Weighted Social Tagging Reveals Deeper Insights in EER Literature
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This is a presentation at ASEE2011, Vancouver, BC, Canada.
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Weighted Social Tagging Reveals Deeper Insights in EER Literature
- 1. Weighted Social Tagging as a Research Methodology to Determine Systemic Trends in Engineering Education Research Xin Chen, Nikitha Sambamurthy, Corey Schimpf, Hanjun Xian, Krishna Madhavan {chen654, snikitha, cschimpf, hxian, cm}@purdue.edu
- 2. 1 Motivation 2 Methodology 3 Data Analysis and Results 4 Future Work
- 3. 1 Motivation 2 Methodology 3 Data Analysis and Results 4 Future Work
- 4. Motivation Trends & Core Topics of EER Analyze Literature, e. g. JEE Paper review by a domain expert
- 7. Motivation Trends & Core Topics of EER Analyze Literature, e. g. JEE Paper review by a domain expert Machine analysis based on word count
- 8. Motivation Kim, Ko, Elmqvist, & Ebert (2011)
- 9. Motivation Viégas, Wattenberg, & Feinberg (2009)
- 10. Motivation
- 11. Motivation
- 13. Motivation Pomales-Garcia & Liu (2007) Friesen, Taylor & Britton (2005) Brophy, Klein, Portsmore & Rogers (2008)
- 14. Motivation Pomales-Garcia & Liu (2007)
- 15. Motivation
- 16. Motivation 1 Friesen, Taylor & Britton (2005)
- 17. Motivation 1
- 18. Motivation 1 2 Brophy, Klein, Portsmore & Rogers (2008)
- 19. Motivation 1 2
- 20. Motivation 1 2 3
- 21. Motivation 1 2 3
- 22. Motivation 1 2 3
- 25. Motivation Problem Solving Spectrum Social tagging harnesses the power of collective human intelligence. Human intelligence tasks that computers are unable to do.
- 26. Motivation Problem Solving Spectrum “19th century culture was defined by the novel, 20th century culture by cinema, the culture of the 21st century will be defined by the interface. ” -- Quoted by Aaron Koblin in the TED talk: Artfully Visualizing Our Humanity
- 27. Motivation EER community Our goal is to build an interactive data mining and visualization interface for EER community.
- 28. Motivation Weighted Social Tagging Weight Confidence Rating Tagger Document Tag Our very first attempt to introduce human insight and precision into the analysis of systemic trends.
- 29. 1 Motivation 2 Methodology 3 Data Analysis and Results 4 Future Work
- 30. 1 Motivation 2 Methodology 3 Data Analysis and Results 4 Future Work
- 31. Methodology Weighted Social Tagging Weight Confidence Rating Tagger Document Tag
- 32. Methodology Document Anthony et al. (2007) Tagger Tag Weight Confidence Rating
- 33. Methodology Document Tag Weight cross-disciplinary 10/100 Anthony et al. (2007) Tagger Confidence Rating 0.5 I’m 50% sure that cross-disciplinary weights 10 out of100 as a descriptive term for this paper.
