Publication Date
In 2025 | 0 |
Since 2024 | 0 |
Since 2021 (last 5 years) | 3 |
Since 2016 (last 10 years) | 8 |
Since 2006 (last 20 years) | 8 |
Descriptor
Memory | 8 |
Models | 5 |
Natural Language Processing | 5 |
Semantics | 5 |
Reading Comprehension | 4 |
Reading Processes | 4 |
Graphs | 3 |
Inferences | 3 |
Language Processing | 3 |
Prior Learning | 3 |
Schemata (Cognition) | 3 |
More ▼ |
Source
Grantee Submission | 8 |
Author
Publication Type
Reports - Research | 6 |
Speeches/Meeting Papers | 3 |
Journal Articles | 2 |
Reports - Descriptive | 2 |
Opinion Papers | 1 |
Education Level
Early Childhood Education | 1 |
Elementary Education | 1 |
Grade 2 | 1 |
Primary Education | 1 |
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
Test of English as a Foreign… | 1 |
What Works Clearinghouse Rating
Dragos Corlatescu; Micah Watanabe; Stefan Ruseti; Mihai Dascalu; Danielle S. McNamara – Grantee Submission, 2023
Reading comprehension is essential for both knowledge acquisition and memory reinforcement. Automated modeling of the comprehension process provides insights into the efficacy of specific texts as learning tools. This paper introduces an improved version of the Automated Model of Comprehension, version 3.0 (AMoC v3.0). AMoC v3.0 is based on two…
Descriptors: Reading Comprehension, Models, Concept Mapping, Graphs
Corlatescu, Dragos-Georgian; Dascalu, Mihai; McNamara, Danielle S. – Grantee Submission, 2021
Reading comprehension is key to knowledge acquisition and to reinforcing memory for previous information. While reading, a mental representation is constructed in the reader's mind. The mental model comprises the words in the text, the relations between the words, and inferences linking to concepts in prior knowledge. The automated model of…
Descriptors: Reading Comprehension, Reading Processes, Memory, Schemata (Cognition)
McNamara, Danielle S. – Grantee Submission, 2020
This article provides a commentary within the special issue, Integration: The Keystone of Comprehension. According to most contemporary frameworks, a driving force in comprehension is the reader's ability to generate the links among the words and sentences (ideas) in the texts and between the ideas in the text and what the readers already know. As…
Descriptors: Inferences, Language Processing, Reading Comprehension, Reading Research
Corlatescu, Dragos-Georgian; Dascalu, Mihai; McNamara, Danielle S. – Grantee Submission, 2021
Reading comprehension is key to knowledge acquisition and to reinforcing memory for previous information. While reading, a mental representation is constructed in the reader's mind. The mental model comprises the words in the text, the relations between the words, and inferences linking to concepts in prior knowledge. The automated model of…
Descriptors: Reading Comprehension, Memory, Inferences, Syntax
Johns, Brendan T.; Jones, Michael N.; Mewhort, D. J. K. – Grantee Submission, 2019
To account for natural variability in cognitive processing, it is standard practice to optimize a model's parameters by fitting it to behavioral data. Although most language-related theories acknowledge a large role for experience in language processing, variability reflecting that knowledge is usually ignored when evaluating a model's fit to…
Descriptors: Language Processing, Models, Information Sources, Linguistics
Jones, Michael N. – Grantee Submission, 2018
Abstraction is a core principle of Distributional Semantic Models (DSMs) that learn semantic representations for words by applying dimensional reduction to statistical redundancies in language. Although the posited learning mechanisms vary widely, virtually all DSMs are prototype models in that they create a single abstract representation of a…
Descriptors: Abstract Reasoning, Semantics, Memory, Learning Processes
Sano, Makoto; Baker, Doris Luft; Collazo, Marlen; Le, Nancy; Kamata, Akihito – Grantee Submission, 2020
Purpose: Explore how different automated scoring (AS) models score reliably the expressive language and vocabulary knowledge in depth of young second grade Latino English learners. Design/methodology/approach: Analyze a total of 13,471 English utterances from 217 Latino English learners with random forest, end-to-end memory networks, long…
Descriptors: English Language Learners, Hispanic American Students, Elementary School Students, Grade 2
Jones, Michael N.; Dye, Melody; Johns, Brendan T. – Grantee Submission, 2017
Classic accounts of lexical organization posit that humans are sensitive to environmental frequency, suggesting a mechanism for word learning based on repetition. However, a recent spate of evidence has revealed that it is not simply frequency but the diversity and distinctiveness of contexts in which a word occurs that drives lexical…
Descriptors: Word Frequency, Vocabulary Development, Context Effect, Semantics