SlideShare a Scribd company logo
1 of 43
Download to read offline
Introduction to
Epistemic Network
Analysis
Vitomir Kovanovic,
University of South Australia
#vkovanovic
Vitomir.Kovanovic@unisa.edu.au
1
What is Epistemic Network
Analysis (ENA)
• Epistemic Network Analysis is a network-based method for
analysing codified data.
• Developed by Professor David Shaffer from the University of
Wisconsin Madison (UWM) and his team.
• There is a web interface and R package
• http://epistemicnetwork.org
2
The original problem of ENA
• Understand how people become professionals
• Involves understanding of the ways important concepts –codes–
interact together
• The applications of ENA expanded far beyond epistemology domain
• New term: Quantitative ethnography.
• Can be used to understand how different codes co-occur.
3
What is Epistemology?
Epistemology studies the nature of
knowledge, justification, and the
rationality of belief
New term: quantitative ethnography
4
Video
https://www.youtube.com/watch?v=wrTiXNIeHZA
5
ENA in Education
• Often used for understanding of student conversations and
discussion messages.
• Also used for analysis of interview data.
6
Key concepts in ENA
• Codes: a set of concepts whose interactions we want to understand
• Unit of analysis: objects for which we want to understand
interactions between the codes
• Stanza(Conversation): Units in which we measure code co-
occurrence
7
How ENA works: Example
dataset
8
• Units of analysis: Individual Students
• Stanzas: Individual messages
How ENA works: Example
dataset
9
• Codes:
• Data
• Technical Constraints
• Performance Parameters
• Client and Consultant Requests
• Design Reasoning
• Collaboration
How ENA works: Code co-
occurrence matrix
10
• Code co-occurrence in stanzas is used to produce code co-occurrence
matrix for each unit of analysis (i.e., person)
Data Technical
Constraints
Performance
Parameters
Client and
Consultant
Requests
Design
Reasoning
Collaboration
Data / 120 80 323 52 32
Technical
Constraints
/ 23 120 112 32
Performance
Parameters
/ 17 28 152
Client and
Consultant
Requests
/ 21 68
Design
Reasoning
/ 12
Collaboration /
How ENA works: Code co-
occurrence matrix
11
• Code co-occurrence in stanzas is used to produce code co-occurrence
matrix for each unit of analysis (i.e., person)
C1 C2 C3 C4 C5 C6
C1 / 120 80 323 52 32
C2 / 23 120 112 32
C3 / 17 28 152
C4 / 21 68
C5 / 12
C6 /
How ENA works: Code co-
occurrence matrix
12
• Code co-occurrence in stanzas is used to produce code co-occurrence
matrix for each unit of analysis (i.e., person)
C1 C2 C3 C4 C5 C6
C1 / 120 80 323 52 32
C2 / 23 120 112 32
C3 / 17 28 152
C4 / 21 68
C5 / 12
C6 /
How ENA works: Matrix to
vector
13
• Co-occurrence matrices are “flattened out” into vectors of N(N-1)/2 elements
6*5/2=15 columns (dimensions)
C1-
C2
C1-
C3
C1-
C4
C1-
C5
C1-
C6
C2-
C3
C2-
C4
C2-
C5
C2-
C6
C3-
C4
C3-
C5
C3-
C6
C4-
C5
C4-
C6
C5-
C6
U1 120 80 323 52 32 23 120 112 32 17 28 152 21 68 12
C1 C2 C3 C4 C5 C6
C1 / 120 80 323 52 32
C2 / 23 120 112 32
C3 / 17 28 152
C4 / 21 68
C5 / 12
C6 /
How ENA works: Matrix to
vector
14
• Co-occurrence matrices are “flattened out” into vectors of N(N-1)/2 elements
6*5/2=15 columns (dimensions)
C1-
C2
C1-
C3
C1-
C4
C1-
C5
C1-
C6
C2-
C3
C2-
C4
C2-
C5
C2-
C6
C3-
C4
C3-
C5
C3-
C6
C4-
C5
C4-
C6
C5-
C6
U1 120 80 323 52 32 23 120 112 32 17 28 152 21 68 12
C1 C2 C3 C4 C5 C6
C1 / 120 80 323 52 32
C2 / 23 120 112 32
C3 / 17 28 152
C4 / 21 68
C5 / 12
C6 /
How ENA works: Matrix to
vector
15
• Co-occurrence matrices are “flattened out” into vectors of N(N-1)/2 elements
6*5/2=15 columns (dimensions)
C1-
C2
C1-
C3
C1-
C4
C1-
C5
C1-
C6
C2-
C3
C2-
C4
C2-
C5
C2-
C6
C3-
C4
C3-
C5
C3-
C6
C4-
C5
C4-
C6
C5-
C6
U1 120 80 323 52 32 23 120 112 32 17 28 152 21 68 12
C1 C2 C3 C4 C5 C6
C1 / 120 80 323 52 32
C2 / 23 120 112 32
C3 / 17 28 152
C4 / 21 68
C5 / 12
C6 /
How ENA works: Matrix to
vector
16
• Co-occurrence matrices are “flattened out” into vectors of N(N-1)/2 elements
6*5/2=15 columns (dimensions)
C1-
C2
C1-
C3
C1-
C4
C1-
C5
C1-
C6
C2-
C3
C2-
C4
C2-
C5
C2-
C6
C3-
C4
C3-
C5
C3-
C6
C4-
C5
C4-
C6
C5-
C6
U1 120 80 323 52 32 23 120 112 32 17 28 152 21 68 12
C1 C2 C3 C4 C5 C6
C1 / 120 80 323 52 32
C2 / 23 120 112 32
C3 / 17 28 152
C4 / 21 68
C5 / 12
C6 /
How ENA works: Matrix to
vector
17
• Co-occurrence matrices are “flattened out” into vectors of N(N-1)/2 elements
6*5/2=15 columns (dimensions)
C1-
C2
C1-
C3
C1-
C4
C1-
C5
C1-
C6
C2-
C3
C2-
C4
C2-
C5
C2-
C6
C3-
C4
C3-
C5
C3-
C6
C4-
C5
C4-
C6
C5-
C6
U1 120 80 323 52 32 23 120 112 32 17 28 152 21 68 12
C1 C2 C3 C4 C5 C6
C1 / 120 80 323 52 32
C2 / 23 120 112 32
C3 / 17 28 152
C4 / 21 68
C5 / 12
C6 /
How ENA works: Matrix to
vector
18
• Co-occurrence matrices are “flattened out” into vectors of N(N-1)/2 elements
6*5/2=15 columns (dimensions)
C1-
C2
C1-
C3
C1-
C4
C1-
C5
C1-
C6
C2-
C3
C2-
C4
C2-
C5
C2-
C6
C3-
C4
C3-
C5
C3-
C6
C4-
C5
C4-
C6
C5-
C6
U1 120 80 323 52 32 23 120 112 32 17 28 152 21 68 12
C1 C2 C3 C4 C5 C6
C1 / 120 80 323 52 32
