Mining Twitter to Understand Engineering Students' Experiences

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This is a presentation at ASEE2012, San Antonio, Texas. Mining Twitter data to understand engineering students learning experiences. This presentation contains the qualitative research part. An updated version of this project with large-scale data mining (using classification and detection algorithm to identify potentially at-risk students) is published in this paper. http://web.ics.purdue.edu/~chen654/pub/XinChen_etal_IEEETrans_tlt-cs_Mining_Twitter.pdf

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Mining Twitter to Understand Engineering Students' Experiences

  1. 1. Exploring Engineering Students’ College Experiences Using Social Media Monitoring Tool Radian6 Xin Chen, Mihaela Vorvoreanu Ph.D, Krishna Madhavan Ph.D Purdue University {chen654, mihaela, cm}@purdue.edu
  2. 2. The story begins with:
  3. 3. The story begins with: Social Media Proliferation
  4. 4. The story begins with: Social Media Proliferation
  5. 5. The story begins with: Social Media Proliferation
  6. 6. The story begins with: Social Media Proliferation Data Deluge Image: http://www.psfk.com/2011/01/building-digital-libraries-to-contain-the-data-deluge.html
  7. 7. What insights do we gain from user generated data?
  8. 8. What insights do we gain from user generated data? Brand Monitoring
  9. 9. What insights do we gain from user generated data? Brand Monitoring
  10. 10. What insights do we gain from user generated data? Brand Monitoring
  11. 11. What insights do we gain from user generated data? Brand Monitoring
  12. 12. What insights do we gain from user generated data? Brand Monitoring
  13. 13. What insights do we gain from user generated data? Event Monitoring Brand Monitoring
  14. 14. What insights do we gain from user generated data? Event Monitoring Brand Monitoring
  15. 15. What insights do we gain from user generated data? Event Monitoring Brand Monitoring Disaster Responses
  16. 16. What insights do we gain from user generated data? Disaster Responses Event Monitoring Brand Monitoring
  17. 17. What insights do we gain from user generated data? Disaster Responses Event Monitoring Brand Monitoring Network Analysis
  18. 18. How does higher education use social media data?
  19. 19. How does higher education use social media data? The State of Web and Social Media Analytics in Higher Ed, Survey by Higher Ed Experts, July 2011
  20. 20. How does higher education use social media data? The State of Web and Social Media Analytics in Higher Ed, Survey by Higher Ed Experts, July 2011
  21. 21. How does higher education use social media data? The State of Web and Social Media Analytics in Higher Ed, Survey by Higher Ed Experts, July 2011
  22. 22. How does higher education use social media data? Mostly Number Counting, No Content Analysis The State of Web and Social Media Analytics in Higher Ed, Survey by Higher Ed Experts, July 2011
  23. 23. How does higher education use social media data? The State of Web and Social Media Analytics in Higher Ed, Survey by Higher Ed Experts, July 2011
  24. 24. How does higher education use social media data? The State of Web and Social Media Analytics in Higher Ed, Survey by Higher Ed Experts, July 2011
  25. 25. How does higher education use social media data? Mostly for Marketing, Not Directly Related to Current Students The State of Web and Social Media Analytics in Higher Ed, Survey by Higher Ed Experts, July 2011
  26. 26. Methods Strategy Collect web content relevant to engineering students to understand their college experiences
  27. 27. Methods Strategy Collect web content relevant to engineering students to understand their college experiences Challenge Relevant vocabulary is undefined; time span is undefined.
  28. 28. Methods
  29. 29. Methods Iterative process of defining keywords combination for data retrieval using Radian6
  30. 30. Methods Iterative process of defining keywords combination for data retrieval using Radian6 Oct. 31st -- Nov. 30th, 2011 Keywords combination: 116 tweets #engineeringProblems: 667 tweets
  31. 31. Methods Iterative process of defining keywords combination for data retrieval using Radian6 Oct. 31st -- Nov. 30th, 2011 Keywords combination: 116 tweets #engineeringProblems: 667 tweets Two stages of content analysis
  32. 32. Results 1 Sacrifice and Negative Feelings 2 Issues with Classes, Professors, Homework, and Exams 3 Gender and Other Minorities 4 Engineer Stereotypes and Identity Formation
