1. Carving up the Space of End User Programming EUSES, Lincoln, NE, Oct ‘05

2. Agenda Overview of our conceptual “space” (The projects below are aimed at refining this space.) Past projects: –Re-analysis of 55M data –Survey of Information Week readers Future projects: –Interviews of Katrina-related web & db developers –Contextual inquiry of specific populations

3. Purpose of our “Conceptual Space” Our goal is to understand the population of end users (EUs) who program. –What are EUs’ strengths and weaknesses? –What sorts of programming are they doing? –How many EUs are doing each type of programming? –Where can we invest our time to achieve significant benefits? Answering these goes hand-in-hand with mapping out our “conceptual space.”

4. Where are the strengths of EUs? Task Structure Means of Programming Activity Type

5. 55M / 90M estimate & task structure Updated 55M estimate –Old: 55M EU programmers in 2005 –New: 90M EU in 2012 –New: incl. 55M spreadsheet and/or db users Received insight into most common tasks –Most common occupations for EUs: Manager, teacher, secretary, accountant Results reported in C. Scaffidi, M. Shaw, and B. Myers. Estimating the Numbers of End Users and End User Programmers. Proceedings of VL/HCC, 2005.

6. More focused “task structure” dimension Means of Programming Activity Type Task Structure - Accountant- Teacher - Manager- (Others) - Secretary

7. Survey of feature usage 2005 survey of Information Week readers –Ask about usage of application features –Focus on abstraction-related features (E.g.: JavaScript, web server scripting, databases, macros, and spreadsheet features) Propensities to use features fell cleanly into three clusters –Macros, Linked Structures, Imperative Code Results to be reported in C. Scaffidi, A. Ko, B. Myers, M. Shaw. Identifying Types of End Users: Hints from an Informal Survey, Technical Report CMU-ISRI-05-110/CMU-HCII-05-101, Institute for Software Research International, Carnegie Mellon University, Pittsburgh, PA, 2005.

8. More focused “means” dimension Task Structure - Accountant- Teacher - Manager- (Others) - Secretary Means of Programming - Macros - Linked Structures - Imperative Code - (Others) Activity Type

9. Moving along to future projects… Two past projects refined our “space” –Re-analysis of government data helped refine “task” dimension –Information Week survey helped refine “means” dimension What about the “activity” dimension? –Katrina-related “person locator” study –Contextual inquiry of three populations

10. Study of Katrina-db creators Fall 2005 telephone interviews How do different EUs handle one need? –Need: “person locator” site –Solution: wide and varied, depending on EU (Some are even syntheses of existing web databases.) –How did they decide what to build? –Why did they decide to build in the first place? –What types of activities were difficult? –How did they overcome these difficulties?

11. Cross-cut of web and db “means” Task Structure: - Accountant- Teacher - Manager- (Others) - Secretary Means of Programming - Macros - Linked Structures - Imperative Code - (Others) Activity Type (e.g.: knowledge, comprehension, application, analysis, synthesis, evaluation)

12. Study of data interoperability problems Fall 2005 contextual inquiry How do different EUs cope with problems? –Focus: data interoperability between apps –Population: Administrative assistants / secretaries Managers (emphasis on marketing managers) Graphic designers (intended as a half-step toward professional programmers) –Hopefully we will gain insight into how Linked Structure features assist or confound EUs. Study inspired by article “Science fiction?” in The Economist, Sep 2005.

13. Cross-cut of linked structure “means” Task Structure: - Accountant- Teacher - Manager- (Others) - Secretary Means of Programming - Macros - Linked Structures - Imperative Code - (Others) Activity Type (e.g.: knowledge, comprehension, application, analysis, synthesis, evaluation)

14. Summary Past Work –Extending the EU count estimate –Scoping out most common EU occupations (“task” dimension) –Exploring propensities to use abstractions (“means” dimension) Future Work (“activity” dimension) –Seeing how various EUs respond to one need –Scoping out data interoperability problems

15. Thank You To the EUSES community for your interest and feedback To NSF, Sloan, and NASA for funding

16. References 55M/90M estimates: C. Scaffidi, M. Shaw, and B. Myers. Estimating the Numbers of End Users and End User Programmers. Proceedings of VL/HCC, 2005. Feature clustering: C. Scaffidi, A. Ko, B. Myers, M. Shaw. Identifying Types of End Users: Hints from an Informal Survey, Technical Report CMU-ISRI-05-110/CMU-HCII-05- 101, Institute for Software Research International, Carnegie Mellon University, Pittsburgh, PA, 2005. Inspiration for interoperability study: “Science fiction?” in The Economist, Sep 2005. Bloom’s taxonomy: B. Bloom, B. Mesia, and D. Krathwohl. Taxonomy of Educational Objectives. David McKay Publishers, New York, NY, 1964. Green and Blackwell’s activity type categories: A. Blackwell and T. Green. Cognitive Dimensions of Notations Tutorial at VL/HCC, 2005.