- 34. Methodology Tag Weight Confidence Composite cross-disciplinary Methodology Anthony et al. (2007) Implication 0.5 5 teams 5 0.3 1.5 technology Background 10 20 0.5 10 mix-methods 15 0.7 10.5 statistics 15 0.7 10.5 implement 20 0.5 10 encourage 15 0.5 7.5
- 35. Methodology Tag Weight Confidence Composite cross-disciplinary Methodology Anthony et al. (2007) Implication 0.5 5 teams 5 0.3 1.5 technology Background 10 20 0.5 10 mix-methods 15 0.7 10.5 statistics 15 0.7 10.5 implement 20 0.5 10 encourage 15 0.5 7.5
- 36. Methodology Tag Weight Confidence Composite cross-disciplinary Methodology Anthony et al. (2007) Implication 0.5 5 teams 5 0.3 1.5 technology Background 10 20 0.5 10 mix-methods 15 0.7 10.5 statistics 15 0.7 10.5 implement 20 0.5 10 encourage 15 0.5 7.5
- 37. Methodology Tag Weight Confidence Composite cross-disciplinary Methodology Anthony et al. (2007) Implication 0.5 5 teams 5 0.3 1.5 technology Background 10 20 0.5 10 mix-methods 15 0.7 10.5 statistics 15 0.7 10.5 implement 20 0.5 10 encourage 15 0.5 7.5
- 38. Methodology Tag Weight Confidence Composite cross-disciplinary Methodology Anthony et al. (2007) Implication 0.5 5 teams 5 0.3 1.5 technology Background 10 20 0.5 10 mix-methods 15 0.7 10.5 statistics 15 0.7 10.5 implement 20 0.5 10 encourage 15 0.5 7.5 Sum=100
- 39. Methodology Tag Weight Confidence Composite cross-disciplinary Methodology Anthony et al. (2007) Implication 0.5 5 teams 5 0.3 1.5 technology Background 10 20 0.5 10 mix-methods 15 0.7 10.5 statistics 15 0.7 10.5 implement 20 0.5 10 encourage 15 0.5 7.5 Each rating ranges 0~1
- 40. Methodology Tag Weight Confidence Composite cross-disciplinary Methodology Anthony et al. (2007) Implication 0.5 5 teams 5 0.3 1.5 technology Background 10 20 0.5 10 mix-methods 15 0.7 10.5 statistics 15 0.7 10.5 implement 20 0.5 10 encourage 15 0.5 7.5 Weight x Confidence
- 41. Methodology 3 ENE graduate students 152 papers JEE 2005-2009
- 42. Methodology 3 ENE graduate students 152 papers JEE 2005-2009 3,456 tags each with a weight and a confidence rating
- 43. Methodology 3 ENE graduate students 152 papers JEE 2005-2009 3,456 tags each with a weight and a confidence rating Trends & core content
- 44. Methodology 3 ENE graduate students 152 papers JEE 2005-2009 3,456 tags each with a weight and a confidence rating Trends & core content Characteristics of the taggers Information Retrieval
- 45. 1 Motivation 2 Methodology 3 Data Analysis and Results 4 Future Work
- 46. 1 Motivation 2 Methodology 3 Data Analysis and Results 4 Future Work
- 47. Data Analysis and Results 3 Hypotheses Compared with word frequency counting, weighted social tagging method could get: H1 Wide coverage of meaning space with minimized bias. H2 Better description of content of individual papers. H3 Be#er characteriza,on of trends in the body of literature.
- 48. Data Analysis and Results 3 Hypotheses Compared with word frequency counting, weighted social tagging method could get: H1 Wide coverage of meaning space with minimized bias. H2 Better description of content of individual papers. H3 Be#er characteriza,on of trends in the body of literature.
- 49. Data Analysis and Results H1 Wide coverage of meaning space with minimized bias. Composite Scores from Tagger A 20 High correlation, Narrow coverage 15 10 5 0 5 10 15 Composite Scores from Tagger B 20
- 50. Data Analysis and Results H1 Wide coverage of meaning space with minimized bias. Composite Scores from Tagger A 20 Low correlation, Wide coverage 15 10 5 0 5 10 15 Composite Scores from Tagger B 20
- 51. Data Analysis and Results H1 Wide coverage of meaning space with minimized bias. -0.2254 -0.0542 0.0581 -0.2105 weight confidence -0.1737 -0.0489 -0.0969 -0.0378 composite -0.2226
- 52. Data Analysis and Results 3 Hypotheses Compared with word frequency counting, weighted social tagging method could get: H1 Wide coverage of meaning space with minimized bias. H2 Better description of content of individual papers. H3 Be#er characteriza,on of trends in the body of literature.
- 53. Data Analysis and Results 3 Hypotheses Compared with word frequency counting, weighted social tagging method could get: H1 Wide coverage of meaning space with minimized bias. H2 Better description of content of individual papers. H3 Be#er characteriza,on of trends in the body of literature.
