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...

Cognitivism

Cognitivism

Cognitive psychology emerged as a significant field in the mid-20th century. It focused on understanding humans' internal mental processes. This approach emphasizes how people perceive, remember, think, and solve problems—elements critical to human cognition.
Previously dominated by behaviorism, which prioritized observable behaviors and largely ignored mental processes, psychology transformed in the 1950s. Cognitive psychologists argue that understanding how we think and process...

Introduction to Cognitive Psychology

Introduction to Cognitive Psychology

Cognitive psychology is the field of psychology dedicated to examining how people think. It attempts to explain how and why we think the way we do by studying the interactions among human thinking, emotion, creativity, language, and problem-solving, as well as other cognitive processes. Cognitive psychology studies how information is processed and manipulated in remembering, thinking, and knowing.
This field emerged in the mid-20th century, following a period dominated by behaviorism, which...

Cluster Sampling Method

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...

Case Studies

Case Studies

There are many research methods available to psychologists in their efforts to understand, describe, and explain behavior and the cognitive and biological processes that underlie it.

The Representativeness Heuristic

The Representativeness Heuristic

The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.

You might also read

Related Articles

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

Sort by

Same author

Multi-method validation of the new computerized test of fluid intelligence MatriKS.

Behavior research methods·2026

Same author

Error patterns on a computerized version of Raven's progressive matrices in specific learning disorders.

Frontiers in psychology·2026

Same author

Prolonged Visual Entrainment Induces Long-Lasting Alpha-Band Modulations.

Psychophysiology·2026

Same author

Response to Shala: Temporal predictability of pain shapes motor system inhibition.

Pain·2026

Same author

Sensor-Based Assessment of Morning Prospective Memory in Narcolepsy Type 1: Evidence from Children and Adult Cohorts.

Sensors (Basel, Switzerland)·2026

Same author

The role of outcome affective value in driving human Pavlovian learning.

Journal of experimental psychology. Animal learning and cognition·2026

Search research articles

Related Experiment Video

Updated: Aug 5, 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

Preliminary evidence on machine learning approaches for clusterizing students' cognitive profile.

Matteo Orsoni¹, Sara Giovagnoli¹, Sara Garofalo¹

¹Department of Psychology, University of Bologna, Italy.

|March 27, 2023

Summary

This study compared clustering methods for identifying student cognitive profiles. A two-level approach using Self-Organizing Maps (SOMs) and k-means clustering proved most effective for grouping students by cognitive abilities.

Keywords:

Cognitive profiling K-means Machine learning Self-organizing maps Specific learning difficulties (SLD)

More Related Videos

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

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

Published on: December 15, 2023

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Related Experiment Videos

Last Updated: Aug 5, 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

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

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

Published on: December 15, 2023

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

Educational Psychology
Data Science in Education
Cognitive Science

Background:

Personalized teaching strategies require understanding students' cognitive profiles.
Clustering methods show promise for grouping students by cognition, but comparisons are lacking.
Identifying cognitive profiles aids in tailored educational interventions.

Purpose of the Study:

To compare two clustering techniques for grouping students based on cognitive abilities.
To evaluate the effectiveness of a two-level clustering approach versus k-means alone.
To assess the predictive performance of AdaBoost and Artificial Neural Network (ANN) algorithms for cognitive profiles.

Main Methods:

A study involving 292 students aged 11-15 years.
Cognitive abilities assessed included general intelligence, attention, visual perception, working memory, and phonological awareness.
Compared a two-level approach (SOMs + k-means) with k-means clustering alone, followed by AdaBoost and ANN prediction.

Main Results:

The two-level clustering approach (SOMs + k-means) yielded superior results for student profile grouping.
The Artificial Neural Network (ANN) algorithm demonstrated the highest accuracy in classifying the resulting cognitive profiles.
This research lays groundwork for tools to predict student cognitive profiles.

Conclusions:

A combined SOMs and k-means approach is effective for identifying student cognitive profiles.
ANN algorithms are suitable for predicting these identified cognitive profiles.
Findings support the development of instruments for personalized education based on cognitive assessments.