C2 / 23 120 112 32
C3 / 17 28 152
C4 / 21 68
C5 / 12
C6 /
How ENA works: Matrix to
vector
19
• Co-occurrence matrices are converted to vectors and joined together
to form Analytic space of N*(N-1)/2 elements
C1-
C2
C1-
C3
C1-
C4
C1-
C5
C1-
C6
C2-
C3
C2-
C4
C2-
C5
C2-
C6
C3-
C4
C3-
C5
C3-
C6
C4-
C5
C4-
C6
C5-
C6
U1 120 80 323 52 32 23 120 112 32 17 28 152 21 68 12
U2
U3
…
…
U20 32 125 112 52 32 17 128 211 54 36 63 85 109 276 42
• NOTE: Each vector is a point in a 15-dimensional space
• EACH GRAPH IS A POINT
How ENA works: Matrix to
vector
20
• Co-occurrence matrices are converted to vectors and joined together
to form Analytic space of N*(N-1)/2 elements
1-2 1-3 1-4 1-5 1-6 2-3 2-4 2-5 2-6 3-4 3-5 3-6 4-5 4-6 5-6
U1 120 80 323 52 32 23 120 112 32 17 28 152 21 68 12
U2
U3
…
…
U20 32 125 112 52 32 17 128 211 54 36 63 85 109 276 42
• NOTE: Each vector is a point in a 15-dimensional space
• EACH GRAPH IS A POINT
How ENA works: Singular Value
Decomposition of Analytic Space
21
• Approximate N columns with a smaller number R of “composite columns”
• The whole point is to be able to plot N dimensions on a 2D plot
How ENA works: Singular Value
Decomposition of Analytic Space
22
• Approximate N columns with a smaller number R of “composite columns”
• The whole point is to be able to plot N dimensions on a 2D plot
m=1,000 students
n=100 edges
A=100,000
U = 1,000 x 1,000 = 1,000,000
VT= 100 x 100 = 10,000
How ENA works: Singular Value
Decomposition of Analytic Space
23
• Approximate N columns with a smaller number R of “composite columns”
• The whole point is to be able to plot N dimensions on a 2D plot
m=1,000 students
n=100 edges
A=100,000
How ENA works: Singular Value
Decomposition of Analytic Space
24
• Approximate N columns with a smaller number R of “composite columns”
• The whole point is to be able to plot N dimensions on a 2D plot
m=1,000 students
n=100 edges
A=100,000
r=2 (keep top two singular values)
U=1,000 x 2 = 2,000
VT=2 x 100 = 200
Total=2,200
How ENA works: Singular Value
Decomposition of Analytic Space
25
• Approximate N columns with a smaller number R of “composite columns”
• The whole point is to be able to plot N dimensions on a 2D plot
m=1,000 students
n=100 edges
A=100,000
r=2 (singular values)
U=1,000 x 2 = 2,000
VT=2 x 100 = 200
Total=2,200
Latent factor scores (student 2D coordinates)
Latent factor coefficients
(code pair 2D coordinates)
Visualising SVD’ed Analytic
Space: Projection space
26
Visualising individual graphs:
ENA network models
27
Visualising individual graphs:
ENA network models
28
Visualising individual graphs:
ENA network models
29
Remarks on coding
• Code values can be
 Boolean: 0 if code does not occurs, 1 if it does
 Integer: 0 if code does not occur, N if it does N times
 Fractional number: Value indicating “strength” or “association” of the code to the text
• In case of binary values, co-occurrence is 1 if both codes occur
• In case or integer or fractional numbers, co-occurrence score is the product if the
individual scores.
• Fractions useful for:
 LDA topic modelling:
 Each topic is a code, code value are topic associations to individual texts
• Integers useful for:
 Word count analysis:
 Each word (category) is a code, co-occurrence value is the product of code scores.
30
Moving stanza
• Stanza can be moving,
specially useful for
conversations where
individual messages
are too short
31
ENA Example 1: CoI + LDA
E Ferreira, R., Kovanovic, V., Gasevic, D., & Rolim, V. (2018). Towards
Combined Network and Text Analytics of Student Discourse in Online
Discussions. In The 19th International Conference on Artificial
Intelligence in Education. London, UK.
• Understand the development of cognitive presence with respect to
different course topics
 CoI process model, does not pay attention to course content
• Examine the role of instructional intervention of role assignment
32
ENA Example 1: CoI + LDA
• 1,747 messages from 6 course offers
• Each message coded for the level of cognitive presence:
 Triggering Event
 Exploration
 Integration
 Resolution
 Other
• Applied topic modelling to pick course topics
 Extracted topics were corresponding to course topics
 + one topic regarding logistics
33
Results: Projection graph all
students
34
Results: Projection graph
(intervention + control groups)
35
Control
Intervention
Results: ENA network model for
all students
36All students
Results: ENA network model for
two student groups
37Control Intervention
Social-Epistemic Network
Signature
ENA clusters
Topics per cluster
Cluster 1 Cluster 2
Cluster 3
Cluster 4
Social centrality per ENA cluster
Recap
• ENA works on codified data
• We need to define
 Codes
 Units
 Stanzas
• Unit’s co-occurrence matrices are converted to vectors
• All unit’s merged to form Analytic space matrix
• Analytic space is reduced to 2D with SVD
• Plot units on the 2D plot
• Plot codes on the 2D plot
42
Practical example
• Data: download from http://bit.ly/enanie
• Go to http://epistemicnetwork.org and create account
43

