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

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...

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...

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...

Law of Effect

Law of Effect

B.F. Skinner, a prominent figure in behavioral psychology, introduced operant conditioning by emphasizing the role of consequences in shaping behavior. This theory builds upon the law of effect proposed by Edward Thorndike, which posits that behaviors followed by satisfying outcomes are likely to be repeated. In contrast, those followed by unsatisfying outcomes are less likely to recur.
Edward Thorndike's foundational work involved studying learning in animals, particularly using puzzle...

Associative Learning

Associative Learning

Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...

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

Validation of the Spanish Sexual Risk Behaviour Scale among adolescents, youth, and future health and educational professionals.

BMC psychology·2026

Same author

Deep learning and support vector machines for transcription start site identification.

PeerJ. Computer science·2023

Same author

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

Journal of visualized experiments : JoVE·2021

Same author

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

Journal of visualized experiments : JoVE·2020

Same author

Observation of Metacognitive Skills in Natural Environments: A Longitudinal Study With Mixed Methods.

Frontiers in psychology·2019

Same author

Metacognitive Precursors: An Analysis in Children with Different Disabilities.

Brain sciences·2017

Search research articles

Related Experiment Video

Updated: Mar 1, 2026

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

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

Published on: December 9, 2022

How Do B-Learning and Learning Patterns Influence Learning Outcomes?

María Consuelo Sáiz Manzanares¹, Raúl Marticorena Sánchez², César Ignacio García Osorio²

¹Department of Health Sciences, University of BurgosBurgos, Spain.

Frontiers in Psychology

|June 1, 2017

Summary

Student learning patterns differ based on Blended Learning (B-Learning) type. Learning Analytics reveal that Replacement Blend (RB) patterns predict outcomes and relate to metacognitive strategies, unlike Supplemental Blend (SB).

Keywords:

blended learning learning analytics learning management systems learning outcomes replacement blend self-regulated learning successful learning supplemental blend

More Related Videos

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

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

Related Experiment Videos

Last Updated: Mar 1, 2026

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

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

Published on: December 9, 2022

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

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

Area of Science:

Educational Technology
Learning Analytics
Online Learning Environments

Background:

Learning Management System (LMS) platforms generate extensive data on student learning patterns.
Learning Analytics (LA) enables the analysis of digital learning activity logs.
Different Blended Learning (B-Learning) models may influence distinct student learning patterns.

Purpose of the Study:

To investigate differences in student learning outcomes and platform activity patterns across B-Learning types (Replacement Blend vs. Supplemental Blend).
To explore the relationship between students' metacognitive and motivational strategies (MS), their learning outcomes, and their platform learning patterns.

Main Methods:

Analysis of 87,065 log records from 129 students (69 in RB, 60 in SB) on the Moodle 3.1 platform.
Comparative analysis of learning patterns and outcomes based on B-Learning type.
Correlation analysis between learning patterns, metacognitive/motivational strategies, and learning outcomes.

Main Results:

Significant differences in student learning patterns were observed between Replacement Blend (RB) and Supplemental Blend (SB) environments.
The specific B-Learning blend (RB vs. SB) influences student behavior and engagement on the LMS platform.
Learning patterns within RB environments were found to be predictive of student learning outcomes.
A relationship between learning patterns and metacognitive/motivational strategies was identified specifically within RB environments.

Conclusions:

The type of Blended Learning significantly impacts student learning patterns and behaviors on LMS platforms.
Replacement Blend environments show a stronger predictive relationship between learning patterns and student success.
Metacognitive and motivational strategies are linked to learning patterns in Replacement Blend settings, suggesting potential intervention points.