The document discusses educational data mining (EDM) and activity traces. EDM is defined as applying data mining methods and techniques to data from educational environments to identify significant patterns. Activity traces are detailed logs of all interactions with a learning system that can provide temporal, action, and context information. EDM can help improve student models, discover domain knowledge structures, assess pedagogical supports, and identify empirical evidence. Tools like UnderTracks can be used to explore educational data and better understand learning processes. The workshop aims to design EDM tools tailored to specific educational data sources and intended applications.

1. KNOWLEDGE MODELING FROM
ACTIVITY TRACES WORKSHOP
9th Joint Europpean TEL Summer School 2013
Ben-Manson TOUSSAINT Antigoni PARMAXI
Ricardo KAWASE Vanda LUENGO

2. QUESTIONS TO BE ANSWERED
 What is Educational Data Mining?
 Related techniques, methods and tools?
 How EDM helps in education?
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3. EDUCATIONAL DATA MINING
Application of Data Mining methods
and techniques on top of data
coming from educational
environments.
In simpler words: it is all about identifying significant patterns in large
educational datasets.
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4. QUID SIGNIFICANT PATTERNS?
Co-occurrences: association rule
mining, sequence mining.
Actions A is always followed by decision D;
people that decides D often do B.
Clusters: cluster analysis
Analyzed events have enough distinctive
characteristics to be split into specific sets
Classification: classification rule
discovery.
Elements are identified considering
characteristics of predefined sets
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5. QUID SIGNIFICANT PATTERNS?
Co-occurrences: association rule
mining, sequence mining.
Actions A is always followed by decision D;
people that decides D often do B.
Clusters: cluster analysis
Analyzed events have enough distinctive
characteristics to be split into specific sets
Classification: classification rule
discovery.
Elements are identified considering
characteristics of predefined sets
COUNTING
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6. EDM CLASSES OF METHODS
 Prediction
 Clustering
 Relationship Mining
 Discovery with Models
 Distillation of Data For Human Judgment
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7. EDM CLASSES OF METHODS
 Outlier Detection
 Social Network Analysis
 Process Mining
 Text Mining
 Knowledge Tracing
 Non-negative Matrix Factorization
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8. WHY « ACTIVITY TRACES »?
Activity traces are logs of possibly ALL
the interactions with a learning system.
Everything is/can be recorded: temporal
information, actions, learning context,
task context, etc.
Detailed
Multi
sourced
Diverse
formats
Diverse
structures
Fine
grained
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9. WHY « ACTIVITY TRACES »?
Activity traces are logs of possibly ALL
the interactions with a learning system.
Everything is/can be recorded: temporal
information, actions, learning context,
task context, etc.
Better than simply judge
performance
GOOD
BAD
15/20
A+
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10. TELEOS: Technology Enhanced Learning
Environment for Orthopedic Surgery
EXAMPLE OF ACTIVITY TRACES
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11. Timecode;Action;cam3Dx;cam3Dy;cam3Dz;focale3Dx;focale3Dy;focale3Dz;camFacex;camFacey;camFacez;focaleFacex;foc
aleFacey;focaleFacez;camProfilx;camProfily;camProfilz;focaleProfilx;focaleProfily;focaleProfilz;posPoigneex;posPoigneey;p
osPoigneez;posOutilx;posOutily;posOutilz;orientOutilx;orientOutily;orientOutilz;orientOutilw;vitessex;vitessey;vitessez;forc
ex;forcey;forcez;repereTp1x;repereTp1y;repereTp1z;repereTp2x;repereTp2y;repereTp2z;repereDp1x;repereDp1y;repereDp1z
;repereDp2x;repereDp2y;repereDp2z;repereGp1x;repereGp1y;repereGp1z;repereGp2x;repereGp2y;repereGp2z
1306746656792;Definir_Profil;211.30980894338197;-1499.741081765865;114.75;181.640718;200.0;114.75;181.640718;-
252.929817;114.75;181.640718;126.46490849999999;114.75;617.5784412;126.46490849999999;114.75;181.640718;126.46490849
999999;114.75;177.38360449223399;-
116.84233604394854;86.0625;335.13844371935056;39.32833600862352;115.18934436100506;-0.38940225319420335;-
0.6044420948782265;-
0.3785297392171276;0.5828643802475699;0.0;0.0;0.0;0.0;0.0;0.0;0.0;0.0;114.75;363.281436;0.0;114.75;242.187624;0.0;0.0;242.18
7624;0.0;229.5;121.093812;0.0;0.0;121.093812;0.0;229.5;211.30980894338197;-1499.741081765865;114.75
1306746683828;Definir_Face;-1499.741081765865;96.79581755661803;114.75;200.0;126.46490849999999;114.75;-
8.056644749999975;-
166.2890692002577;263.9156020790058;181.640718;126.46490849999999;114.75;617.5784412;126.46490849999999;114.75;181.
