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

Improving Translational Accuracy02:07

Improving Translational Accuracy

3.3K
3.3K
Improving Translational Accuracy02:07

Improving Translational Accuracy

12.2K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
12.2K
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

351
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
351
Classification of Systems-I01:26

Classification of Systems-I

421
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
421
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

161
Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
161
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

330
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
330

You might also read

Related Articles

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

Sort by
Same author

A synthetic dataset for time series super-resolution with deep learning.

Scientific data·2026
Same author

Towards the portability of knowledge in reinforcement learning-based systems for automatic drone navigation.

PeerJ. Computer science·2023
Same author

Data science for analyzing and improving educational processes.

Journal of computing in higher education·2021
Same author

Early Prediction of Student Learning Performance Through Data Mining: A Systematic Review.

Psicothema·2021
Same author

Identification and Characterization of Sterol Acyltransferases Responsible for Steryl Ester Biosynthesis in Tomato.

Frontiers in plant science·2018
Same author

Tomato UDP-Glucose Sterol Glycosyltransferases: A Family of Developmental and Stress Regulated Genes that Encode Cytosolic and Membrane-Associated Forms of the Enzyme.

Frontiers in plant science·2017

Related Experiment Video

Updated: Nov 11, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.9K

Improving the portability of predicting students' performance models by using ontologies.

Javier López-Zambrano1,2, Juan A Lara3, Cristóbal Romero2

  • 1Escuela Superior Politécnica Agropecuaria de Manabí (ESPAM MFL), Faculty of Computing, SISCOM Group, 131106 Calceta, Ecuador.

Journal of Computing in Higher Education
|March 29, 2021
PubMed
Summary

This study enhances educational data mining by using ontologies to improve predictive model transferability between courses. This approach boosts model portability and accuracy for learning analytics applications.

Keywords:
Educational data miningModel portabilityOntologyPredictive modellingStudent performanceTransfer learning

More Related Videos

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

872

Related Experiment Videos

Last Updated: Nov 11, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.9K
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

872

Area of Science:

  • Educational Data Mining
  • Learning Analytics
  • Artificial Intelligence in Education

Background:

  • Predictive models in education often lack portability, limiting their application across different courses.
  • Over-reliance on low-level, course-specific attributes hinders the transferability of educational data mining models.
  • High-level, semantically rich attributes like ontologies offer a potential solution to improve model portability.

Purpose of the Study:

  • To propose and evaluate an ontology-based approach for summarizing student interactions in learning management systems.
  • To enhance the portability and predictive accuracy of educational data mining models.
  • To demonstrate the effectiveness of ontological models in transferring knowledge between different courses.

Main Methods:

  • Development of an ontology using a taxonomy of actions to summarize student interactions within the Moodle learning management system.
  • Training and testing predictive models using both low-level raw log attributes and high-level ontological attributes.
  • Comparison of model performance and portability between the ontology-based approach and the traditional low-level attribute approach.

Main Results:

  • The proposed ontology-based approach significantly improves the portability of predictive models compared to using low-level raw attributes.
  • Models trained on ontological attributes maintain predictive accuracy when applied to different target courses.
  • Ontological models demonstrate enhanced transferability, reducing the need for retraining on each new course.

Conclusions:

  • Utilizing ontologies with action taxonomies is an effective strategy for improving the portability of predictive models in educational data mining.
  • Ontological models derived from one course can be successfully applied to other courses with similar usage patterns without substantial loss in accuracy.
  • This research contributes a novel method for creating more robust and transferable predictive models in learning analytics.