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A personalized early warning system
for supporting learners:
the UOC case
David Bañeres (dbaneres@uoc.edu)
Universitat Oberta de Catalunya
UOC
Virtual Campus
StaffFaculty
Students
UOC
Virtual Campus
StaffFaculty
Students
- Provide support
- Manage programmes
- Manage system
- Solve questions
- Provide support
- Prepare activities
- Assess activities
- Generate resources
- …- …
• Some repetitive tasks can be (semi)-automated
Potential enhancement:
• We can provide a real 24x7 support to students
• We can provide a better personalized support
LIS: Learning Intelligent System
• Project supported by the eLearn Center through the New Goals call
• 3 years project
• A research group composed of 4 members
Principal Aim
To develop an adaptive
system to be globally
applicable at UOC campus to
help students to succeed in
their learning process.
Predictive
Analytics
Early
Warning
System
Automated
Recommendations
Automated
feedback
My
Checklist
Gamified
Interface
Research topics
Predictive
Analytics
Early
Warning
System
Automated
Recommendations
Automated
feedback
My
Checklist
Gamified
Interface
Research topics
Outline
• GAR predictive model
• Infrastructure
• Dashboards
• Nudgeting system
Outline
• GAR predictive model
• Infrastructure
• Dashboards
• Nudgeting system
CAA1
CAA2
…
CAAn
?
Pass
Course?
Grade CAA1
Grade CAA1, Grade CAA2
Grade CAA1, Grade CAA2,…,
Grade CAAn
Students at-risk identification model v1
CAA1
CAA2
…
CAAn
?
Pass
Course?
Accuracy
Detect At-risk 56% 87% 91%
Acc. Detect
Non-at-risk 93% 95% 97%
Students at-risk identification model v1
CAA1
CAA2
…
CAAn
?
Pass
Course?
Grades CAANewbie Student
Enrolled Courses Semester
Failed Times Course
GPA
Profile
Student
+
Students at-risk identification model v2
56% 87% 91%
93% 95% 97%
CAA1
CAA2
…
CAAn
?
Pass
Course?
Accuracy
Detect At-risk 81% 88% 92%
Acc. Detect
Non-at-risk 92% 92% 96%
Profile
Student
Students at-risk identification model v2
Accuracy
Threshold
High quality
Low quality
Relevancy of accuracy on real setting
Model
for a CAA
Accuracy
Threshold
High quality
Low quality
Relevancy of accuracy on real setting
Model
for a CAA
Accuracy
Threshold
High quality
Low quality
Relevancy of accuracy on real setting
Relevancy of accuracy on real setting
Model
for a CAA
75%
High quality
Low quality
Accuracy
Threshold
Outline
• GAR predictive model
• Infrastructure
• Dashboards
• Nudgeting system
Infrastructure
Infrastructure
Institutional
data mart
Infrastructure
Computational
service
Infrastructure
Informational
service
Infrastructure
Desanonamyzer
service
Infrastructure
Nudgeting
service
Outline
• GAR predictive model
• Infrastructure
• Dashboards
• Nudgeting system
Grade
CAA1
Grade
CAA2
Pass?
B A PASS
B B PASS
B C+ FAIL
B C- FAIL
B D FAIL
B N FAIL
CAA2
Student’s dashboard v1
Fail Pass
Student’s dashboard v2
Grade
CAA1
Grade
CAA2
Pass?
B A PASS
B B PASS
B C+ FAIL
B C- FAIL
B D FAIL
B N FAIL
CAA2
Student’s dashboard v2
Teacher dashboards
Teacher dashboards
Teacher dashboards
Recovered
Students
Outline
• GAR predictive model
• Infrastructure
• Dashboards
• Nudgeting system
Consecutive
non-submitted
One activity
non-submitted
Likelihood to fail May fail but low
accuracy model
May pass but low
accuracy model
Likelihood to passPass the activity but
grade lower than
prediction
Prediction feedback
Nudgeting system
Consecutive
non-submitted
One activity
non-submitted
Submit activity
Informational feedback
Nudgeting system
Not accessed
on activity
Not yet
submitted
activity
Already submitted
the activity
Remainder
Nudgeting system
Lessons learned
● Predictive model needs further analysis to be applied on real setting
● Infrastructure solves particular UOC setting
● Is the EWS/nudgeting system really useful?
UOCresearch
@UOC_research
http://lis-project.research.uoc.edu/
Welcome LIS at UOC,
we expect that you succeed
as students do.

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