More Related Content

Similar to Introduction to Epistemic Network Analysis

The following ppt is about principal component analysis
The following ppt is about principal component analysisThe following ppt is about principal component analysis
The following ppt is about principal component analysisSushmit8
 
An analysis between exact and approximate algorithms for the k-center proble...
An analysis between exact and approximate algorithms for the  k-center proble...An analysis between exact and approximate algorithms for the  k-center proble...
An analysis between exact and approximate algorithms for the k-center proble...IJECEIAES
 
5 DimensionalityReduction.pdf
5 DimensionalityReduction.pdf5 DimensionalityReduction.pdf
5 DimensionalityReduction.pdfRahul926331
 
Lecture slides week14-15
Lecture slides week14-15Lecture slides week14-15
Lecture slides week14-15Shani729
 
Fixed-Point Code Synthesis for Neural Networks
Fixed-Point Code Synthesis for Neural NetworksFixed-Point Code Synthesis for Neural Networks
Fixed-Point Code Synthesis for Neural NetworksIJITE
 
Fixed-Point Code Synthesis for Neural Networks
Fixed-Point Code Synthesis for Neural NetworksFixed-Point Code Synthesis for Neural Networks
Fixed-Point Code Synthesis for Neural Networksgerogepatton
 
Clustering of graphs and search of assemblages
Clustering of graphs and search of assemblagesClustering of graphs and search of assemblages
Clustering of graphs and search of assemblagesData-Centric_Alliance
 
Dimension Reduction Introduction & PCA.pptx
Dimension Reduction Introduction & PCA.pptxDimension Reduction Introduction & PCA.pptx
Dimension Reduction Introduction & PCA.pptxRohanBorgalli
 
Mm chap08 -_lossy_compression_algorithms
Mm chap08 -_lossy_compression_algorithmsMm chap08 -_lossy_compression_algorithms
Mm chap08 -_lossy_compression_algorithmsEellekwameowusu
 
Learning Convolutional Neural Networks for Graphs
Learning Convolutional Neural Networks for GraphsLearning Convolutional Neural Networks for Graphs
Learning Convolutional Neural Networks for GraphsMathias Niepert
 
Tensorizing Neural Network
Tensorizing Neural NetworkTensorizing Neural Network
Tensorizing Neural NetworkRuochun Tzeng
 
DimensionalityReduction.pptx
DimensionalityReduction.pptxDimensionalityReduction.pptx
DimensionalityReduction.pptx36rajneekant
 
Do's and Don'ts of using t-SNE.pdf
Do's and Don'ts of using t-SNE.pdfDo's and Don'ts of using t-SNE.pdf
Do's and Don'ts of using t-SNE.pdfFrankClat
 
Hardware Acceleration for Machine Learning
Hardware Acceleration for Machine LearningHardware Acceleration for Machine Learning
Hardware Acceleration for Machine LearningCastLabKAIST
 
Deep Learning through Pytorch Exercises
Deep Learning through Pytorch ExercisesDeep Learning through Pytorch Exercises
Deep Learning through Pytorch Exercisesaiaioo
 
digital logic circuits, digital component floting and fixed point
digital logic circuits, digital component floting and fixed pointdigital logic circuits, digital component floting and fixed point
digital logic circuits, digital component floting and fixed pointRai University
 

Similar to Introduction to Epistemic Network Analysis (20)

The following ppt is about principal component analysis
The following ppt is about principal component analysisThe following ppt is about principal component analysis
The following ppt is about principal component analysis
 
An analysis between exact and approximate algorithms for the k-center proble...
An analysis between exact and approximate algorithms for the  k-center proble...An analysis between exact and approximate algorithms for the  k-center proble...
An analysis between exact and approximate algorithms for the k-center proble...
 
01 Chapter MATLAB introduction
01 Chapter MATLAB introduction01 Chapter MATLAB introduction
01 Chapter MATLAB introduction
 
5 DimensionalityReduction.pdf
5 DimensionalityReduction.pdf5 DimensionalityReduction.pdf
5 DimensionalityReduction.pdf
 
Lecture slides week14-15
Lecture slides week14-15Lecture slides week14-15
Lecture slides week14-15
 
Fixed-Point Code Synthesis for Neural Networks
Fixed-Point Code Synthesis for Neural NetworksFixed-Point Code Synthesis for Neural Networks
Fixed-Point Code Synthesis for Neural Networks
 
Fixed-Point Code Synthesis for Neural Networks
Fixed-Point Code Synthesis for Neural NetworksFixed-Point Code Synthesis for Neural Networks
Fixed-Point Code Synthesis for Neural Networks
 
Cgm Lab Manual
Cgm Lab ManualCgm Lab Manual
Cgm Lab Manual
 
Clustering of graphs and search of assemblages
Clustering of graphs and search of assemblagesClustering of graphs and search of assemblages
Clustering of graphs and search of assemblages
 
Dimension Reduction Introduction & PCA.pptx
Dimension Reduction Introduction & PCA.pptxDimension Reduction Introduction & PCA.pptx
Dimension Reduction Introduction & PCA.pptx
 
Mm chap08 -_lossy_compression_algorithms
Mm chap08 -_lossy_compression_algorithmsMm chap08 -_lossy_compression_algorithms
Mm chap08 -_lossy_compression_algorithms
 
Learning Convolutional Neural Networks for Graphs
Learning Convolutional Neural Networks for GraphsLearning Convolutional Neural Networks for Graphs
Learning Convolutional Neural Networks for Graphs
 
Tensorizing Neural Network
Tensorizing Neural NetworkTensorizing Neural Network
Tensorizing Neural Network
 
DimensionalityReduction.pptx
DimensionalityReduction.pptxDimensionalityReduction.pptx
DimensionalityReduction.pptx
 
Densebox
DenseboxDensebox
Densebox
 
Do's and Don'ts of using t-SNE.pdf
Do's and Don'ts of using t-SNE.pdfDo's and Don'ts of using t-SNE.pdf
Do's and Don'ts of using t-SNE.pdf
 
Hardware Acceleration for Machine Learning
Hardware Acceleration for Machine LearningHardware Acceleration for Machine Learning
Hardware Acceleration for Machine Learning
 
Neural Networks made easy
Neural Networks made easyNeural Networks made easy
Neural Networks made easy
 
Deep Learning through Pytorch Exercises
Deep Learning through Pytorch ExercisesDeep Learning through Pytorch Exercises
Deep Learning through Pytorch Exercises
 
digital logic circuits, digital component floting and fixed point
digital logic circuits, digital component floting and fixed pointdigital logic circuits, digital component floting and fixed point
digital logic circuits, digital component floting and fixed point
 

More from Vitomir Kovanovic

Introduction to Learning Analytics for High School Teachers and Managers
Introduction to Learning Analytics for High School Teachers and ManagersIntroduction to Learning Analytics for High School Teachers and Managers
Introduction to Learning Analytics for High School Teachers and ManagersVitomir Kovanovic
 
