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

Purposive Learning

Purposive Learning

E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...

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...

Steps in the Modeling Process

Steps in the Modeling Process

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...

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...

Piaget's Theory of Cognitive Development from Childhood into Adulthood

Piaget's Theory of Cognitive Development from Childhood into Adulthood

Jean Piaget's theory of cognitive development emphasizes the role of thinking in a child's learning process, suggesting that children are naturally curious about their environment. His approach to development is discontinuous, proposing that cognitive abilities progress through distinct stages, each with unique characteristics. Central to Piaget's theory is schemata—mental structures that allow individuals to understand and interpret the world.
Schemata: Building Blocks of Knowledge

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

R2KAN-U-Net: A Novel Architecture Integrating Kolmogorov-Arnold Networks with Residual U-Net for Robust Traffic Sign Segmentation.

Sensors (Basel, Switzerland)·2026

Same author

Comprehensive review of machine learning and deep learning techniques for epileptic seizure detection and prediction based on neuroimaging modalities.

Visual computing for industry, biomedicine, and art·2025

Same author

Artificial intelligence for comprehensive DNA methylation analysis: overview, challenges, and future directions.

Briefings in bioinformatics·2025

Same author

3D-WDA-PMorph: Efficient 3D MRI/TRUS Prostate Registration using Transformer-CNN Network and Wavelet-3D-Depthwise-Attention.

Journal of imaging informatics in medicine·2025

Same author

3D tooth identification for forensic dentistry using deep learning.

BMC oral health·2025

Same author

OliveTreeCrownsDb: A high-resolution UAV dataset for detection and segmentation in agricultural computer vision.

Data in brief·2025

Search research articles

Related Experiment Video

Updated: Aug 24, 2025

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

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

Published on: June 10, 2021

A proposed architectural learner model for a personalized learning environment.

Youssra Bellarhmouch¹, Adil Jeghal², Hamid Tairi¹

¹LISAC Laboratory, Faculty of Sciences Dhar EL Mehraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco.

Education and Information Technologies

|October 21, 2022

Summary

This study enhances e-learning personalization by developing a learner model that integrates diverse data. This approach improves adaptation precision for a more effective educational experience.

Keywords:

Adaptive environment Learner characteristics Learner model Personalization Stereotypes

More Related Videos

Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties

Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties

Published on: September 27, 2020

Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools

Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools

Published on: June 20, 2020

Related Experiment Videos

Last Updated: Aug 24, 2025

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

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

Published on: June 10, 2021

Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties

Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties

Published on: September 27, 2020

Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools

Measuring the Functional Abilities of Children Aged 3-6 Years Old with Observational Methods and Computer Tools

Published on: June 20, 2020

Area of Science:

Educational Technology
Artificial Intelligence in Education

Background:

The COVID-19 pandemic highlighted the critical need for robust e-learning solutions.
E-learning platforms are essential for maintaining educational continuity.
Learner modeling is key to personalizing e-learning environments for effectiveness.

Purpose of the Study:

To propose an enhanced learner model for personalized e-learning.
To improve the precision and effectiveness of adaptive learning systems.

Main Methods:

Integrating diverse learner data: learning style, domain knowledge, assessment results, and affective data.
Employing a hybrid approach combining stereotype methods, fuzzy logic, and similarity techniques.
Developing methods for initializing and updating the learner model.

Main Results:

The proposed learner model offers enhanced precision in personalization.
The integration of multiple data types addresses management uncertainty in learner modeling.
The hybrid approach provides a robust framework for adaptive e-learning.

Conclusions:

A comprehensive learner model is fundamental for effective e-learning personalization.
The integration of varied data sources and advanced techniques significantly improves adaptation.
This approach paves the way for more precise and efficient personalized learning experiences.