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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
1

Motivation

2

Methodology

3

Data Analysis and Results

4

Future Work
1

Motivation

2

Methodology

3

Data Analysis and Results

4

Future Work
Motivation
Trends & Core Topics of EER

Analyze Literature, e. g. JEE

Paper review by a domain expert
Motivation

Wankat (1999)
Motivation

Wankat (2004)
Motivation
Trends & Core Topics of EER

Analyze Literature, e. g. JEE

Paper review by a domain expert

Machine analysis based on word count
Motivation

Kim, Ko, Elmqvist, & Ebert (2011)
Motivation

Viégas, Wattenberg, & Feinberg (2009)
Motivation
Motivation
Motivation
However
see
use
many
get

terms
may

large
agreed

using
Motivation

Pomales-Garcia & Liu (2007)

Friesen, Taylor & Britton (2005)

Brophy, Klein, Portsmore & Rogers (2008)
Motivation

Pomales-Garcia & Liu (2007)
Motivation
Motivation

1
Friesen, Taylor & Britton (2005)
Motivation

1
Motivation

1

2

Brophy, Klein, Portsmore & Rogers (2008)
Motivation

1

2
Motivation

1

2

3
Motivation

1

2

3
Motivation
1

2

3
Motivation
Problem Solving Spectrum
Motivation
Problem Solving Spectrum
Motivation
Problem Solving Spectrum

Social tagging harnesses the power of collective human intelligence.

Human intelligence tasks that computers are unable to do.
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
Motivation

EER community

Our goal is to build an interactive data mining
and visualization interface for EER community.
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.
1

Motivation

2

Methodology

3

Data Analysis and Results

4

Future Work
1

Motivation

2

Methodology

3

Data Analysis and Results

4

Future Work
Methodology

Weighted Social Tagging
Weight
Confidence Rating

Tagger
Document

Tag
Methodology
Document

Anthony et al. (2007)

Tagger

Tag

Weight

Confidence Rating
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.
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
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
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
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
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
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
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
Methodology

3 ENE graduate
students

152 papers
JEE 2005-2009
Methodology

3 ENE graduate
students

152 papers
JEE 2005-2009

3,456 tags each with a weight
and a confidence rating
Methodology

3 ENE graduate
students

152 papers
JEE 2005-2009

3,456 tags each with a weight
and a confidence rating

Trends &
core content
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
1

Motivation

2

Methodology

3

Data Analysis and Results

4

Future Work
1

Motivation

2

Methodology

3

Data Analysis and Results

4

Future Work
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.	
  
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.	
  
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
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
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
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.	
  
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.	
  
Data Analysis and Results
H2

Better description of content of individual papers.

Pomales-Garcia & Liu (2007)
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
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
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
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
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
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.	
  
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.	
  
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
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
2005

2006

2007

2008

2009

engineering
education
learning
students
research
student
programs
study
journal
design
cooperative
university
faculty
accreditation
women
college
assessment
work
laboratory
program

engineering
students
education
learning
research
project
knowledge
teaching
university
science
journal
student

engineering
students
design
education
research
university
student

engineering
students
education
research
design
learning
journal
student

engineering
education
students
research
learning
ethics
science
journal
women
teaching
career
faculty
development
data
study

course
process
design
educational
faculty
study
history
transfer

information
study
journal
learning
problem
work
process
science
women
faculty
analysis
experts
participants

knowledge
science
university
program
qualitative
faculty
analysis
teaching
study
methods
courses
conceptual

university
student
efficacy
national
participants
2005

2006

2007

2008

2009

engineering
engineering
education
learning
students
research
student
programs
study
journal
design
cooperative
university
faculty
accreditation
women
college
assessment
work
laboratory
program

engineering
students
education
education
learning
research
project
knowledge
teaching
teaching
university
science
journal
student

engineering
students
students
design
education
research
university
student

engineering
students
education
research
design
learning
learning
journal
student

engineering
education
students
research
research
learning
ethics
science
journal
women
teaching
career
faculty
development
data
study

course
process
design
educational
faculty
study
history
transfer

information
study
journal
learning
problem
work
process
Generic Terms
science
women
faculty
analysis
experts
participants

knowledge
science
university
...
program
qualitative
faculty
analysis
teaching
study
methods
courses
conceptual

university
student
efficacy
national
participants
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
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
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
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
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
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
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
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
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

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interactive

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gender

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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

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comparative

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industry

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organization

t-test

efficiency

mechanism

institutional difference

curriculum

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2006

2007

2008

2009

assessment

simulation

concept

how people learn

survey

engagement

retention

knowledge

concept

discipline

laboratory

ethics

teamwork

active learning

teamwork

skill

survey

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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
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
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
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

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descriptive study

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2006

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2007

2008

2009

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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.
1

Motivation

2

Methodology

3

Data Analysis and Results

4

Future Work
1

Motivation

2

Methodology

3

Data Analysis and Results

4

Future Work
Future Work

EER community
Future Work
1

Increase number and diversity of taggers

EER community
Future Work
1

Increase number and diversity of taggers

2

Ease tagging process

EER community
Future Work
1

Increase number and diversity of taggers

2

Ease tagging process

3

Further analyze trends from different angles

EER community
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
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
Questions ?

Xin Chen, Nikitha Sambamurthy, Corey Schimpf,
Hanjun Xian, Krishna Madhavan
{chen654, snikitha, cschimpf, hxian, cm}@purdue.edu

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ASEE2011 Presentation: Social Tagging