640718;126.46490849999999;114.75;177.38360449223399;-
116.84233604394854;86.0625;335.138902345843;39.328157607227496;115.08174787359914;-0.38949267224301576;-
0.6040070556569312;-
0.37843767748992585;0.5833145496172669;0.0;0.0;0.0;0.0;0.0;0.0;0.0;0.0;114.75;363.281436;0.0;114.75;242.187624;0.0;0.0;242.1
87624;0.0;229.5;121.093812;0.0;0.0;121.093812;0.0;229.5;-1499.741081765865;96.79581755661803;114.75
1306746725406;Pousser_Trocart;193.85740250609845;-699.8933866094736;114.75;181.640718;0.0;114.75;214.70714709549637;-
251.42854175699;121.34615471018392;181.640718;126.46490849999999;114.75;617.5784412;126.46490849999999;114.75;181.6
40718;126.46490849999999;114.75;177.38360449223399;-
116.84233604394854;86.0625;210.90728583260932;90.48682215534416;156.15217930009555;-0.0903389292353134;-
0.5988323953428804;-
0.06631379234334253;0.7929950335250877;0.09338127076625824;7.403165340423584;15.029462814331055;0.49065532013460
71;-0.16106373693968656;-
0.22894717686706523;0.0;0.0;114.75;363.281436;0.0;114.75;242.187624;0.0;0.0;242.187624;0.0;229.5;121.093812;0.0;0.0;121.093
812;0.0;229.5;193.85740250609845;-699.8933866094736;114.75
TELEOS: Technology Enhanced Learning
Environment for Orthopedic Surgery
EXAMPLE OF ACTIVITY TRACES
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12. Definitely helpful considering the amount of data
Can find information that you were not aware of
Can identify outliers
Can identify slight differences between patterns
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13. ON SECOND THOUGHT, EDM IS
PROBABLY MORE THAN COUNTING
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14. 28/05/2013
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15. WELL! WHAT’S THE POINT
How can EDM concretely help in education
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16. 1.Improvement
of student
models
Student’s …
Current
knowledge
…Motivation
…Meta-
cognition
…Behavior
2. Discovery or
improvement of
models of a
domain’s knowledge
structure
Interrelationships
of knowledge in a
domain
Automatic
discovery
of domain
structures
3. Study of
pedagogical
support
Discovery of
effective type of
pedagogical
support
4. Highlighting
of empirical
evidence
Extension of
educational
theories
Key factors
impacting
learning
Design of
better learning
systems
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17. 1.Improvement
of student
models
Student’s …
Current
knowledge
…Motivation
…Meta-
cognition
…Behavior
2. Discovery or
improvement of
models of a
domain’s knowledge
structure
Interrelationships
of knowledge in a
domain
Automatic
discovery
of domain
structures
3. Study of
pedagogical
support
Discovery of
effective type of
pedagogical
support
4. Highlighting
of empirical
evidence
Extension of
educational
theories
Key factors
impacting
learning
Developing methods for
exploring educational data
and better understand
students, and the settings
which they learn in
www.educationaldatamining.org
Design of
better learning
systems
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18. Right dataset
Long dataset
Wide dataset
Detailed
dataset
[Educational] data mining tools and
techniques are generally appropriate on:
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19. SHARING EDUCATIONAL
RESOURCES
® http://writetodone.com
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20. SOME TOOLS
UnderTracks
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22. What kind of educational data do you deal with?
 Source.- social network, web learning environment, MOOCS Intelligent Tutoring
System, etc.
 Volume.- huge, wide…
 Type.- numerical, textual, alphanumerical
 Time information.- granularity, no time traces, etc.
 Multi-flowed.- are traces for a same activity split into different flows?
 Heterogeneity: structure, formats, temporal granularity...
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23. Let’s design the EDM tool
that fit your need
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24. What application do you target? Why?
 Improve student models
 Discover/improve models of domain’s knowledge structure
 Assess pedagogical supports
 Identify empirical evidence
What features?
 Classes of methods: ____________________________________
 Preprocessing tasks: ____________________________________
 Visualization tasks: _____________________________________
 Mining features: ________________________________________
What results are you expecting? For what purposes?
(in a few words)
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