Elsevier

Computers & Education

Volume 53, Issue 3, November 2009, Pages 563-574
Computers & Education

Factors influencing university drop out rates

https://doi.org/10.1016/j.compedu.2009.03.013Get rights and content

Abstract

This paper develops personalized models for different university degrees to obtain the risk of each student abandoning his degree and analyzes the profile for undergraduates that abandon the degree. In this study three faculties located in Granada, South of Spain, were involved. In Software Engineering three university degrees with 10,844 students, in humanities nineteen university degrees with 39,241 students and in Economic Sciences five university degrees with 25,745 students were considered. Data, corresponding to the period 1992 onwards, are used to obtain a model of logistic regression for each faculty which represents them satisfactorily. These models and the framework data show that certain variables appear repeatedly in the explanation of the drop out in all of the faculties. These variables are, among others, start age, the father’s and mother’s studies, academic performance, success, average mark in the degree and the access form and in some cases also, the number of rounds needed to pass. Students with weak educational strategies and without persistence to achieve their aims in life have low academic performance and low success rates and this implies a high risk of abandoning the degree. The results suggest that each university centre could consider similar models to elaborate a particular action plan to help lower the drop out rate reducing costs and efforts. As concluded in this paper, the profile of the students who tend to abandon their studies is dependent on the subject studied. For this reason, a general methodology based on a Data Warehouse architecture is proposed. This architecture does most of the work automatically and is general enough to be used at any university centre because it only takes into account the usual data the students provide when registered in a course and their grades throughout the years.

Introduction

Higher education in Spain has recently been undergoing an important process of restructuring because of the need to converge with other members of the European Union and to introduce information and communication technologies (ICT) in the teaching processes. These changes demand certain reforms in order to adapt the goals of the institution to the new social needs.

The increasing interest in studying university drop out comes from the increase of cases registered in the Spanish universities together with the elevated cost that the education of every undergraduate means to Public Administration. According to the statistics of the Spanish Coordination University Council (National Evaluation’s Plan of the Quality of the Universities, PNECU), presented in December 2002, 26% of the undergraduates leave their studies or change their degrees. The data provided by the Organization for Cooperation and Economic Development (OCDE), presented the same year, show that the academic failure in Spain is set at over 50%, related fundamentally to the rates of drop out. With other data provided by the Spanish Center of Research, Documents and MEC’s Evaluation (CIDE), MEC (Spanish Department of Education and Science), drop out rates are set between 30% and 50%. This phenomenon began in the rest of Europe rather than in Spain, reaching 45% in Austria. According to “The Standards for Educational and Psychological Testing, 19991, every year the percentage of students that abandons their studies or changes the degree increases, results obtained as from the analysis of the data of the rates registered in the college. Other studies accomplished in Central Europe and the United States, show a similar percentage, although these studies are made with minority populations and perhaps this is the explanation for a bigger level of drop out (see Callejo, 2001, Feldman, 2005, Last and Fulbrook, 2003, Orazem, 2000).

As far as 1968, Rubio García Mina carried out one of the first studies on the university drop out in Spain and analyzed the cohorts from 1960 to 1966 in the technical superior schools of Madrid. This study and others were approximations to an incipient phenomenon, coinciding with some institutional reforms and social changes, like the access of a bigger percentage of students to the university, implantation of the Spanish law for the General Organizing of the Educational System (LOGSE), the reform of University Curriculums, new requirements of higher education (new methodologies, new technologies, practices at companies), etc. The disconnection between the laws of obligatory education with the university study programs, and the strong linkage of these with the business world, together with other institutional circumstances that did not suit the new student’s characteristics well enough, have had the effect of a great increase in the percentage of drop out, specially in technical degrees. These circumstances together with the overcrowding also produce in humanities and social sciences. The problem is unsolved, because every year the drop out rates at all the universities, and in all the degrees, increase, although the differences between all of them continue being significant.