Extending video interactions to support self-regulated learning in an online ...
Extending video interactions to support self-regulated learning in an online ...Extending video interactions to support self-regulated learning in an online ...
Extending video interactions to support self-regulated learning in an online ...Vitomir Kovanovic
 
Analysing social presence in online discussions through network and text anal...
Analysing social presence in online discussions through network and text anal...Analysing social presence in online discussions through network and text anal...
Analysing social presence in online discussions through network and text anal...Vitomir Kovanovic
 
Automated Analysis of Cognitive Presence in Online Discussions Written in Por...
Automated Analysis of Cognitive Presence in Online Discussions Written in Por...Automated Analysis of Cognitive Presence in Online Discussions Written in Por...
Automated Analysis of Cognitive Presence in Online Discussions Written in Por...Vitomir Kovanovic
 
Validating a theorized model of engagement in learning analytics
Validating a theorized model of engagement in learning analyticsValidating a theorized model of engagement in learning analytics
Validating a theorized model of engagement in learning analyticsVitomir Kovanovic
 
Examining the Value of Learning Analytics for Supporting Work-integrated Lear...
Examining the Value of Learning Analytics for Supporting Work-integrated Lear...Examining the Value of Learning Analytics for Supporting Work-integrated Lear...
Examining the Value of Learning Analytics for Supporting Work-integrated Lear...Vitomir Kovanovic
 
Developing Self-regulated Learning in High-school Students: The Role of Learn...
Developing Self-regulated Learning in High-school Students: The Role of Learn...Developing Self-regulated Learning in High-school Students: The Role of Learn...
Developing Self-regulated Learning in High-school Students: The Role of Learn...Vitomir Kovanovic
 
Unsupervised Learning for Learning Analytics Researchers
Unsupervised Learning for Learning Analytics ResearchersUnsupervised Learning for Learning Analytics Researchers
Unsupervised Learning for Learning Analytics ResearchersVitomir Kovanovic
 
Introduction to R for Learning Analytics Researchers
Introduction to R for Learning Analytics ResearchersIntroduction to R for Learning Analytics Researchers
Introduction to R for Learning Analytics ResearchersVitomir Kovanovic
 
A novel model of cognitive presence assessment using automated learning analy...
A novel model of cognitive presence assessment using automated learning analy...A novel model of cognitive presence assessment using automated learning analy...
A novel model of cognitive presence assessment using automated learning analy...Vitomir Kovanovic
 
Introduction to Learning Analytics
Introduction to Learning AnalyticsIntroduction to Learning Analytics
Introduction to Learning AnalyticsVitomir Kovanovic
 
Understand students’ self-reflections through learning analytics
Understand students’ self-reflections through learning analyticsUnderstand students’ self-reflections through learning analytics
Understand students’ self-reflections through learning analyticsVitomir Kovanovic
 
Assessing cognitive presence using automated learning analytics methods
Assessing cognitive presence using automated learning analytics methodsAssessing cognitive presence using automated learning analytics methods
Assessing cognitive presence using automated learning analytics methodsVitomir Kovanovic
 
Kovanović et al. 2017 - developing a mooc experimentation platform: insight...
Kovanović et al.   2017 - developing a mooc experimentation platform: insight...Kovanović et al.   2017 - developing a mooc experimentation platform: insight...
Kovanović et al. 2017 - developing a mooc experimentation platform: insight...Vitomir Kovanovic
 
Learning Analytics for Communities of Inquiry
Learning Analytics for Communities of InquiryLearning Analytics for Communities of Inquiry
Learning Analytics for Communities of InquiryVitomir Kovanovic
 
A Novel Model of Cognitive Presence Assessment Using Automated Learning Analy...
A Novel Model of Cognitive Presence Assessment Using Automated Learning Analy...A Novel Model of Cognitive Presence Assessment Using Automated Learning Analy...
A Novel Model of Cognitive Presence Assessment Using Automated Learning Analy...Vitomir Kovanovic
 
Towards Automated Classification of Discussion Transcripts: A Cognitive Prese...
Towards Automated Classification of Discussion Transcripts: A Cognitive Prese...Towards Automated Classification of Discussion Transcripts: A Cognitive Prese...
Towards Automated Classification of Discussion Transcripts: A Cognitive Prese...Vitomir Kovanovic
 
What does effective online/blended teaching look like?
What does effective online/blended teaching look like?What does effective online/blended teaching look like?
What does effective online/blended teaching look like?Vitomir Kovanovic
 
MOOCs in the news- A European perspective
MOOCs in the news- A European perspectiveMOOCs in the news- A European perspective
MOOCs in the news- A European perspectiveVitomir Kovanovic
 
Automated Content Analysis of Discussion Transcripts
Automated Content Analysis of Discussion TranscriptsAutomated Content Analysis of Discussion Transcripts
Automated Content Analysis of Discussion TranscriptsVitomir Kovanovic
 

More from Vitomir Kovanovic (20)

Introduction to Learning Analytics for High School Teachers and Managers
Introduction to Learning Analytics for High School Teachers and ManagersIntroduction to Learning Analytics for High School Teachers and Managers
Introduction to Learning Analytics for High School Teachers and Managers
 
Extending video interactions to support self-regulated learning in an online ...
Extending video interactions to support self-regulated learning in an online ...Extending video interactions to support self-regulated learning in an online ...
Extending video interactions to support self-regulated learning in an online ...
 
Analysing social presence in online discussions through network and text anal...
Analysing social presence in online discussions through network and text anal...Analysing social presence in online discussions through network and text anal...
Analysing social presence in online discussions through network and text anal...
 
Automated Analysis of Cognitive Presence in Online Discussions Written in Por...
Automated Analysis of Cognitive Presence in Online Discussions Written in Por...Automated Analysis of Cognitive Presence in Online Discussions Written in Por...
Automated Analysis of Cognitive Presence in Online Discussions Written in Por...
 
Validating a theorized model of engagement in learning analytics
Validating a theorized model of engagement in learning analyticsValidating a theorized model of engagement in learning analytics
Validating a theorized model of engagement in learning analytics
 
Examining the Value of Learning Analytics for Supporting Work-integrated Lear...
Examining the Value of Learning Analytics for Supporting Work-integrated Lear...Examining the Value of Learning Analytics for Supporting Work-integrated Lear...
Examining the Value of Learning Analytics for Supporting Work-integrated Lear...
 
