rethinking dropout in online higher education: the case of the universitat oberta de catalunya
TRANSCRIPT
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Rethinking Dropout in Online
Higher Education: the case of the
Universitat Oberta de Catalunya
Josep Grau-Valldosera, Julià Minguillón
Grau-Valldosera, J., & Minguillón, J. (2014). Rethinking dropout in online higher education: The case of
the Universitat Oberta de Catalunya. The International Review Of Research In Open And Distributed
Learning, 15(1). Retrieved from http://www.irrodl.org/index.php/irrodl/article/view/1628
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Rethinking dropout
• Quantifying the problem
• Taking a break or dropping out?
The model
• Enrollment patterns
• Evidences from data
Summary
• Conclusions
• Current and future work
Index
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Rethinking Dropout in Online Higher Education: the case of the UOC
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1. Rethinking dropout
• How do we define dropout in an open university?
• Quantifying the problem
• Taking a break or dropping out?
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Rethinking Dropout in Online Higher Education: the case of the UOC
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1.1. Quantifying the problem
Rethinking Dropout in Online Higher Education: the case of the UOC
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1.1. Quantifying the problem
27.8%
Rethinking Dropout in Online Higher Education: the case of the UOC
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1.1. Quantifying the problem
Economic and
social costs
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1.2. Taking a break or dropping out?
Bologna (EHEA) degrees (Catalan), 2008/1 – 2013/2
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1.2. Taking a break or dropping out?
Rethinking Dropout in Online Higher Education: the case of the UOC
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2. The model
• Empirical approach based on historical enrollment
data
• Enrollment patterns
• Evidences from data
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2.1. Enrollment patterns
Students are aligned starting from their first
semester:
1 1 1 Student with three consecutive semesters
1 0 1 1 Student taking a break the 2nd semester
1 1 0 0 0 0 Student with four consecutive breaks
:
:
1 ... 1 0 ... 0 1 How many consecutive ‘0’ before ‘1’?
P(Xi=1 | Xi-1, ..., Xi-N=0) < ε
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2.2. Evidences from data
Data from LRU (IRRODL paper):
ε = 1% N varies from 5 to 12 semesters
ε = 5% N varies from 3 to 5
ε = 10% N varies from 2 to 3
But using EHEA data shows that N=1 or 2 (10%)!
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3. Summary
• Conclusions
• Current and future work
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3.1. Conclusions
• No need to use the official dropout definition
• Small window (N=2 or 3) for reasonable Type I
error (10%)
• Recent data (EHEA) shows that taking a break is
almost the same than dropping out!
• Enrollment patterns show dropout is a decaying
phenomenon, but highly accumulative
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3.2. Current and future work
• N=2 “ensures” a student is a dropout • Build predictive models using data from the 1st semester
• Learner background
• Enrollment data
• Navigational and interaction patterns
• Formative evaluation
• Academic performance
• But N=1 is a very good predictor as well • Ask students taking 2nd semester breaks
• Actions for “waking up” true breaks
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