  33. 33. The results: 4 major themes 1 Sacrifice and Negative Feelings
  34. 34. The results: 4 major themes 1 Sacrifice and Negative Feelings “Which one of does not belong: coloring eggs, hunting for eggs, new dog, time with my mommy, Easter feast, studying?”
  35. 35. The results: 4 major themes 1 Sacrifice and Negative Feelings “Which one of does not belong: coloring eggs, hunting for eggs, new dog, time with my mommy, Easter feast, studying?” “I’ve got 482 followers! That’s 480 more friends than I have in real life! Thanks for being there for me mom and dad!”
  36. 36. The results: 4 major themes 1 Sacrifice and Negative Feelings “Which one of does not belong: coloring eggs, hunting for eggs, new dog, time with my mommy, Easter feast, studying?” “I’ve got 482 followers! That’s 480 more friends than I have in real life! Thanks for being there for me mom and dad!” “Is it bad that before I started studying for my tests today that I considered throwing myself in front of a moving car??”
  37. 37. The results: 4 major themes 2 Issues with Classes, Professors, Homework, and Exams
  38. 38. The results: 4 major themes 2 Issues with Classes, Professors, Homework, and Exams “Learning more from a hick on YouTube than from my professor.”
  39. 39. The results: 4 major themes 2 Issues with Classes, Professors, Homework, and Exams “Learning more from a hick on YouTube than from my professor.” “I’ve never been so happy to make a 50 on a test! Beat the class average by over 20 points.”
  40. 40. The results: 4 major themes 2 Issues with Classes, Professors, Homework, and Exams “Learning more from a hick on YouTube than from my professor.” “I’ve never been so happy to make a 50 on a test! Beat the class average by over 20 points.” “Would it kill him to just put one problem on the test that we’ve actually seen before?”
  41. 41. The results: 4 major themes 3 Gender and Other Minorities Issues
  42. 42. The results: 4 major themes 3 Gender and Other Minorities Issues “Finally talked to a girl today!!! ...It was Siri.”
  43. 43. The results: 4 major themes 3 Gender and Other Minorities Issues “Finally talked to a girl today!!! ...It was Siri.” “Trying to find a girls bathroom in an engineering building”
  44. 44. The results: 4 major themes 3 Gender and Other Minorities Issues “Finally talked to a girl today!!! ...It was Siri.” “Trying to find a girls bathroom in an engineering building” “85 kids leaving the classroom before mine…of those 85 four are girls. Engineers math class #Stereotypical”
  45. 45. The results: 4 major themes 3 Gender and Other Minorities Issues “Finally talked to a girl today!!! ...It was Siri.” “Trying to find a girls bathroom in an engineering building” “85 kids leaving the classroom before mine…of those 85 four are girls. Engineers math class #Stereotypical” “I get a B- on my public speech because I talked about The National Society Of Black Engineers.”
  46. 46. The results: 4 major themes 4 Engineer Stereotypes and Identity Formation
  47. 47. The results: 4 major themes 4 Engineer Stereotypes and Identity Formation “I am f***ing studying engineering but who am I really? A nerd!!! Dawg a nerd! No one looks at engineers as special alight! Read that!”
  48. 48. The results: 4 major themes 4 Engineer Stereotypes and Identity Formation “I am f***ing studying engineering but who am I really? A nerd!!! Dawg a nerd! No one looks at engineers as special alight! Read that!” “Professor just called us nerds. Every room I go in it smells like nerd...strong.”
  49. 49. The results: 4 major themes 4 Engineer Stereotypes and Identity Formation “I am f***ing studying engineering but who am I really? A nerd!!! Dawg a nerd! No one looks at engineers as special alight! Read that!” “Professor just called us nerds. Every room I go in it smells like nerd...strong.” “I hate having class in the Engineering building. Engineers don’t shower”
  50. 50. Implications and Future works
  51. 51. Implications and Future works 1 Research and Policy Implications
  52. 52. Implications and Future works 1 Research and Policy Implications 2 Social Support, Community Building
  53. 53. Implications and Future works 1 Research and Policy Implications 2 Social Support, Community Building 3 Social Media Analytics Tool for Education
  54. 54. Are these Twitter users building a community? Partial #engineeringProblems Network based on Mentions, Replies, and Follows
  55. 55. Questions ? Xin Chen, Mihaela Vorvoreanu Ph.D, Krishna Madhavan Ph.D Purdue University {chen654, mihaela, cm}@purdue.edu

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