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A Problem-Oriented Method
for Supporting AEH Authors
through Data Mining
Javier Bravo1, César Vialardi2, Alvaro Ortigosa1
1Universidad
Autónoma de Madrid
2Universidad de Lima
[email protected]
OPAH Research group
http://tangow.ii.uam.es/opah
The Context: course construction
 The teacher defines and organizes the
activities according to the user needs
Using an authoring tool
Based on her designs
Javier Bravo - Universidad
Autónoma de Madrid
Course definition
(activities and adaptation rules)
AHS Development model vs problems
Course delivering system
Students
Course
definition
Authoring Tool
Javier Bravo - Universidad
Autónoma de Madrid
I don’t understand
the results of data
mining tools !!
Instructor
User Model
Data Mining Tools
Proposed Method
A
A1
C4.5
C
B
A2
D
E
filter
log-file fitered
decision tree (DT)
log-file
•Goal: find a given symthoms related
to problems in adaptation rules or
contents.
•Solving these problems the
instructor can improve the course
definition.
Javier Bravo - Universidad
Autónoma de Madrid
A
profile
activity
A1
D
E
dectected symthoms
Path of DT
Example: preprocessing logs
userId,age,language,experience,activity,complete,grade,type,typeActivity,success
e0,young,spanish,novice,traffic,0.0,0.0,START-SESSION,T,no
e0,young,spanish,novice,EduVial,0.0,0.0,FIRSTVISIT,T,no
e0,young,spanish,novice,EduVial,0.0,0.0,LEAVE-COMPOSITE,T,no
e0,young,spanish,novice,Req_Conductor,0.0,0.0,FIRSTVISIT,T,no
e0,young,spanish,novice,Req_Conductor,1.0,1.0,LEAVE-ATOMIC,T,yes
e0,young,spanish,novice,Via,0.0,0.0,FIRSTVISIT,T,no
e0,young,spanish,novice,Via,1.0,1.0,LEAVE-ATOMIC,T,yes
e0,young,spanish,novice,Conduccion,0.0,0.0,FIRSTVISIT,T,no
e0,young,spanish,novice,Conduccion,1.0,1.0,LEAVE-ATOMIC,T,yes
e0,young,spanish,novice,S_Signs,0.0,0.0,FIRSTVISIT,T,no
e0,young,spanish,novice,S_Signs,0.0,0.0,LEAVE-COMPOSITE,T,no
e0,young,spanish,novice,S_Ag_Exer,0.0,0.0,FIRSTVISIT,P,no
e0,young,spanish,novice,S_Ag_Exer,1.0,1.0,LEAVE-ATOMIC,P,yes
 For
Filterthis
(out):
experiment synthetic log files generated by
Simulog
are used.
 Type different
of LEAVE-ATOMIC.
 TypeActivity different of P.
Javier Bravo - Universidad
Autónoma de Madrid
Example: decision tree
activity
=S_Ag_Exer
language
=Spanish
experience
=novice
=English
yes (69/0)
=S_Circ_Exer
=S_Vert_S_Exer
=S_Vert_M_Exer
yes (240/0)
yes (240/0)
yes (240/0)
=German
yes (83/0)
=advanced
Profile
age
Activity
yes (46/0)
=young
no (34 / 8)
=old
yes (8 / 0)
Javier Bravo - Universidad
Autónoma de Madrid
young
novice
Spanish
Detected problems
S_Ag_Exer
Other example: decision tree
age
=old
=young
activity
yes (948 / 288)
=S_Circ_Exer
=S_Vert_S_Exer
=S_Vert_M_Exer
=S_Ag_Exer
language
yes (243 / 77)
=English =Spanish =German
yes (129 / 34)
experience
=advanced
yes (12 / 3)
Javier Bravo - Universidad
Autónoma de Madrid
=English
yes (25 / 9)
=novice
no (77 / 22)
yes (243 / 75)
language
=Spanish
experience yes (89 / 27)
=advanced
yes (12 / 4)
=German
yes (25 / 8)
=novice
no (117 / 28)
Problems in the activities S_Ag_Exer and S_Circ_Exer
Conclusions
 Decision tree technique is a useful method for
detecting potential problems in the course
definition on AEH systems.
 Capability of the tree for showing symptoms
of bad adaptation is more important than the
accuracy of the tree.
 Weak point:

Information in the tree is not always complete.
 Use this method with other data mining
techniques: association rules, clustering, …
Javier Bravo - Universidad
Autónoma de Madrid
Future Work
 Test the combination of this method with
other techniques.
 Find the threshold index of failures that
indicates a symptom of bad adaptation.

37º is fever?
 Develop an evaluation tool that implements
this method: ASquare (Author Assistant).
Javier Bravo - Universidad
Autónoma de Madrid
Thank you!
Questions
Javier Bravo - Universidad
Autónoma de Madrid
Synthetic log approach
ST = DS ?
ST:
Symthoms
to be tested
Adaptation
Simulog
Engine
UM
Students
Evaluation Tool
Javier Bravo - Universidad
Autónoma de Madrid
DS: Detected
Symthoms
DS: Detected
Symthoms
Instructor
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