Jove
Visualize
Contact Us

Related Concept Videos

Decision Making: P-value Method01:09

Decision Making: P-value Method

The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can have a...
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
Decision Making01:20

Decision Making

Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...

You might also read

Related Articles

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

Sort by
Same author

Who plays a more crucial role in adolescent well-being: Interactions with parents or peers? An investigation of adolescents aged 10 to 18 years.

Applied psychology. Health and well-being·2026
Same author

Adolescent-Perceived Maternal Responses to Their Negative Emotions Predict Adolescents' Willingness to Share Emotional Distress With Mothers: A Cross-Lagged Panel Network Model.

Family process·2025
Same author

Modelling nonlinear moderation effects with local structural equation modelling (LSEM): A non-technical introduction.

International journal of psychology : Journal international de psychologie·2024
Same author

To see or not to see: the parallel processing of self-relevance and facial expressions.

Cognitive research: principles and implications·2023
Same author

Determinants of higher education teachers' intention to use technology-based exams.

Education and information technologies·2022
Same author

A Large-Scale COVID-19 Twitter Chatter Dataset for Open Scientific Research-An International Collaboration.

Epidemiologia (Basel, Switzerland)·2022
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
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 Experiment Video

Updated: Jun 24, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

A Machine-Learning-Based Approach to Informing Student Admission Decisions.

Tuo Liu1, Cosima Schenk1, Stephan Braun1

  • 1Institute of Psychology, Goethe University Frankfurt, 60323 Frankfurt am Main, Germany.

Behavioral Sciences (Basel, Switzerland)
|March 28, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a machine learning approach for university admissions, improving enrollment predictions by accounting for statistical uncertainty. This data-driven method optimizes applicant selection, reducing over- and underenrollment risks compared to traditional methods.

Keywords:
admissionsenrollment managementenrollment yieldmachine learningpredictive modelingstatistical uncertainty

Related Experiment Videos

Last Updated: Jun 24, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

  • Higher Education Management
  • Data Science in Education
  • Predictive Analytics

Background:

  • University admissions face challenges with high application volumes and limited study places, necessitating strategic management.
  • Traditional methods using historical enrollment yields ignore statistical uncertainty, leading to suboptimal admission decisions and potential over- or underenrollment.

Purpose of the Study:

  • To develop and evaluate a novel machine learning-based approach for optimizing student admission decisions.
  • To improve the accuracy of enrollment predictions by incorporating statistical uncertainty.

Main Methods:

  • Trained and compared multiple machine learning models on historical university application data.
  • Developed a model to predict enrolled applicants conditionally, considering statistical uncertainty.
  • Applied the best model to estimate individual enrollment probabilities and aggregated these to predict total enrollment and associated risks.

Main Results:

  • The proposed machine learning approach demonstrated superior performance over traditional methods.
  • Enabled data-driven adjustments to the number of admitted applicants.
  • Effectively controlled the risk of over- and underenrollment.

Conclusions:

  • The machine learning-based approach offers a more robust and data-driven solution for strategic student admission management.
  • This method enhances the precision of enrollment predictions, leading to more efficient resource allocation and improved student intake outcomes.