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

Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

Protein Networks

Protein Networks

Network Covalent Solids

Network Covalent Solids

Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...

Neural Regulation

Neural Regulation

Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.

Network Function of a Circuit

Network Function of a Circuit

Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.

Neural Circuits

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...

You might also read

Related Articles

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

Sort by

Same author

Reconceptualizing Scoring Reliability Through Linguistic Similarity.

Educational and psychological measurement·2025

Same author

A Robust Method for Detecting Item Misfit in Large-Scale Assessments.

Educational and psychological measurement·2023

Same author

Scoring Graphical Responses in TIMSS 2019 Using Artificial Neural Networks.

Educational and psychological measurement·2023

Same author

Erratum to: A Response-Time-Based Latent Response Mixture Model for Identifying and Modeling Careless and Insufficient Effort Responding in Survey Data.

Psychometrika·2022

Same author

A Response-Time-Based Latent Response Mixture Model for Identifying and Modeling Careless and Insufficient Effort Responding in Survey Data.

Psychometrika·2021

Same author

Commentary: Matching IRT Models to PRO Constructs- Modeling Alternatives, and Some Thoughts on What Makes a Model Different.

Psychometrika·2021

Search research articles

Related Experiment Video

Updated: Feb 13, 2026

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Automated Item Generation with Recurrent Neural Networks.

Matthias von Davier¹

¹National Board of Medical Examiners, 3750 Market Street, Philadelphia, PA, 19104-3102, USA. mvondavier@nbme.org.

|March 14, 2018

Summary

Automated item generation using deep learning and probabilistic language models offers a scalable solution to the high costs and limited resources associated with traditional human item writing for assessments.

Keywords:

automatic item generation deep learning machine learning neural networks

More Related Videos

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Published on: November 26, 2019

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Related Experiment Videos

Last Updated: Feb 13, 2026

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Published on: November 26, 2019

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Area of Science:

Artificial Intelligence
Educational Measurement
Natural Language Processing

Background:

Human item writers are a costly and limited resource for maintaining assessment item banks.
Existing automated item generation methods often rely on narrowly defined item types or complex schema derivation.
The need for scalable and cost-effective solutions in assessment development is critical.

Purpose of the Study:

To present a novel approach for automated item generation using deep learning.
To leverage probabilistic language models for creating diverse and potentially valid test items.
To overcome limitations of previous automated item generation techniques.

Main Methods:

Implementation of deep learning techniques.
Utilizing probabilistic language models, similar to those used in advanced language processing.
Focus on generating items with predictable difficulty levels and variability.

Main Results:

The proposed deep learning approach offers a promising alternative to traditional item writing.
This method has the potential to provide unlimited resources for assessment development.
It represents a significant advancement over previous automated item generation strategies.

Conclusions:

Deep learning and probabilistic language models can revolutionize automated item generation.
This approach addresses the resource limitations and high costs of human item writers.
The study paves the way for more scalable and efficient assessment development.