Developing Self-regulated Learning in High-school Students: The Role of Learn...
Developing Self-regulated Learning in High-school Students: The Role of Learn...Developing Self-regulated Learning in High-school Students: The Role of Learn...
Developing Self-regulated Learning in High-school Students: The Role of Learn...
 
Unsupervised Learning for Learning Analytics Researchers
Unsupervised Learning for Learning Analytics ResearchersUnsupervised Learning for Learning Analytics Researchers
Unsupervised Learning for Learning Analytics Researchers
 
Introduction to R for Learning Analytics Researchers
Introduction to R for Learning Analytics ResearchersIntroduction to R for Learning Analytics Researchers
Introduction to R for Learning Analytics Researchers
 
A novel model of cognitive presence assessment using automated learning analy...
A novel model of cognitive presence assessment using automated learning analy...A novel model of cognitive presence assessment using automated learning analy...
A novel model of cognitive presence assessment using automated learning analy...
 
Introduction to Learning Analytics
Introduction to Learning AnalyticsIntroduction to Learning Analytics
Introduction to Learning Analytics
 
Understand students’ self-reflections through learning analytics
Understand students’ self-reflections through learning analyticsUnderstand students’ self-reflections through learning analytics
Understand students’ self-reflections through learning analytics
 
Assessing cognitive presence using automated learning analytics methods
Assessing cognitive presence using automated learning analytics methodsAssessing cognitive presence using automated learning analytics methods
Assessing cognitive presence using automated learning analytics methods
 
Kovanović et al. 2017 - developing a mooc experimentation platform: insight...
Kovanović et al.   2017 - developing a mooc experimentation platform: insight...Kovanović et al.   2017 - developing a mooc experimentation platform: insight...
Kovanović et al. 2017 - developing a mooc experimentation platform: insight...
 
Learning Analytics for Communities of Inquiry
Learning Analytics for Communities of InquiryLearning Analytics for Communities of Inquiry
Learning Analytics for Communities of Inquiry
 
A Novel Model of Cognitive Presence Assessment Using Automated Learning Analy...
A Novel Model of Cognitive Presence Assessment Using Automated Learning Analy...A Novel Model of Cognitive Presence Assessment Using Automated Learning Analy...
A Novel Model of Cognitive Presence Assessment Using Automated Learning Analy...
 
Towards Automated Classification of Discussion Transcripts: A Cognitive Prese...
Towards Automated Classification of Discussion Transcripts: A Cognitive Prese...Towards Automated Classification of Discussion Transcripts: A Cognitive Prese...
Towards Automated Classification of Discussion Transcripts: A Cognitive Prese...
 
What does effective online/blended teaching look like?
What does effective online/blended teaching look like?What does effective online/blended teaching look like?
What does effective online/blended teaching look like?
 
MOOCs in the news- A European perspective
MOOCs in the news- A European perspectiveMOOCs in the news- A European perspective
MOOCs in the news- A European perspective
 
Automated Content Analysis of Discussion Transcripts
Automated Content Analysis of Discussion TranscriptsAutomated Content Analysis of Discussion Transcripts
Automated Content Analysis of Discussion Transcripts
 

Recently uploaded

會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文
會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文
會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文中 央社
 
How To Create Editable Tree View in Odoo 17
How To Create Editable Tree View in Odoo 17How To Create Editable Tree View in Odoo 17
How To Create Editable Tree View in Odoo 17Celine George
 
How to Manage Closest Location in Odoo 17 Inventory
How to Manage Closest Location in Odoo 17 InventoryHow to Manage Closest Location in Odoo 17 Inventory
How to Manage Closest Location in Odoo 17 InventoryCeline George
 
The Story of Village Palampur Class 9 Free Study Material PDF
The Story of Village Palampur Class 9 Free Study Material PDFThe Story of Village Palampur Class 9 Free Study Material PDF
The Story of Village Palampur Class 9 Free Study Material PDFVivekanand Anglo Vedic Academy
 
DEMONSTRATION LESSON IN ENGLISH 4 MATATAG CURRICULUM
DEMONSTRATION LESSON IN ENGLISH 4 MATATAG CURRICULUMDEMONSTRATION LESSON IN ENGLISH 4 MATATAG CURRICULUM
DEMONSTRATION LESSON IN ENGLISH 4 MATATAG CURRICULUMELOISARIVERA8
 
Envelope of Discrepancy in Orthodontics: Enhancing Precision in Treatment
 Envelope of Discrepancy in Orthodontics: Enhancing Precision in Treatment Envelope of Discrepancy in Orthodontics: Enhancing Precision in Treatment
Envelope of Discrepancy in Orthodontics: Enhancing Precision in Treatmentsaipooja36
 
Major project report on Tata Motors and its marketing strategies
Major project report on Tata Motors and its marketing strategiesMajor project report on Tata Motors and its marketing strategies
Major project report on Tata Motors and its marketing strategiesAmanpreetKaur157993
 
Exploring Gemini AI and Integration with MuleSoft | MuleSoft Mysore Meetup #45
Exploring Gemini AI and Integration with MuleSoft | MuleSoft Mysore Meetup #45Exploring Gemini AI and Integration with MuleSoft | MuleSoft Mysore Meetup #45
Exploring Gemini AI and Integration with MuleSoft | MuleSoft Mysore Meetup #45MysoreMuleSoftMeetup
 
The Liver & Gallbladder (Anatomy & Physiology).pptx
The Liver &  Gallbladder (Anatomy & Physiology).pptxThe Liver &  Gallbladder (Anatomy & Physiology).pptx
The Liver & Gallbladder (Anatomy & Physiology).pptxVishal Singh
 
會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽
會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽
會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽中 央社
 
demyelinated disorder: multiple sclerosis.pptx
demyelinated disorder: multiple sclerosis.pptxdemyelinated disorder: multiple sclerosis.pptx
demyelinated disorder: multiple sclerosis.pptxMohamed Rizk Khodair
 
SPLICE Working Group: Reusable Code Examples
SPLICE Working Group:Reusable Code ExamplesSPLICE Working Group:Reusable Code Examples
SPLICE Working Group: Reusable Code ExamplesPeter Brusilovsky
 
MOOD STABLIZERS DRUGS.pptx
MOOD     STABLIZERS           DRUGS.pptxMOOD     STABLIZERS           DRUGS.pptx
MOOD STABLIZERS DRUGS.pptxPoojaSen20
 