- 54. Data Analysis and Results H2 Better description of content of individual papers. Pomales-Garcia & Liu (2007)
- 55. Data Analysis and Results H2 Better description of content of individual papers. Word Frequency Counting Top 15 keywords Weighted Social Tagging students engineering education participants skills teaching questions student professors study excellence technology classroom more used interviews sex parity student view student involvement excellence ethnographic perspective perception discrepancies consensus institutions undergraduate participant activities qualitative research technology usage variables discursive
- 56. Data Analysis and Results H2 Better description of content of individual papers. Word Frequency Counting Top 15 keywords Weighted Social Tagging students engineering education participants skills teaching questions student professors study excellence excellence technology classroom more used interviews sex parity student view student involvement excellence excellence ethnographic perspective perception discrepancies consensus institutions undergraduate participant activities qualitative research technology usage variables discursive
- 57. Data Analysis and Results H2 Better description of content of individual papers. Word Frequency Counting Top 15 keywords Weighted Social Tagging students engineering education participants skills teaching questions student professors study excellence technology technology classroom more used interviews sex parity student view student involvement excellence ethnographic perspective perception discrepancies consensus institutions undergraduate participant activities qualitative research technology usage technology usage variables discursive
- 58. Data Analysis and Results H2 Better description of content of individual papers. Word Frequency Counting Top 15 keywords Weighted Social Tagging students engineering education participants participants skills teaching questions student professors study excellence technology classroom more used interviews sex parity student view student involvement excellence ethnographic perspective perception discrepancies consensus institutions undergraduate participant activities participants activities qualitative research technology usage variables discursive
- 59. Data Analysis and Results H2 Better description of content of individual papers. Word Frequency Counting Top 15 keywords Weighted Social Tagging students engineering education participants skills teaching questions student professors study excellence technology classroom more used interviews interviews sex parity sex parity student student view view student involvement student involvement excellence ethnographic perspective ethnographic perspective perception discrepancies consensus institutions undergraduate participant activities qualitative research technology usage variables discursive
- 60. Data Analysis and Results 3 Hypotheses Compared with word frequency counting, weighted social tagging method could get: H1 Wide coverage of meaning space with minimized bias. H2 Better description of content of individual papers. H3 Be#er characteriza,on of trends in the body of literature.
- 61. Data Analysis and Results 3 Hypotheses Compared with word frequency counting, weighted social tagging method could get: H1 Wide coverage of meaning space with minimized bias. H2 Better description of content of individual papers. H3 Be#er characteriza,on of trends in the body of literature.
- 62. Data Analysis and Results H3 Be#er characteriza,on of trends in the body of literature. Top 20 Keywords 2005-2009 Word Frequency Counting Weighted Social Tagging
- 63. Data Analysis and Results H3 Be#er characteriza,on of trends in the body of literature. Top 20 Keywords 2005-2009 Word Frequency Counting Weighted Social Tagging
- 66. Data Analysis and Results H3 Be#er characteriza,on of trends in the body of literature. Top 20 Keywords 2005-2009 Word Frequency Counting Weighted Social Tagging
- 67. Data Analysis and Results H3 Be#er characteriza,on of trends in the body of literature. Top 20 Keywords 2005-2009 Word Frequency Counting Weighted Social Tagging
- 68. 2005 2006 2007 2008 2009 assessment simulation concept how people learn survey engagement retention knowledge concept discipline laboratory ethics teamwork active learning teamwork skill survey ethnography design women experiment model expert qualitative self-efficacy problem-based learning interactive model methodology gender historical knowledge essay meta-analysis engineering education collaboration class design First Year Engineering pedagogy faculty concept entrepreneurship satisfaction development concept women assessment retention survey behavioral complexity skills innovation cross-disciplinary research career creative experiment comparative cross-disciplinary interview self-directed learning active learning discourse assessment k-12 methodology online engineering culture engineering culture retention accessibility institution diversity feedback collaboration descriptive interaction individual future scenarios recruitment intention comparative semi-structured interview learning factory comparative bias industry women retention descriptive study organization t-test efficiency mechanism institutional difference curriculum attrition observation cognitive psychology cross-profession training
