Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Mathematical Modeling: Problem Solving01:29

Mathematical Modeling: Problem Solving

Mathematical modeling transforms real-world scenarios into mathematical expressions, allowing for structured problem-solving and analysis. This process involves defining the situation, assigning variables to measurable quantities, selecting an appropriate model, and solving the resulting equation. Such models are invaluable in finance, providing precise methods to evaluate investments, loans, and repayment structures.A widely used example is the calculation of fixed monthly payments on a loan,...
Modeling with Differential Equations01:25

Modeling with Differential Equations

Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
Modeling and Similitude01:12

Modeling and Similitude

Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
Steps in the Modeling Process01:14

Steps in the Modeling Process

Albert Bandura's theory of observational learning identifies four critical processes: attention, retention, motor reproduction, and reinforcement or motivation.
Attention is the first necessary component for observational learning. It involves focusing on what the model is doing and saying. For example, if you decide to take a drawing class to enhance your skills, you need to pay close attention to the instructor's words and hand movements. The characteristics of the model significantly...
Hindsight Biases01:12

Hindsight Biases

Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now?

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

From Incremental Validity to Decision Utility: A Framework for Intelligence Testing in Education.

Journal of Intelligence·2026
Same author

Testing the Island Effect in a Highly Mobile Pollinator: Wing Morphological Divergence in <i>Euglossa mixta</i> from Continental and Insular Panama.

Animals : an open access journal from MDPI·2026
Same author

Navigating Ambivalence: Artificial Intelligence and Its Impact on Student Engagement in Engineering Education.

Behavioral sciences (Basel, Switzerland)·2026
Same author

Wing Shape Fluctuating Asymmetry in Flies: Insights into Environmental and Public Health Risk.

Animals : an open access journal from MDPI·2025
Same author

Unraveling Wing Shape Variation in Malaria Mosquitoes from the Arctic Edge: A Geometric Morphometric Study in Western Siberia.

Animals : an open access journal from MDPI·2025
Same author

Impact of Chirality on the Dynamic Susceptibility of Concentric Nanotori.

Nanomaterials (Basel, Switzerland)·2025
Same journal

An Eye-Tracking Study on Text Accessibility and Comprehension in University Students.

Behavioral sciences (Basel, Switzerland)·2026
Same journal

The Relationship Between Physical Activity, Social Support, and Life Satisfaction Among Female College Students: A Variable- and Person-Centered Analysis.

Behavioral sciences (Basel, Switzerland)·2026
Same journal

Shifting the Blame: How Narrative Framing, Coercive Strategies, and Rape Myth Acceptance Distort Perceptions of Sexual Assault and Fuel Victim Blame.

Behavioral sciences (Basel, Switzerland)·2026
Same journal

An AI Perspective on Counseling Supervision.

Behavioral sciences (Basel, Switzerland)·2026
Same journal

Symbolic Participation or Substantial Learning Behavior? A PSM-Based Comparison Between Honors and Non-Honors Undergraduates from Two Top Elite Universities in China.

Behavioral sciences (Basel, Switzerland)·2026
Same journal

Literacy Profiles in Twice-Exceptional Preadolescents with Intellectual Giftedness and Dyslexia.

Behavioral sciences (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: May 28, 2026

Use of Galvanic Skin Responses, Salivary Biomarkers, and Self-reports to Assess Undergraduate Student Performance During a Laboratory Exam Activity
07:32

Use of Galvanic Skin Responses, Salivary Biomarkers, and Self-reports to Assess Undergraduate Student Performance During a Laboratory Exam Activity

Published on: February 10, 2016

Factors Associated with Dropout Intention in Engineering Education: A Learning Interpretable Modeling Approach.

Liliana Pedraja-Rejas1, Nayeli Ocaranza1, Pamela Ocaranza Paz1

  • 1Departamento de Ingeniería Industrial y de Sistemas, Facultad de Ingeniería, Universidad de Tarapacá, Arica 1000000, Chile.

Behavioral Sciences (Basel, Switzerland)
|May 27, 2026
PubMed
Summary
This summary is machine-generated.

Student dropout intention in engineering is influenced by socio-economic factors and self-regulation, not just academic motivation. Early identification of these predictors can aid preventive support strategies for higher education retention.

Keywords:
decision treedropout intentionengineering educationhigher educationlogistic regressionstudent retention

Related Experiment Videos

Last Updated: May 28, 2026

Use of Galvanic Skin Responses, Salivary Biomarkers, and Self-reports to Assess Undergraduate Student Performance During a Laboratory Exam Activity
07:32

Use of Galvanic Skin Responses, Salivary Biomarkers, and Self-reports to Assess Undergraduate Student Performance During a Laboratory Exam Activity

Published on: February 10, 2016

Area of Science:

  • Higher Education Studies
  • Educational Psychology
  • Engineering Education Research

Background:

  • Higher education dropout presents significant academic, institutional, and social challenges.
  • Early identification of student dropout intention is crucial for developing effective preventive support strategies.
  • Understanding factors influencing dropout intention in specific fields like engineering is essential for targeted interventions.

Purpose of the Study:

  • To examine the predictors of dropout intention among undergraduate engineering students at a Chilean university.
  • To identify key variables associated with students' likelihood to withdraw from their engineering programs.
  • To provide empirical evidence for developing early warning systems and support mechanisms in higher education.

Main Methods:

  • Survey data collected from 189 undergraduate engineering students.
  • Operationalization of five key variables: personal self-regulation, mental health, institutional perception, socio-economic conditions, and academic motivation.
  • Estimation of two predictive models: multivariable logistic regression and a shallow decision tree.

Main Results:

  • Logistic regression identified socio-economic conditions and personal self-regulation as significant protective predictors of dropout intention.
  • Mental health, institutional perception, and academic motivation were not significant predictors in the adjusted logistic regression model.
  • The decision tree model corroborated the importance of socio-economic conditions and personal self-regulation, with a secondary role for mental health.

Conclusions:

  • Dropout intention in engineering education is primarily influenced by structural factors (socio-economic conditions) and individual resources (self-regulation).
  • Institutional perception and academic motivation alone were less significant in predicting dropout intention in this context.
  • Findings support the development of targeted support strategies focusing on socio-economic factors and self-regulatory skills for engineering student retention.