María Esteban

Comparison of Personal, Social and Academic Variables Related to University Drop-out and Persistence

Dropping out of university has serious consequences not only for the student who drops out but also for the institution and society as a whole. Although this phenomenon has been widely studied, there is a need for broader knowledge of the context in which it occurs. Yet research on the subject often focuses on variables that, although they affect drop-out rates, lie beyond a university’s control. This makes it hard to come up with effective preventive measures. That is why a northern Spanish university has undertaken a ex post facto holistic research study on 1,311 freshmen (2008/9, 2009/10, and 2010/11 cohorts). The study falls within the framework of the ALFA-GUIA European Project and focuses on those drop-out factors where there is scope for taking remedial measures. This research explored the possible relationship of degree drop-out and different categories of variables: variables related to the educational stage prior to university entry (path to entry university and main reason for degree choice), variables related to integration and coexistence at university (social integration, academic integration, relationships with teachers/peers and value of the living environment) financial status and performance during university studies (in terms of compliance with the program, time devoted to study, use of study techniques and class attendance). Descriptive, correlational and variance analyses were conducted to discover which of these variables really distinguish those students who drop-out from their peers who complete their studies. Results highlight the influence of vocation as main reason for degree choice, path to university entry, financial independency, social and academic adaptation, time devoted to study, use of study techniques and program compliance in the studied phenomenon.

Procrastinating Behavior in Computer-Based Learning Environments to Predict Performance: A Case Study in Moodle

Introduction: Research about student performance has traditionally considered academic procrastination as a behavior that has negative effects on academic achievement. Although there is much evidence for this in class-based environments, there is a lack of research on Computer-Based Learning Environments (CBLEs). Therefore, the purpose of this study is to evaluate student behavior in a blended learning program and specifically procrastination behavior in relation to performance through Data Mining techniques. Materials and Methods: A sample of 140 undergraduate students participated in a blended learning experience implemented in a Moodle (Modular Object Oriented Developmental Learning Environment) Management System. Relevant interaction variables were selected for the study, taking into account student achievement and analyzing data by means of association rules, a mining technique. The association rules were arrived at and filtered through two selection criteria: 1, rules must have an accuracy over 0.8 and 2, they must be present in both sub-samples. Results: The findings of our study highlight the influence of time management in online learning environments, particularly on academic achievement, as there is an association between procrastination variables and student performance. Conclusion: Negative impact of procrastination in learning outcomes has been observed again but in virtual learning environments where practical implications, prevention of, and intervention in, are different from class-based learning. These aspects are discussed to help resolve student difficulties at various ages.

Corrigendum: Comparison of Personal, Social and Academic Variables Related to University Drop-out and Persistence

[This corrects the article on p. 1610 in vol. 7, PMID: 27803684.].

Freshmen Program Withdrawal: Types and Recommendations

University program dropout is a problem that has important consequences not only for the student that leaves but also for the institution in which the withdrawal occurs. Therefore, higher education institutions must study the problem in greater depth to establish appropriate prevention measures in the future. However, most research papers currently focus primarily on the characteristics of students who leave university, rather than on those who choose to pursue alternative courses of study and therefore fail to take into account the different kinds of abandonment. The aim of this paper is to identify the different types of dropout to define their characteristics and propose some recommendations. Thus, an ex post facto study was carried out on a sample of 1,311 freshmen from a university in the north of Spain using data gathered using an ad-hoc designed questionnaire, applied by telephone or an online survey, and completed with data available in the university data warehouse. A descriptive analysis was performed to characterize the sample and identify five different groups, including 1. Students persisting in their initiated degree 2. Students who change of program (within the same university) 3. Students transferring to a different university 4. Students enrolling in non-higher-education studies 5. Students that quit studying. Also, data mining techniques (decision trees) were applied to classify the cases and generate predictive models to aid in the design of differentiated intervention strategies for each of the corresponding groups.