- 69. 2005 2006 2007 2008 2009 assessment simulation concept how people learn survey engagement retention knowledge concept discipline laboratory ethics teamwork active learning teamwork skill survey ethnography design women experiment model expert qualitative self-efficacy problem-based learning interactive model methodology gender historical knowledge essay meta-analysis engineering education collaboration class design First Year Engineering pedagogy faculty concept entrepreneurship satisfaction development concept women assessment retention survey behavioral complexity skills innovation cross-disciplinary research career creative experiment comparative cross-disciplinary interview self-directed learning active learning discourse assessment k-12 methodology online engineering culture engineering culture retention accessibility institution diversity feedback collaboration descriptive interaction individual future scenarios recruitment intention comparative semi-structured interview learning factory comparative bias industry women retention descriptive study organization t-test efficiency mechanism institutional difference curriculum attrition observation cognitive psychology cross-profession training
- 70. 2005 assessment assessment 2006 2007 2008 2009 simulation concept how people learn survey engagement retention knowledge concept discipline laboratory ethics teamwork active learning teamwork skill survey ethnography design women experiment model expert qualitative self-efficacy problem-based learning interactive model methodology gender historical knowledge essay meta-analysis engineering education collaboration class design First Year Engineering pedagogy faculty concept entrepreneurship satisfaction development concept women assessment assessment retention survey behavioral complexity skills innovation cross-disciplinary research career creative experiment comparative cross-disciplinary interview self-directed learning active learning discourse assessment assessment k-12 methodology online engineering culture engineering culture retention accessibility institution diversity feedback collaboration descriptive interaction individual future scenarios recruitment intention comparative semi-structured interview learning factory comparative bias industry women retention descriptive study organization t-test efficiency mechanism institutional difference curriculum attrition observation cognitive psychology cross-profession training
- 71. 2005 2006 2007 2008 2009 assessment simulation concept how people learn survey engagement retention knowledge concept discipline laboratory ethics teamwork active learning teamwork skill survey ethnography design women experiment model expert qualitative self-efficacy problem-based learning interactive model methodology gender historical knowledge essay meta-analysis engineering education collaboration class design First Year Engineering pedagogy faculty concept entrepreneurship satisfaction development concept women assessment retention survey behavioral complexity skills innovation cross-disciplinary research career creative experiment comparative cross-disciplinary interview self-directed learning active learning discourse assessment k-12 methodology online engineering culture engineering culture retention accessibility institution diversity feedback collaboration descriptive interaction individual future scenarios recruitment intention comparative semi-structured interview learning factory comparative bias industry women retention descriptive study organization t-test efficiency mechanism institutional difference curriculum attrition observation 2005 2006 assessment 2007 2008 2009 cognitive psychology cross-profession training
- 72. 2005 2006 2007 2008 2009 assessment simulation concept how people learn survey engagement retention knowledge concept discipline laboratory ethics teamwork active learning teamwork skill survey ethnography design women experiment model expert qualitative self-efficacy problem-based learning interactive model methodology gender historical knowledge essay meta-analysis engineering education collaboration class design First Year Engineering pedagogy faculty concept entrepreneurship satisfaction development concept women assessment retention survey behavioral complexity skills innovation cross-disciplinary research career creative experiment comparative cross-disciplinary interview self-directed learning active learning discourse assessment k-12 methodology online engineering culture engineering culture retention accessibility institution diversity feedback collaboration descriptive interaction individual future scenarios recruitment intention comparative semi-structured interview learning factory comparative bias industry women retention descriptive study organization t-test efficiency mechanism institutional difference curriculum attrition observation cognitive psychology cross-profession training
