 5) <- over 80% of programming relevant visits on “Library/Repository and Q&A” ! ! ! 19
  20. 20. 20 Well, but he is probably an expert pythonist…
  21. 21. How can this discount expertise measure augment community interaction? • “Inclusive” • Provides initial expertise estimation to smooth the process of “blending in” a new community. • Tracks expertise development in a learning community (e.g, MOOC). • Allows ad-hoc network formation. 21
  22. 22. Future Work • Distinguishes expertise development for different programming languages. • Monitors changes of expertise through a longitudinal study (e.g., 6-12 months). ! ! 22
  23. 23. Takeaways • Browsing history could be a source for a discount expertise metric. • Discount expertise metric has the potential to argument community interaction. ! ! 23 Contact: Pei-Yao Hung, peiyaoh@umich.edu

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