Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Reinforcement Schedules

Reinforcement Schedules

Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...

Cognitive Learning

Cognitive Learning

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...

Observational Learning

Observational Learning

Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...

Distributed Loads: Problem Solving

Distributed Loads: Problem Solving

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...

Reinforcement

Reinforcement

Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:

Introduction to Learning

Introduction to Learning

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...

You might also read

Related Articles

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

Sort by

Same author

Complex reconstruction technique applied in advanced head and neck cancer.

Medicine·2020

Same author

A Phytophthora capsici effector suppresses plant immunity via interaction with EDS1.

Molecular plant pathology·2020

Same author

Development and evaluation of a panel of multiplex one-tube nested real time PCR assay for simultaneous detection of 14 respiratory viruses in five reactions.

Journal of medical virology·2020

Same author

Maternal cigarette smoke exposure disturbs glutamate/GABA balance in pFRG of neonatal rats.

Respiratory physiology & neurobiology·2020

Same author

Correction: MiR-1 downregulation correlates with poor survival in clear cell renal cell carcinoma where it interferes with cell cycle regulation and metastasis.

Oncotarget·2020

Same author

The role of long non-coding RNA H19 in breast cancer.

Oncology letters·2020

Search research articles

Related Experiment Video

Updated: Jan 13, 2026

Author Spotlight: Enhancing Engineering Education via WebVR-Based Online Laboratories

Author Spotlight: Enhancing Engineering Education via WebVR-Based Online Laboratories

Published on: February 23, 2024

Multi-access edge computing scheduling optimization model for remote education under 6G network environment based on

Lei Jin¹, Xin Gao², Ji Wang¹

¹Information Technology Center, Zhejiang University, Hangzhou, 310058, Zhejiang, China.

Scientific Reports

|January 7, 2026

Summary

This study introduces a new computational model for optimizing digital education schedules using reinforcement learning. The model enhances learning outcomes by adapting content delivery to individual learner needs in remote environments.

Keywords:

Adaptive scheduling Comput learning models Learner state estimation Reinforcement learning Remote education optimization

Related Experiment Videos

Last Updated: Jan 13, 2026

Author Spotlight: Enhancing Engineering Education via WebVR-Based Online Laboratories

Author Spotlight: Enhancing Engineering Education via WebVR-Based Online Laboratories

Published on: February 23, 2024

Area of Science:

Computational learning models
Adaptive educational technologies
Reinforcement learning in education

Background:

Digital education requires advanced computational frameworks for remote learning.
Traditional scheduling fails to address dynamic learner engagement and asynchronous content.
Existing models struggle with learner variability and sparse feedback.

Purpose of the Study:

To develop a novel computational model for optimizing content delivery schedules in digital education.
To address the limitations of traditional scheduling in dynamic remote learning environments.
To enhance learning outcomes through adaptive instructional strategies.

Main Methods:

Utilized reinforcement learning to optimize content delivery schedules.
Introduced the Attentive Stochastic Transition Estimation Network (ASTEN) to model learner states.
Employed the Selective Informative Delivery Strategy (SIDS) for optimal content emission based on uncertainty and utility.

Main Results:

The model effectively captures nuanced learner behaviors like sporadic interaction and temporal decay.
Integrated cognitive and behavioral signals for responsive, tailored instruction.
Empirical evaluations show significant enhancement in learning outcomes due to adaptive scheduling.

Conclusions:

The proposed model significantly improves learning outcomes in adaptive educational technologies.
Offers practical insights for developing responsive instructional strategies in remote learning.
Addresses the inadequacy of one-size-fits-all approaches in diverse learning environments.