- 73. 2005 2006 2007 2008 2009 assessment simulation concept how people learn survey engagement retention knowledge concept discipline laboratory ethics teamwork active learning teamwork skill survey ethnography design women women experiment model expert qualitative self-efficacy problem-based learning interactive model methodology gender historical knowledge essay meta-analysis engineering education collaboration class design First Year Engineering pedagogy faculty concept entrepreneurship satisfaction development concept women women assessment retention survey behavioral complexity skills innovation cross-disciplinary research career creative experiment comparative cross-disciplinary interview self-directed learning active learning discourse assessment k-12 methodology online engineering culture engineering culture retention accessibility institution diversity feedback collaboration descriptive interaction individual future scenarios recruitment intention comparative semi-structured interview learning factory comparative bias industry women women retention descriptive study organization t-test efficiency mechanism institutional difference curriculum attrition observation cognitive psychology cross-profession training
- 74. 2005 2006 2007 2008 2009 assessment simulation concept how people learn survey engagement retention knowledge concept discipline laboratory ethics teamwork active learning teamwork skill survey ethnography design women experiment model expert qualitative self-efficacy problem-based learning interactive model methodology gender historical knowledge essay meta-analysis engineering education collaboration class design First Year Engineering pedagogy faculty concept entrepreneurship satisfaction development concept women assessment retention survey behavioral complexity skills innovation cross-disciplinary research career creative experiment comparative cross-disciplinary interview self-directed learning active learning discourse assessment k-12 methodology online engineering culture engineering culture retention accessibility institution diversity feedback collaboration descriptive interaction individual future scenarios recruitment intention comparative semi-structured interview learning factory comparative bias industry women retention descriptive study organization t-test efficiency mechanism institutional difference curriculum attrition observation 2005 2006 women 2007 2008 2009 cognitive psychology cross-profession training
- 75. 2005 2006 2007 2008 2009 assessment simulation concept how people learn survey engagement retention knowledge concept discipline laboratory ethics teamwork active learning teamwork skill survey ethnography design women experiment model expert qualitative self-efficacy problem-based learning interactive model methodology gender historical knowledge essay meta-analysis engineering education collaboration class design First Year Engineering pedagogy faculty concept entrepreneurship satisfaction development concept women assessment retention survey behavioral complexity skills innovation cross-disciplinary research career creative experiment comparative cross-disciplinary interview self-directed learning active learning discourse assessment k-12 methodology online engineering culture engineering culture retention accessibility institution diversity feedback collaboration descriptive interaction individual future scenarios recruitment intention comparative semi-structured interview learning factory comparative bias industry women retention descriptive study organization t-test efficiency mechanism institutional difference curriculum attrition observation cognitive psychology cross-profession training
- 76. 2005 2006 2007 2008 2009 assessment simulation concept how people learn survey engagement retention retention knowledge concept discipline laboratory ethics teamwork active learning teamwork skill survey ethnography design women experiment model expert qualitative self-efficacy problem-based learning interactive model methodology gender historical knowledge essay meta-analysis engineering education collaboration class design First Year Engineering pedagogy faculty concept entrepreneurship satisfaction development concept women assessment retention retention survey behavioral complexity skills innovation cross-disciplinary research career creative experiment comparative cross-disciplinary interview self-directed learning active learning discourse assessment k-12 methodology online engineering culture engineering culture retention retention accessibility institution diversity feedback collaboration descriptive interaction individual future scenarios recruitment intention comparative semi-structured interview learning factory comparative bias industry women retention retention descriptive study organization t-test efficiency mechanism institutional difference curriculum attrition observation cognitive psychology cross-profession training
- 77. 2005 2006 2007 2008 2009 assessment simulation concept how people learn survey engagement retention knowledge concept discipline laboratory ethics teamwork active learning teamwork skill survey ethnography design women experiment model expert qualitative self-efficacy problem-based learning interactive model methodology gender historical knowledge essay meta-analysis engineering education collaboration class design First Year Engineering pedagogy faculty concept entrepreneurship satisfaction development concept women assessment retention survey behavioral complexity skills innovation cross-disciplinary research career creative experiment comparative cross-disciplinary interview self-directed learning active learning discourse assessment k-12 methodology online engineering culture engineering culture retention accessibility institution diversity feedback collaboration descriptive interaction individual future scenarios recruitment intention comparative semi-structured interview learning factory comparative bias industry women retention descriptive study organization t-test efficiency mechanism institutional difference curriculum attrition observation 2005 2006 retention 2007 2008 2009 cognitive psychology cross-profession training