Andreas Schleicher presents at the launch of What does child empowerment mean...
Andreas Schleicher presents at the launch of What does child empowerment mean...Andreas Schleicher presents at the launch of What does child empowerment mean...
Andreas Schleicher presents at the launch of What does child empowerment mean...EduSkills OECD
 
How to Analyse Profit of a Sales Order in Odoo 17
How to Analyse Profit of a Sales Order in Odoo 17How to Analyse Profit of a Sales Order in Odoo 17
How to Analyse Profit of a Sales Order in Odoo 17Celine George
 
ĐỀ THAM KHẢO KÌ THI TUYỂN SINH VÀO LỚP 10 MÔN TIẾNG ANH FORM 50 CÂU TRẮC NGHI...
ĐỀ THAM KHẢO KÌ THI TUYỂN SINH VÀO LỚP 10 MÔN TIẾNG ANH FORM 50 CÂU TRẮC NGHI...ĐỀ THAM KHẢO KÌ THI TUYỂN SINH VÀO LỚP 10 MÔN TIẾNG ANH FORM 50 CÂU TRẮC NGHI...
ĐỀ THAM KHẢO KÌ THI TUYỂN SINH VÀO LỚP 10 MÔN TIẾNG ANH FORM 50 CÂU TRẮC NGHI...Nguyen Thanh Tu Collection
 
Spring gala 2024 photo slideshow - Celebrating School-Community Partnerships
Spring gala 2024 photo slideshow - Celebrating School-Community PartnershipsSpring gala 2024 photo slideshow - Celebrating School-Community Partnerships
Spring gala 2024 photo slideshow - Celebrating School-Community Partnershipsexpandedwebsite
 
Improved Approval Flow in Odoo 17 Studio App
Improved Approval Flow in Odoo 17 Studio AppImproved Approval Flow in Odoo 17 Studio App
Improved Approval Flow in Odoo 17 Studio AppCeline George
 
diagnosting testing bsc 2nd sem.pptx....
diagnosting testing bsc 2nd sem.pptx....diagnosting testing bsc 2nd sem.pptx....
diagnosting testing bsc 2nd sem.pptx....Ritu480198
 

Recently uploaded (20)

會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文
會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文
會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文
 
How To Create Editable Tree View in Odoo 17
How To Create Editable Tree View in Odoo 17How To Create Editable Tree View in Odoo 17
How To Create Editable Tree View in Odoo 17
 
How to Manage Closest Location in Odoo 17 Inventory
How to Manage Closest Location in Odoo 17 InventoryHow to Manage Closest Location in Odoo 17 Inventory
How to Manage Closest Location in Odoo 17 Inventory
 
The Story of Village Palampur Class 9 Free Study Material PDF
The Story of Village Palampur Class 9 Free Study Material PDFThe Story of Village Palampur Class 9 Free Study Material PDF
The Story of Village Palampur Class 9 Free Study Material PDF
 
DEMONSTRATION LESSON IN ENGLISH 4 MATATAG CURRICULUM
DEMONSTRATION LESSON IN ENGLISH 4 MATATAG CURRICULUMDEMONSTRATION LESSON IN ENGLISH 4 MATATAG CURRICULUM
DEMONSTRATION LESSON IN ENGLISH 4 MATATAG CURRICULUM
 
Envelope of Discrepancy in Orthodontics: Enhancing Precision in Treatment
 Envelope of Discrepancy in Orthodontics: Enhancing Precision in Treatment Envelope of Discrepancy in Orthodontics: Enhancing Precision in Treatment
Envelope of Discrepancy in Orthodontics: Enhancing Precision in Treatment
 
Major project report on Tata Motors and its marketing strategies
Major project report on Tata Motors and its marketing strategiesMajor project report on Tata Motors and its marketing strategies
Major project report on Tata Motors and its marketing strategies
 
Exploring Gemini AI and Integration with MuleSoft | MuleSoft Mysore Meetup #45
Exploring Gemini AI and Integration with MuleSoft | MuleSoft Mysore Meetup #45Exploring Gemini AI and Integration with MuleSoft | MuleSoft Mysore Meetup #45
Exploring Gemini AI and Integration with MuleSoft | MuleSoft Mysore Meetup #45
 
The Liver & Gallbladder (Anatomy & Physiology).pptx
The Liver &  Gallbladder (Anatomy & Physiology).pptxThe Liver &  Gallbladder (Anatomy & Physiology).pptx
The Liver & Gallbladder (Anatomy & Physiology).pptx
 
會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽
會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽
會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽
 
“O BEIJO” EM ARTE .
“O BEIJO” EM ARTE                       .“O BEIJO” EM ARTE                       .
“O BEIJO” EM ARTE .
 
demyelinated disorder: multiple sclerosis.pptx
demyelinated disorder: multiple sclerosis.pptxdemyelinated disorder: multiple sclerosis.pptx
demyelinated disorder: multiple sclerosis.pptx
 
SPLICE Working Group: Reusable Code Examples
SPLICE Working Group:Reusable Code ExamplesSPLICE Working Group:Reusable Code Examples
SPLICE Working Group: Reusable Code Examples
 
MOOD STABLIZERS DRUGS.pptx
MOOD     STABLIZERS           DRUGS.pptxMOOD     STABLIZERS           DRUGS.pptx
MOOD STABLIZERS DRUGS.pptx
 
Andreas Schleicher presents at the launch of What does child empowerment mean...
Andreas Schleicher presents at the launch of What does child empowerment mean...Andreas Schleicher presents at the launch of What does child empowerment mean...
Andreas Schleicher presents at the launch of What does child empowerment mean...
 
How to Analyse Profit of a Sales Order in Odoo 17
How to Analyse Profit of a Sales Order in Odoo 17How to Analyse Profit of a Sales Order in Odoo 17
How to Analyse Profit of a Sales Order in Odoo 17
 
ĐỀ THAM KHẢO KÌ THI TUYỂN SINH VÀO LỚP 10 MÔN TIẾNG ANH FORM 50 CÂU TRẮC NGHI...
ĐỀ THAM KHẢO KÌ THI TUYỂN SINH VÀO LỚP 10 MÔN TIẾNG ANH FORM 50 CÂU TRẮC NGHI...ĐỀ THAM KHẢO KÌ THI TUYỂN SINH VÀO LỚP 10 MÔN TIẾNG ANH FORM 50 CÂU TRẮC NGHI...
ĐỀ THAM KHẢO KÌ THI TUYỂN SINH VÀO LỚP 10 MÔN TIẾNG ANH FORM 50 CÂU TRẮC NGHI...
 
