[go: up one dir, main page]

Baldwin et al., 2021 - Google Patents

Using natural language processing to predict item response times and improve test construction

Baldwin et al., 2021

Document ID
6070309001568978886
Author
Baldwin P
Yaneva V
Mee J
Clauser B
Ha L
Publication year
Publication venue
Journal of Educational Measurement

External Links

Snippet

In this article, it is shown how item text can be represented by (a) 113 features quantifying the text's linguistic characteristics,(b) 16 measures of the extent to which an information‐ retrieval‐based automatic question‐answering system finds an item challenging, and (c) …
Continue reading at onlinelibrary.wiley.com (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F17/30634—Querying
    • G06F17/30657—Query processing
    • G06F17/30675—Query execution
    • G06F17/30684—Query execution using natural language analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/20—Handling natural language data
    • G06F17/27—Automatic analysis, e.g. parsing
    • G06F17/2765—Recognition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/20—Handling natural language data
    • G06F17/27—Automatic analysis, e.g. parsing
    • G06F17/2705—Parsing
    • G06F17/271—Syntactic parsing, e.g. based on context-free grammar [CFG], unification grammars
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/20—Handling natural language data
    • G06F17/27—Automatic analysis, e.g. parsing
    • G06F17/274—Grammatical analysis; Style critique
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/02—Knowledge representation
    • G06N5/022—Knowledge engineering, knowledge acquisition
    • G—PHYSICS
    • G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
    • G09B7/00—Electrically-operated teaching apparatus or devices working with questions and answers
    • G09B7/02—Electrically-operated teaching apparatus or devices working with questions and answers of the type wherein the student is expected to construct an answer to the question which is presented or wherein the machine gives an answer to the question presented by a student
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
    • G09B19/00—Teaching not covered by other main groups of this subclass
    • G—PHYSICS
    • G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
    • G09B5/00—Electrically-operated educational appliances
    • G09B5/02—Electrically-operated educational appliances with visual presentation of the material to be studied, e.g. using film strip
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management

Similar Documents

Publication Publication Date Title
Baldwin et al. Using natural language processing to predict item response times and improve test construction
Hofstra et al. The diversity–innovation paradox in science
McNamara et al. Coh-Metrix: Capturing linguistic features of cohesion
Collins‐Thompson et al. Predicting reading difficulty with statistical language models
Ford Cognitive styles and virtual environments
Elman Lexical knowledge without a lexicon?
Yu et al. A MAD method to assess idea novelty: Improving validity of automatic scoring using maximum associative distance (MAD).
Ströbel et al. The relationship between first and second language writing: Investigating the effects of first language complexity on second language complexity in advanced stages of learning
Yildiz Durak et al. A comparison of human‐written versus AI‐generated text in discussions at educational settings: investigating features for ChatGPT, Gemini and BingAI
Shardlow et al. Predicting lexical complexity in english texts: the complex 2.0 dataset
Arnulf et al. Measuring semantic components in training and motivation: a methodological introduction to the semantic theory of survey response
Winter et al. More is better: English language statistics are biased toward addition
Alharthi et al. Study of linguistic features incorporated in a literary book recommender system
Allen et al. Natural language processing as a technique for conducting text‐based research
Sun et al. A WordNet-based near-synonyms and similar-looking word learning system
Yang et al. Automatic assessment of divergent thinking in Chinese language with TransDis: A transformer-based language model approach
Zehner et al. Automatically analyzing text responses for exploring gender-specific cognitions in PISA reading
Németh et al. Bio, psycho, or social: supervised machine learning to classify discursive framing of depression in online health communities
Perikos et al. Automatic estimation of exercises' difficulty levels in a tutoring system for teaching the conversion of natural language into first‐order logic
Roivainen Personality adjectives in twitter tweets and in the Google books corpus. An analysis of the facet structure of the openness factor of personality
Botarleanu et al. Age of Exposure 2.0: Estimating word complexity using iterative models of word embeddings
Symons Unauthorized use or dissemination of this information is expressly forbidden. If you have any questions about this material, please contact cognetadmin@ cognet. mit. edu.
Vankrunkelsven et al. Semantic gender: Norms for 24,000 Dutch words and its role in word meaning
Last et al. Automatic question generation
Sheehan Measuring cohesion: An approach that accounts for differences in the degree of integration challenge presented by different types of sentences