- 78. 2005 2006 2007 2008 2009 assessment simulation concept how people learn survey engagement retention knowledge concept discipline laboratory ethics teamwork active learning teamwork skill survey ethnography design women experiment model expert qualitative self-efficacy problem-based learning interactive model methodology gender historical knowledge essay meta-analysis engineering education collaboration class design First Year Engineering pedagogy faculty concept entrepreneurship satisfaction development concept women assessment retention survey behavioral complexity skills innovation cross-disciplinary research career creative experiment comparative cross-disciplinary interview self-directed learning active learning discourse assessment k-12 methodology online engineering culture engineering culture retention accessibility institution diversity feedback collaboration descriptive interaction individual future scenarios recruitment intention comparative semi-structured interview learning factory comparative bias industry women retention descriptive study organization t-test efficiency mechanism institutional difference curriculum attrition observation cognitive psychology cross-profession training
- 79. 2005 2006 2007 2008 assessment simulation concept how people learn 2009 survey survey engagement retention knowledge concept discipline laboratory ethics teamwork active learning teamwork skill survey survey ethnography design women experiment model expert qualitative self-efficacy problem-based learning interactive model methodology gender historical knowledge essay meta-analysis engineering education collaboration class design First Year Engineering pedagogy faculty concept entrepreneurship satisfaction development concept women assessment retention survey survey behavioral complexity skills innovation cross-disciplinary research career creative experiment comparative cross-disciplinary interview self-directed learning active learning discourse assessment k-12 methodology online engineering culture engineering culture retention accessibility institution diversity feedback collaboration descriptive interaction individual future scenarios recruitment intention comparative semi-structured interview learning factory comparative bias industry women retention descriptive study organization t-test efficiency mechanism institutional difference curriculum attrition observation cognitive psychology cross-profession training
- 80. 2005 2006 2007 2008 2009 assessment simulation concept how people learn survey engagement retention knowledge concept discipline laboratory ethics teamwork active learning teamwork skill survey ethnography design women experiment model expert qualitative self-efficacy problem-based learning interactive model methodology gender historical knowledge essay meta-analysis engineering education collaboration class design First Year Engineering pedagogy faculty concept entrepreneurship satisfaction development concept women assessment retention survey behavioral complexity skills innovation cross-disciplinary research career creative experiment comparative cross-disciplinary interview self-directed learning active learning discourse assessment k-12 methodology online engineering culture engineering culture retention accessibility institution diversity feedback collaboration descriptive interaction individual future scenarios recruitment intention comparative semi-structured interview learning factory comparative bias industry women retention descriptive study organization t-test efficiency mechanism institutional difference curriculum attrition observation 2005 2006 survey 2007 2008 2009 cognitive psychology cross-profession training
- 81. Data Analysis and Results H3 Be#er characteriza,on of trends in the body of literature. Composite Score 120 2005 2006 2007 2008 2009 85 50 15 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Top 20 Tags Top ranking tags become less dominant indicates topics are expanding.
- 82. 1 Motivation 2 Methodology 3 Data Analysis and Results 4 Future Work
- 83. 1 Motivation 2 Methodology 3 Data Analysis and Results 4 Future Work
- 85. Future Work 1 Increase number and diversity of taggers EER community
- 86. Future Work 1 Increase number and diversity of taggers 2 Ease tagging process EER community
- 87. Future Work 1 Increase number and diversity of taggers 2 Ease tagging process 3 Further analyze trends from different angles EER community
- 88. Future Work 1 Increase number and diversity of taggers 2 Ease tagging process 3 Further analyze trends from different angles 4 Analyze characteristics of taggers EER community
- 89. Future Work 1 Increase number and diversity of taggers 2 Ease tagging process 3 Further analyze trends from different angles 4 Analyze characteristics of taggers 5 Build an interactive interface EER community
- 90. Questions ? Xin Chen, Nikitha Sambamurthy, Corey Schimpf, Hanjun Xian, Krishna Madhavan {chen654, snikitha, cschimpf, hxian, cm}@purdue.edu