Spring gala 2024 photo slideshow - Celebrating School-Community Partnerships
Spring gala 2024 photo slideshow - Celebrating School-Community PartnershipsSpring gala 2024 photo slideshow - Celebrating School-Community Partnerships
Spring gala 2024 photo slideshow - Celebrating School-Community Partnerships
 
Improved Approval Flow in Odoo 17 Studio App
Improved Approval Flow in Odoo 17 Studio AppImproved Approval Flow in Odoo 17 Studio App
Improved Approval Flow in Odoo 17 Studio App
 
diagnosting testing bsc 2nd sem.pptx....
diagnosting testing bsc 2nd sem.pptx....diagnosting testing bsc 2nd sem.pptx....
diagnosting testing bsc 2nd sem.pptx....
 

Introduction to Epistemic Network Analysis

  • 1. Introduction to Epistemic Network Analysis Vitomir Kovanovic, University of South Australia #vkovanovic Vitomir.Kovanovic@unisa.edu.au 1
  • 2. What is Epistemic Network Analysis (ENA) • Epistemic Network Analysis is a network-based method for analysing codified data. • Developed by Professor David Shaffer from the University of Wisconsin Madison (UWM) and his team. • There is a web interface and R package • http://epistemicnetwork.org 2
  • 3. The original problem of ENA • Understand how people become professionals • Involves understanding of the ways important concepts –codes– interact together • The applications of ENA expanded far beyond epistemology domain • New term: Quantitative ethnography. • Can be used to understand how different codes co-occur. 3
  • 4. What is Epistemology? Epistemology studies the nature of knowledge, justification, and the rationality of belief New term: quantitative ethnography 4
  • 6. ENA in Education • Often used for understanding of student conversations and discussion messages. • Also used for analysis of interview data. 6
  • 7. Key concepts in ENA • Codes: a set of concepts whose interactions we want to understand • Unit of analysis: objects for which we want to understand interactions between the codes • Stanza(Conversation): Units in which we measure code co- occurrence 7
  • 8. How ENA works: Example dataset 8 • Units of analysis: Individual Students • Stanzas: Individual messages
  • 9. How ENA works: Example dataset 9 • Codes: • Data • Technical Constraints • Performance Parameters • Client and Consultant Requests • Design Reasoning • Collaboration
  • 10. How ENA works: Code co- occurrence matrix 10 • Code co-occurrence in stanzas is used to produce code co-occurrence matrix for each unit of analysis (i.e., person) Data Technical Constraints Performance Parameters Client and Consultant Requests Design Reasoning Collaboration Data / 120 80 323 52 32 Technical Constraints / 23 120 112 32 Performance Parameters / 17 28 152 Client and Consultant Requests / 21 68 Design Reasoning / 12 Collaboration /
  • 11. How ENA works: Code co- occurrence matrix 11 • Code co-occurrence in stanzas is used to produce code co-occurrence matrix for each unit of analysis (i.e., person) C1 C2 C3 C4 C5 C6 C1 / 120 80 323 52 32 C2 / 23 120 112 32 C3 / 17 28 152 C4 / 21 68 C5 / 12 C6 /
  • 12. How ENA works: Code co- occurrence matrix 12 • Code co-occurrence in stanzas is used to produce code co-occurrence matrix for each unit of analysis (i.e., person) C1 C2 C3 C4 C5 C6 C1 / 120 80 323 52 32 C2 / 23 120 112 32 C3 / 17 28 152 C4 / 21 68 C5 / 12 C6 /
  • 13. How ENA works: Matrix to vector 13 • Co-occurrence matrices are “flattened out” into vectors of N(N-1)/2 elements 6*5/2=15 columns (dimensions) C1- C2 C1- C3 C1- C4 C1- C5 C1- C6 C2- C3 C2- C4 C2- C5 C2- C6 C3- C4 C3- C5 C3- C6 C4- C5 C4- C6 C5- C6 U1 120 80 323 52 32 23 120 112 32 17 28 152 21 68 12 C1 C2 C3 C4 C5 C6 C1 / 120 80 323 52 32 C2 / 23 120 112 32 C3 / 17 28 152 C4 / 21 68 C5 / 12 C6 /
  • 14. How ENA works: Matrix to vector 14 • Co-occurrence matrices are “flattened out” into vectors of N(N-1)/2 elements 6*5/2=15 columns (dimensions) C1- C2 C1- C3 C1- C4 C1- C5 C1- C6 C2- C3 C2- C4 C2- C5 C2- C6 C3- C4 C3- C5 C3- C6 C4- C5 C4- C6 C5- C6 U1 120 80 323 52 32 23 120 112 32 17 28 152 21 68 12 C1 C2 C3 C4 C5 C6 C1 / 120 80 323 52 32 C2 / 23 120 112 32 C3 / 17 28 152 C4 / 21 68 C5 / 12 C6 /
  • 15. How ENA works: Matrix to vector 15 • Co-occurrence matrices are “flattened out” into vectors of N(N-1)/2 elements 6*5/2=15 columns (dimensions) C1- C2 C1- C3 C1- C4 C1- C5 C1- C6 C2- C3 C2- C4 C2- C5 C2- C6 C3- C4 C3- C5 C3- C6 C4- C5 C4- C6 C5- C6 U1 120 80 323 52 32 23 120 112 32 17 28 152 21 68 12 C1 C2 C3 C4 C5 C6 C1 / 120 80 323 52 32 C2 / 23 120 112 32 C3 / 17 28 152 C4 / 21 68 C5 / 12 C6 /
  • 16. How ENA works: Matrix to vector 16 • Co-occurrence matrices are “flattened out” into vectors of N(N-1)/2 elements 6*5/2=15 columns (dimensions) C1- C2 C1- C3 C1- C4 C1- C5 C1- C6 C2- C3 C2- C4 C2- C5 C2- C6 C3- C4 C3- C5 C3- C6 C4- C5 C4- C6 C5- C6 U1 120 80 323 52 32 23 120 112 32 17 28 152 21 68 12 C1 C2 C3 C4 C5 C6 C1 / 120 80 323 52 32 C2 / 23 120 112 32 C3 / 17 28 152 C4 / 21 68 C5 / 12 C6 /
  • 17. How ENA works: Matrix to vector 17 • Co-occurrence matrices are “flattened out” into vectors of N(N-1)/2 elements 6*5/2=15 columns (dimensions) C1- C2 C1- C3 C1- C4 C1- C5 C1- C6 C2- C3 C2- C4 C2- C5 C2- C6 C3- C4 C3- C5 C3- C6 C4- C5 C4- C6 C5- C6 U1 120 80 323 52 32 23 120 112 32 17 28 152 21 68 12 C1 C2 C3 C4 C5 C6 C1 / 120 80 323 52 32 C2 / 23 120 112 32 C3 / 17 28 152 C4 / 21 68 C5 / 12 C6 /
