Jove
Visualize
Contact Us

Related Concept Videos

Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

821
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
821
Distributed Loads01:19

Distributed Loads

699
Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
For example, consider a bookshelf filled with books stacked vertically adjacent to each other. The weight of the books is evenly distributed over the length of the shelf. As a result, the pressure at different locations on the surface of the...
699
Classification of Systems-II01:31

Classification of Systems-II

259
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
259
Classification of Systems-I01:26

Classification of Systems-I

366
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:
366
Social Loafing01:37

Social Loafing

36.9K
Another way in which a group presence can affect performance is social loafing—the exertion of less effort by a person working together with a group. Social loafing occurs when our individual performance cannot be evaluated separately from the group. Thus, group performance declines on easy tasks (Karau & Williams, 1993). Essentially individual group members loaf and let other group members pick up the slack. Because each individual’s efforts cannot be evaluated,...
36.9K
Cognitive Learning01:21

Cognitive Learning

707
Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
707

You might also read

Related Articles

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

Sort by
Same author

A hybrid CNN-DNN model for battery remaining useful life RUL prediction.

Scientific reports·2026
Same author

Developing Multiagent E-Learning System-Based Machine Learning and Feature Selection Techniques.

Computational intelligence and neuroscience·2022
Same author

Adaptive model to support business process reengineering.

PeerJ. Computer science·2021
Same author

A proposed defect tracking model for classifying the inserted defect reports to enhance software quality control.

Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH·2013
Same journal

DARUMA: a gateway to fast and easy prediction of intrinsically disordered regions.

PeerJ. Computer science·2026
Same journal

Alzheimer's disease detection using a quantum deep neural network with Haralick feature extraction and simulated annealing optimization.

PeerJ. Computer science·2026
Same journal

Network anomaly detection using Deep Autoencoder and parallel Artificial Bee Colony algorithm-trained neural network.

PeerJ. Computer science·2026
Same journal

An anomaly detection model for multivariate time series with anomaly perception.

PeerJ. Computer science·2026
Same journal

Retraction: A wormhole attack detection method for tactical wireless sensor networks.

PeerJ. Computer science·2026
Same journal

Evaluation of mental disorder with prioritization of its type by utilizing the bipolar complex fuzzy decision-making approach based on Schweizer-Sklar prioritized aggregation operators.

PeerJ. Computer science·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: Oct 18, 2025

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

3.8K

Enhancing the e-learning system based on a novel tasks' classification load-balancing algorithm.

Ayman E Khedr1, Amira M Idrees1, Rashed Salem2

  • 1Information Systems Department, Faculty of Computers and Information Technology, Future University in Egypt, Cairo, Egypt.

Peerj. Computer Science
|October 4, 2021
PubMed
Summary
This summary is machine-generated.

A new load-balancing algorithm enhances e-learning system performance and student satisfaction. This novel approach optimizes resource utilization, leading to a 95.4% satisfaction rate among students in educational cloud systems.

Keywords:
Classification data miningCloud computingE-learningLoad balancingStudents’ satisfaction

More Related Videos

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
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

4.4K

Related Experiment Videos

Last Updated: Oct 18, 2025

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

3.8K
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
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

4.4K

Area of Science:

  • Computer Science
  • Educational Technology
  • Cloud Computing

Background:

  • E-learning systems face performance bottlenecks due to high user loads (instructors, students).
  • Optimal load distribution is crucial for efficient resource utilization and system performance in cloud environments.

Purpose of the Study:

  • To propose a novel load-balancing algorithm for e-learning systems.
  • To enhance system performance and increase user (student) satisfaction.
  • To evaluate the algorithm's effectiveness in real-world educational settings.

Main Methods:

  • Development and simulation of a new load-balancing algorithm.
  • Implementation and testing of the algorithm on the Helwan University e-learning system.
  • User satisfaction assessment via a questionnaire administered to students.

Main Results:

  • The proposed algorithm demonstrated superior performance compared to existing load-balancing methods.
  • Simulation confirmed the algorithm's applicability and effectiveness.
  • A real-case experiment at Helwan University showed significant improvements.

Conclusions:

  • The novel load-balancing algorithm effectively optimizes e-learning system performance.
  • The algorithm leads to a substantial increase in student satisfaction, achieving 95.4%.
  • This research provides a viable solution for improving educational cloud system efficiency and user experience.