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Rethinking dropout in online higher education: The case of the Universitat Oberta de Catalunya

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In recent years, several studies have been carried out into the reasons why students drop out of online higher education. However, more effort has gone into analyzing the causes of this phenomenon than into trying to characterize students who drop out, that is defining what a dropout is.
As one of the
main findings of this article, the authors (Josep Grau-Valldosera and Julià Minguillón) reach a pure empirical definition, at a programme level, of students who drop out of an online higher education context with non-mandatory enrolment. This definition is based on the probability of students not continuing a specific academic programme following several consecutive semesters of “theoretical break”, and is highly adaptable to institutions offering distance education with no permanence requirements. Analyzing the reasons behind these facts should help higher education institutions to make more sound and efficient decisions.

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Rethinking dropout in online higher education: The case of the Universitat Oberta de Catalunya

  1. 1. 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 1
  2. 2. 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 2 Rethinking Dropout in Online Higher Education: the case of the UOC
  3. 3. 1. Rethinking dropout • How do we define dropout in an open university? • Quantifying the problem • Taking a break or dropping out? 3 Rethinking Dropout in Online Higher Education: the case of the UOC
  4. 4. 4 1.1. Quantifying the problem Rethinking Dropout in Online Higher Education: the case of the UOC CC-BYEsterUsarralde
  5. 5. 5 1.1. Quantifying the problem 27.8% Rethinking Dropout in Online Higher Education: the case of the UOC
  6. 6. 6 1.1. Quantifying the problem Economic and social costs Rethinking Dropout in Online Higher Education: the case of the UOC
  7. 7. 7 1.2. Taking a break or dropping out? Bologna (EHEA) degrees (Catalan), 2008/1 – 2013/2 Rethinking Dropout in Online Higher Education: the case of the UOC
  8. 8. 8 1.2. Taking a break or dropping out? Rethinking Dropout in Online Higher Education: the case of the UOC
  9. 9. 2. The model • Empirical approach based on historical enrollment data • Enrollment patterns • Evidences from data 9 Rethinking Dropout in Online Higher Education: the case of the UOC
  10. 10. 10 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 Rethinking Dropout in Online Higher Education: the case of the UOC
  11. 11. 11 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%)! Rethinking Dropout in Online Higher Education: the case of the UOC
  12. 12. 3. Summary • Conclusions • Current and future work 12 Rethinking Dropout in Online Higher Education: the case of the UOC
  13. 13. 13 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 Rethinking Dropout in Online Higher Education: the case of the UOC
  14. 14. 14 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 Rethinking Dropout in Online Higher Education: the case of the UOC

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