rethinking dropout in online higher education: the case of the universitat oberta de catalunya

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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http://www.irrodl.org/index.php/irrodl/article/view/1628

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

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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1.1. Quantifying the problem


CC

-BY

Este

r U

sarr

ald

e

5


27.8%


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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?


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%)!


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
