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

Intelligence

Intelligence

The term "intelligence" is complex because it refers to both behavior and individuals, and its interpretation varies across cultures. European Americans tend to link intelligence with reasoning and cognitive skills, while in Kenya, it is tied to responsible participation in family and social life. In Uganda, intelligence is seen as the ability to know the right actions and carry them out effectively, while the Iatmul people of Papua New Guinea associate it with the capacity to remember...

Multiple Intelligences Theory

Multiple Intelligences Theory

Howard Gardner's theory of Multiple Intelligence proposes that there are nine distinct types of intelligence, each reflecting different ways of interacting with the world. Introduced in 1983 and expanded in subsequent years, Gardner's framework challenges the traditional notion of a single, generalized intelligence.

You might also read

Related Articles

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

Sort by

Same author

DisenTS: Disentangled Channel Evolving Pattern Modeling for Multivariate Time Series Forecasting.

IEEE transactions on pattern analysis and machine intelligence·2026

Same author

ActionX: pre-training action experts with reinforcement learning for vision-language action models.

Frontiers in neurorobotics·2026

Same author

Rein++: Efficient Generalization and Adaptation for Semantic Segmentation with Vision Foundation Models.

IEEE transactions on pattern analysis and machine intelligence·2026

Same author

Survey of Computerized Adaptive Testing: a Machine Learning Perspective.

IEEE transactions on pattern analysis and machine intelligence·2026

Same author

Learning Deep Tree-Based Retriever for Efficient Recommendation: Theory and Method.

IEEE transactions on pattern analysis and machine intelligence·2025

Same author

Learning Deep ISP for High-Speed Cameras: Achieving DSLR-Quality Imaging Under High Frame Rates.

IEEE transactions on pattern analysis and machine intelligence·2025

Same journal

An efficient privacy-preserving blockchain storage method for internet of things environment.

World wide web·2023

Same journal

Transfer learning based cascaded deep learning network and mask recognition for COVID-19.

World wide web·2023

Same journal

A DRL-based online VM scheduler for cost optimization in cloud brokers.

World wide web·2023

Same journal

Modeling the social influence of COVID-19 via personalized propagation with deep learning.

World wide web·2022

Same journal

PerHeFed: A general framework of personalized federated learning for heterogeneous convolutional neural networks.

World wide web·2022

Same journal

EAGS: An extracting auxiliary knowledge graph model in multi-turn dialogue generation.

World wide web·2022

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 22, 2025

A Behavioral Test Battery for the Repeated Assessment of Motor Skills, Mood, and Cognition in Mice

A Behavioral Test Battery for the Repeated Assessment of Motor Skills, Mood, and Cognition in Mice

Published on: March 2, 2019

A generalized multi-skill aggregation method for cognitive diagnosis.

Suojuan Zhang¹, Song Huang¹, Xiaohan Yu¹

¹College of Command & Control Engineering, Army Engineering University of PLA, 210000 Nanjing, Jiangsu China.

|May 23, 2022

Summary

This summary is machine-generated.

This study introduces a new cognitive diagnosis method using the Sugeno integral generalized multi-skill aggregation (SI-GAM) to better understand student learning. This approach accounts for complex skill interactions, improving personalized online education services.

Keywords:

Cognitive diagnosis Fuzzy measure Multi-skill aggregation Multi-skill interactions Multiple strategies Sugeno integral

More Related Videos

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Dual-Task Stroop Paradigm for Detecting Cognitive Deficits in High-Functioning Stroke Patients

Dual-Task Stroop Paradigm for Detecting Cognitive Deficits in High-Functioning Stroke Patients

Published on: December 16, 2022

Related Experiment Videos

Last Updated: Sep 22, 2025

A Behavioral Test Battery for the Repeated Assessment of Motor Skills, Mood, and Cognition in Mice

A Behavioral Test Battery for the Repeated Assessment of Motor Skills, Mood, and Cognition in Mice

Published on: March 2, 2019

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Dual-Task Stroop Paradigm for Detecting Cognitive Deficits in High-Functioning Stroke Patients

Dual-Task Stroop Paradigm for Detecting Cognitive Deficits in High-Functioning Stroke Patients

Published on: December 16, 2022

Area of Science:

Educational Technology
Cognitive Science
Artificial Intelligence

Background:

Personalized learning in online education requires understanding learner cognitive states.
Cognitive diagnosis assesses student mastery of skills using response data.
Existing multi-skill aggregation methods often assume equal skill impact.

Purpose of the Study:

To propose a generalized multi-skill aggregation method (SI-GAM) for cognitive diagnosis.
To model complex interactions and differential weighting between skills.
To enhance the interpretability and generalizability of cognitive diagnosis frameworks.

Main Methods:

Developed a generalized multi-skill aggregation method based on the Sugeno integral (SI-GAM).
Introduced fuzzy measures to represent complex skill interactions and their varying impacts.
Applied the method to model multi-strategy problems in cognitive diagnosis.

Main Results:

The proposed SI-GAM effectively characterizes complex skill interactions.
The method demonstrated feasibility and effectiveness on both synthetic and real-world datasets.
The cognitive diagnosis process is implemented via a more general and interpretable aggregation method.

Conclusions:

The SI-GAM offers a more nuanced approach to cognitive diagnosis by considering skill interdependencies.
This method advances personalized learning by providing a more accurate assessment of cognitive states.
The findings support the use of SI-GAM for improved educational services in online learning environments.