Section snippets

Previous work

There are several attempts to build theoretic models that explain the phenomenon of drop out from the university studies. The majority of them reveal a series of common characteristics and centre their analyzes in the following groups of variables: the student body, the teaching staff, the institution and the family contexts. For several authors including Forbes and Wickens (2005), the students’ decision of changing or continuing his formative university process is determined basically by the

Materials and methods

There are many alternatives for studying the drop out phenomenon. The easiest and most common way is to take some surveys of the students from time to time. The problem of this alternative is that it is very intrusive. It requires the participation of the students and the processing of the collected data. For these reason this alternative usually considers only a small part of the students. On the other hand, we can make use of the personal information the students provide when registered in

Student data collection and statistical analysis

The population under investigation consisted of all the students signed on the following three faculties of the University of Granada from 1992 onwards: Software Engineering, Humanities and Economic Sciences. These faculties include 27 degrees and have had 75,830 students signed on. Every year the students fill in the registration with personal information (family name, first name, address, city, country, sex, age, birth, etc.) and with the subjects interested in studying. This information

Results

In each faculty, a first step after the imputation procedure was to achieve a descriptive analysis of the variables in the study and analyze the relationships between “abandon” and the remaining variables. For the last purpose, chi-square testing and contingence tables were employed. This analysis showed differences between the group of students that abandon the degree and the rest of students for all the variables considered. Following, by the partial method and using stepwise as method of

Discussion

Behind the observed results, we could verify that the rate of drop out in the different disciplines studied is over 40% and even exceeds 60% in the case of Humanities. This corroborates the data of the Spanish Ministry of Education that calculates the rates of drop out at the Spanish universities at around 40% rising, and, in the case of humanities and the technical sciences like Software Engineering, registered rates are higher. Besides these data coincide with various reports of the

Conclusions

Summing up, the conclusions that we obtain after analyzing all variables of our study are the following:

  • Nowadays the rates of drop out of our students are higher than other previous studies reflected.

  • In many instances, factors associated with drop out have a multi-causal nature, and they are related as much with psychological, vital, generational characteristics as with the student’s educational characteristics.

  • Practically the totality of the considered variables evidences a significant

Acknowledgements

We are grateful to the referees for their constructive comments and to Mª Carmen Aguilera and Mª Belén García for their assistance with the manuscript. This work has been supported by the Spanish Research Program under projects EA-2007-0228 and TIN2005-09098-C05-03 and by the

Research Program under project GR2007/07-2.

References (26)

  • L. Last et al.

    Why do student nurses leave? Suggestions from a Delphi study

    Nurse Education Today

    (2003)
  • C.B.M.J. Martins et al.

    Factors influencing the adoption of the internet as a teaching tool at foreign language schools

    Computer and Education

    (2004)
  • F. Araque et al.

    Data warehousing for improving web based learning sites

    International Journal of Emerging Technologies in Learning

    (2007)
  • F. Araque et al.

    E-learning platform as a teaching support in psychology

    Lecture Notes in Computer Science

    (2007)
  • J. Braak

    Factors influencing the use of computer mediated communication by teachers in secondary schools

    Computer and Education

    (2001)
  • J. Callejo

    A cohorty study on UNED students: An approximation to drop-out analysis

    Revista Iberoamericana de Educación a Distancia

    (2001)
  • R.S. Feldman

    Improving the first year of college: Research and practice

    (2005)
  • A. Forbes et al.

    A good social live helps students to stay the course

    Times High Education Supplement

    (2005)
  • L. Fortin et al.

    Typology of student at risk of dropping out of school: Description by personal, family and school factors

    European Journal of Psychology of Education

    (2006)
  • D.W. Hosmer et al.

    Applied logistic regression. Textbook and solutions manual

    (2004)
  • W.H. Inmon

    Building the data warehouse

    (2005)
  • R. Kimball et al.

    The data warehouse tool kit: The complete guide to dimensional modelling

    (2002)
  • M.J. Kirton

    Transitional factors influencing the academic persistence of first semester undergraduate freshmen

    Dissertation Abstracts International Section A: Humanities and Social Sciences

    (2000)
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