Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Fundamental Attribution Error01:14

Fundamental Attribution Error

12.9K
According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is...
12.9K
Nominal Level of Measurement00:56

Nominal Level of Measurement

29.7K
The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. Not every statistical operation can be used with every set of data. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
The data that cannot be measured but can be grouped into categories fall under the nominal level of measurement. Data that is measured using a nominal...
29.7K
Reliability and Validity01:29

Reliability and Validity

12.8K
Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways.
12.8K
Ratio Level of Measurement00:54

Ratio Level of Measurement

18.4K
The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
A set of data measured using the ratio scale takes care of the ratio problem and provides complete information. Ratio scale data are like interval scale data, except they have a zero point and ratios can be calculated....
18.4K
Ordinal Level of Measurement00:55

Ordinal Level of Measurement

24.8K
The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using an ordinal scale are similar to nominal scale data, but there is one major difference. The ordinal scale data can be ordered. An example of ordinal scale data is a list of the top five national parks...
24.8K
Leveling Effect01:29

Leveling Effect

833
In acid-base chemistry, the leveling effect refers to the limitation imposed by the solvent on the strength of acids and bases in solution. When a base stronger than the solvent's conjugate base is used, it deprotonates the solvent until the base is entirely consumed, making it ineffective against weaker acids. Conversely, an acid stronger than the solvent's conjugate acid protonates the solvent until the acid is depleted, rendering it ineffective against weaker bases. Essentially, the...
833

You might also read

Related Articles

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

Sort by
Same author

Prognostic evaluation of Nanog, Oct4, Sox2, PCNA, Ki67 and E-cadherin expression in gastric cancer.

Medical oncology (Northwood, London, England)·2014
Same author

Immunological responses and protection in Chinese giant salamander Andrias davidianus immunized with inactivated iridovirus.

Veterinary microbiology·2014
Same author

Increased fermentation activity and persistent methanogenesis in a model aquifer system following source removal of an ethanol blend release.

Water research·2014
Same author

Assessment of bacterial and archaeal community structure in Swine wastewater treatment processes.

Microbial ecology·2014
Same author

Th17/Treg cells imbalance and GITRL profile in patients with Hashimoto's thyroiditis.

International journal of molecular sciences·2014
Same author

Simple way to obtain pH-sensitive drug release from functional mesoporous silica materials.

IET nanobiotechnology·2014
Same journal

Intervention Feasible Region and Driver Risk Capacity Aware Human-Machine Collaborative Safe Trajectory Planning.

IEEE transactions on neural networks and learning systems·2026
Same journal

A Unified Differential Denoising Learning Framework With a Pre-Trained Model and Fuzzy Graph Networks for Drug-Drug Interaction Prediction.

IEEE transactions on neural networks and learning systems·2026
Same journal

Self-Supervised Continuous Dynamic Graph Representation Learning via Hawkes Processes.

IEEE transactions on neural networks and learning systems·2026
Same journal

cPU: Consistent Risk Estimator for Positive-Unlabeled Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Tuning-Free Latent Diffusion Models for Ultrahigh-Resolution Image Editing.

IEEE transactions on neural networks and learning systems·2026
Same journal

Hidden Data Recovery and Forecasting via Next-Generation Reservoir Computing With Multiscale Delay Selection.

IEEE transactions on neural networks and learning systems·2026
See all related articles

Related Experiment Video

Updated: Jul 22, 2025

Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding
06:33

Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding

Published on: October 11, 2018

6.9K

XTQA: Span-Level Explanations for Textbook Question Answering.

Jie Ma, Qi Chai, Jun Liu

    IEEE Transactions on Neural Networks and Learning Systems
    |July 24, 2023
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces XTQA, a novel system for explainable textbook question answering (TQA). XTQA effectively extracts span-level explanations, improving both understanding and accuracy in multimodal question answering tasks.

    More Related Videos

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
    03:14

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

    Published on: December 6, 2024

    624
    Using Eye Movements to Evaluate the Cognitive Processes Involved in Text Comprehension
    06:49

    Using Eye Movements to Evaluate the Cognitive Processes Involved in Text Comprehension

    Published on: January 10, 2014

    27.3K

    Related Experiment Videos

    Last Updated: Jul 22, 2025

    Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding
    06:33

    Decomposing the Variance in Reading Comprehension to Reveal the Unique and Common Effects of Language and Decoding

    Published on: October 11, 2018

    6.9K
    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
    03:14

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

    Published on: December 6, 2024

    624
    Using Eye Movements to Evaluate the Cognitive Processes Involved in Text Comprehension
    06:49

    Using Eye Movements to Evaluate the Cognitive Processes Involved in Text Comprehension

    Published on: January 10, 2014

    27.3K

    Area of Science:

    • Artificial Intelligence
    • Natural Language Processing
    • Educational Technology

    Background:

    • Textbook question answering (TQA) systems require explainability for deeper human understanding of complex multimodal contexts.
    • Existing TQA research lacks methods for generating explanations, hindering practical application.
    • Developing explainable AI is crucial for educational tools to build user trust and comprehension.

    Purpose of the Study:

    • To propose a novel architecture, XTQA, for generating span-level explanations in textbook question answering.
    • To address the gap in explainable TQA by developing a coarse-to-fine grained explanation extracting (EE) algorithm.
    • To enhance TQA performance and provide interpretable answers using extracted textual spans.

    Main Methods:

    • Devised a novel architecture, XTQA, focusing on span-level explanations (combinations of sentences within a paragraph).
    • Developed a coarse-to-fine grained explanation extracting (EE) algorithm to narrow down evidence scope from entire lesson contexts.
    • Integrated the EE algorithm into TQA methods to improve explainability and performance.

    Main Results:

    • XTQA achieved the best overall explanation result with a mean intersection over union (mIoU) of 52.38% on the CK12-QA test splits.
    • Demonstrated significant explainability for both nondiagram (ND) and diagram-based questions.
    • Achieved state-of-the-art TQA performance, with scores of 36.46% and 36.95% on the test splits.

    Conclusions:

    • The proposed XTQA architecture effectively generates span-level explanations for textbook question answering.
    • The EE algorithm enhances the explainability and performance of TQA systems.
    • XTQA represents a significant advancement in creating more transparent and accurate educational AI tools.