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

Steps in the Modeling Process01:14

Steps in the Modeling Process

302
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...
302
Introduction to Learning01:18

Introduction to Learning

523
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
523

You might also read

Related Articles

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

Sort by
Same author

Enhanced Fetal Plane Classification in Ultrasound Imaging via Prototypical Networks and Few-Shot Learning.

Journal of imaging informatics in medicine·2025
Same author

A hybrid super ensemble learning model for the early-stage prediction of diabetes risk.

Medical & biological engineering & computing·2023
Same author

Correlation value determined to increase Salmonella prediction success of deep neural network for agricultural waters.

Environmental monitoring and assessment·2022
See all related articles

Related Experiment Video

Updated: Sep 5, 2025

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes
10:43

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Published on: June 10, 2021

5.5K

A novel color labeled student modeling approach using e-learning activities for data mining.

Selim Buyrukoğlu1

  • 1Department of Computer Engineering, Faculty of Engineering, Çankırı Karatekin University, 18100 Çankırı, Turkey.

Universal Access in the Information Society
|July 5, 2022
PubMed
Summary

This study introduces a new Student Classification Rate (SCR) to accurately identify student learning styles and track weekly progress using e-learning data. The SCR approach effectively monitors student learning and improves educational outcomes.

Keywords:
Data miningE-learningLearning styleRandom forestStudent classification rateStudent modeling

More Related Videos

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques
13:44

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Published on: December 9, 2022

3.7K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.1K

Related Experiment Videos

Last Updated: Sep 5, 2025

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes
10:43

Eye-tracking Technology and Data-mining Techniques used for a Behavioral Analysis of Adults engaged in Learning Processes

Published on: June 10, 2021

5.5K
Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques
13:44

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Published on: December 9, 2022

3.7K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.1K

Area of Science:

  • Educational Technology
  • Computer Science

Background:

  • Traditional student assessment methods like exam grades are insufficient for understanding individual student needs and learning styles.
  • E-learning activities, virtual class attendance, and assignment submission times offer richer data for comprehensive student modeling.

Purpose of the Study:

  • To propose a novel color-labeled student modeling approach using e-learning activities to identify learning styles and monitor weekly improvements.
  • To develop and evaluate a new Student Classification Rate (SCR) formula for enhanced student classification.

Main Methods:

  • A new Student Classification Rate (SCR) formula was developed, integrating pre-study, virtual class, and virtual lab stages.
  • Artificial Neural Network and Random Forest algorithms were used to evaluate the SCR using two feature sets.
  • The SCR approach was validated on an Object-Oriented Programming module and two other modules.

Main Results:

  • The Random Forest algorithm with the SCR and regular data feature set achieved the lowest Mean Absolute Error (MAE) of 0.7.
  • 81% of students were identified as preferring live virtual class attendance for their learning style.
  • A strong positive correlation (Pearson r=0.78) was found between the SCR and lab grades, indicating successful monitoring of weekly learning progress.

Conclusions:

  • The proposed SCR approach demonstrates significant potential for accurate student classification and identification of learning styles.
  • The SCR method effectively aids lecturers in monitoring students' weekly progress, potentially leading to improved learning outcomes.
  • The SCR approach shows promise for broader application across different modules to enhance student learning.