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Social networks generate interest in computer science
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Source Technical Symposium on Computer Science Education archive
Proceedings of the 37th SIGCSE technical symposium on Computer science education table of contents
Houston, Texas, USA
SESSION: Recruitment and retention table of contents
Pages: 438 - 442  
Year of Publication: 2006
ISBN:1-59593-259-3
Also published in ...
Authors
Casey Alt  Duke University, Durham, NC
Owen Astrachan  Duke University, Durham, NC
Jeffrey Forbes  Duke University, Durham, NC
Richard Lucic  Duke University, Durham, NC
Susan Rodger  Duke University, Durham, NC
Sponsors
SIGCSE: ACM Special Interest Group on Computer Science Education
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 13,   Downloads (12 Months): 114,   Citation Count: 9
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ABSTRACT

For forty years programming has been the foundation of introductory computer science. Despite exponential increases in computational power during this period, examples used in introductory courses have remained largely unchanged. The incredible growth in statistics courses at all levels, in contrast with the decline of students taking computer science courses, points to the potential for introducing computer science at many levels without emphasizing the process of programming: leverage the expertise and role-models provided by high school mathematics teachers by studying topics that arise from social networks and modeling to introduce computer science as an alternative to the traditional programming approach. This new approach may capture the interest of a broad population of students, crossing gender boundaries. We are developing modules that we hope will capture student interest and provide a compelling yet intellectually rich area of study. We plan to incorporate these modules into existing courses in math, statistics, and computer science at a wide variety of schools at all levels.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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CITED BY  9

Collaborative Colleagues:
Casey Alt: colleagues
Owen Astrachan: colleagues
Jeffrey Forbes: colleagues
Richard Lucic: colleagues
Susan Rodger: colleagues