  • 18. How ENA works: Matrix to vector 18 • Co-occurrence matrices are “flattened out” into vectors of N(N-1)/2 elements 6*5/2=15 columns (dimensions) C1- C2 C1- C3 C1- C4 C1- C5 C1- C6 C2- C3 C2- C4 C2- C5 C2- C6 C3- C4 C3- C5 C3- C6 C4- C5 C4- C6 C5- C6 U1 120 80 323 52 32 23 120 112 32 17 28 152 21 68 12 C1 C2 C3 C4 C5 C6 C1 / 120 80 323 52 32 C2 / 23 120 112 32 C3 / 17 28 152 C4 / 21 68 C5 / 12 C6 /
  • 19. How ENA works: Matrix to vector 19 • Co-occurrence matrices are converted to vectors and joined together to form Analytic space of N*(N-1)/2 elements C1- C2 C1- C3 C1- C4 C1- C5 C1- C6 C2- C3 C2- C4 C2- C5 C2- C6 C3- C4 C3- C5 C3- C6 C4- C5 C4- C6 C5- C6 U1 120 80 323 52 32 23 120 112 32 17 28 152 21 68 12 U2 U3 … … U20 32 125 112 52 32 17 128 211 54 36 63 85 109 276 42 • NOTE: Each vector is a point in a 15-dimensional space • EACH GRAPH IS A POINT
  • 20. How ENA works: Matrix to vector 20 • Co-occurrence matrices are converted to vectors and joined together to form Analytic space of N*(N-1)/2 elements 1-2 1-3 1-4 1-5 1-6 2-3 2-4 2-5 2-6 3-4 3-5 3-6 4-5 4-6 5-6 U1 120 80 323 52 32 23 120 112 32 17 28 152 21 68 12 U2 U3 … … U20 32 125 112 52 32 17 128 211 54 36 63 85 109 276 42 • NOTE: Each vector is a point in a 15-dimensional space • EACH GRAPH IS A POINT
  • 21. How ENA works: Singular Value Decomposition of Analytic Space 21 • Approximate N columns with a smaller number R of “composite columns” • The whole point is to be able to plot N dimensions on a 2D plot
  • 22. How ENA works: Singular Value Decomposition of Analytic Space 22 • Approximate N columns with a smaller number R of “composite columns” • The whole point is to be able to plot N dimensions on a 2D plot m=1,000 students n=100 edges A=100,000 U = 1,000 x 1,000 = 1,000,000 VT= 100 x 100 = 10,000
  • 23. How ENA works: Singular Value Decomposition of Analytic Space 23 • Approximate N columns with a smaller number R of “composite columns” • The whole point is to be able to plot N dimensions on a 2D plot m=1,000 students n=100 edges A=100,000
  • 24. How ENA works: Singular Value Decomposition of Analytic Space 24 • Approximate N columns with a smaller number R of “composite columns” • The whole point is to be able to plot N dimensions on a 2D plot m=1,000 students n=100 edges A=100,000 r=2 (keep top two singular values) U=1,000 x 2 = 2,000 VT=2 x 100 = 200 Total=2,200
  • 25. How ENA works: Singular Value Decomposition of Analytic Space 25 • Approximate N columns with a smaller number R of “composite columns” • The whole point is to be able to plot N dimensions on a 2D plot m=1,000 students n=100 edges A=100,000 r=2 (singular values) U=1,000 x 2 = 2,000 VT=2 x 100 = 200 Total=2,200 Latent factor scores (student 2D coordinates) Latent factor coefficients (code pair 2D coordinates)
  • 30. Remarks on coding • Code values can be  Boolean: 0 if code does not occurs, 1 if it does  Integer: 0 if code does not occur, N if it does N times  Fractional number: Value indicating “strength” or “association” of the code to the text • In case of binary values, co-occurrence is 1 if both codes occur • In case or integer or fractional numbers, co-occurrence score is the product if the individual scores. • Fractions useful for:  LDA topic modelling:  Each topic is a code, code value are topic associations to individual texts • Integers useful for:  Word count analysis:  Each word (category) is a code, co-occurrence value is the product of code scores. 30
  • 31. Moving stanza • Stanza can be moving, specially useful for conversations where individual messages are too short 31
  • 32. ENA Example 1: CoI + LDA E Ferreira, R., Kovanovic, V., Gasevic, D., & Rolim, V. (2018). Towards Combined Network and Text Analytics of Student Discourse in Online Discussions. In The 19th International Conference on Artificial Intelligence in Education. London, UK. • Understand the development of cognitive presence with respect to different course topics  CoI process model, does not pay attention to course content • Examine the role of instructional intervention of role assignment 32
  • 33. ENA Example 1: CoI + LDA • 1,747 messages from 6 course offers • Each message coded for the level of cognitive presence:  Triggering Event  Exploration  Integration  Resolution  Other • Applied topic modelling to pick course topics  Extracted topics were corresponding to course topics  + one topic regarding logistics 33
  • 34. Results: Projection graph all students 34
  • 35. Results: Projection graph (intervention + control groups) 35 Control Intervention
  • 36. Results: ENA network model for all students 36All students
  • 37. Results: ENA network model for two student groups 37Control Intervention
  • 40. Topics per cluster Cluster 1 Cluster 2 Cluster 3 Cluster 4
  • 41. Social centrality per ENA cluster
  • 42. Recap • ENA works on codified data • We need to define  Codes  Units  Stanzas • Unit’s co-occurrence matrices are converted to vectors • All unit’s merged to form Analytic space matrix • Analytic space is reduced to 2D with SVD • Plot units on the 2D plot • Plot codes on the 2D plot 42
  • 43. Practical example • Data: download from http://bit.ly/enanie • Go to http://epistemicnetwork.org and create account 43