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Dropout and transfer paths: What are the risky profiles when analyzing university persistence with machine learning techniques?
- Source :
- PLoS ONE, Vol 14, Iss 6, p e0218796 (2019), PLoS ONE, Scopus, RUO: Repositorio Institucional de la Universidad de Oviedo, Universidad de Oviedo (UNIOVI), RUO. Repositorio Institucional de la Universidad de Oviedo, instname
- Publication Year :
- 2019
- Publisher :
- Public Library of Science (PLoS), 2019.
-
Abstract
- University dropout is a growing problem with considerable academic, social and economic consequences. Conclusions and limitations of previous studies highlight the difficulty of analyzing the phenomenon from a broad perspective and with bigger data sets. This paper proposes a new, machine-learning based method, able to examine the problem using a holistic approach. Advantages of this method include the lack of strong distribution hypothesis, the capacity for handling bigger data sets and the interpretability of the results. Results are consistent with previous research, showing the influence of personal and contextual variables and the importance of academic performance in the first year, but other factors are also highlighted with this model, such as the importance of dedication (part or full time), and the vulnerability of the students with respect to their age. Additionally, a comprehensive graphic output is included to make it easier to interpret the discovered rules.
- Subjects :
- Male
Questionnaires
Leaves
Decision Analysis
Computer science
Student Dropouts
Social Sciences
02 engineering and technology
Plant Science
Cohort Studies
Machine Learning
Learning and Memory
Sociology
Risk Factors
Surveys and Questionnaires
Academic Performance
0202 electrical engineering, electronic engineering, information engineering
Psychology
Dropout (neural networks)
media_common
Interpretability
Multidisciplinary
Schools
Career Choice
Data Collection
Plant Anatomy
05 social sciences
050301 education
Middle Aged
Resilience, Psychological
Research Design
Data Interpretation, Statistical
Lectures
Engineering and Technology
Medicine
Female
Psychological resilience
Curriculum
Management Engineering
Research Article
Adult
Computer and Information Sciences
Full-time
Adolescent
Psychometrics
Universities
media_common.quotation_subject
Science
Decision tree
Research and Analysis Methods
Education
Young Adult
Human Learning
Artificial Intelligence
020204 information systems
Humans
Learning
Students
Data collection
Survey Research
Perspective (graphical)
Decision Trees
Cognitive Psychology
Biology and Life Sciences
Data science
Socioeconomic Factors
Spain
Cognitive Science
0503 education
Neuroscience
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 14
- Issue :
- 6
- Database :
- OpenAIRE
- Journal :
- PLoS ONE
- Accession number :
- edsair.doi.dedup.....005c155ce0f05c89c3abbff3896c6167