Publication Date
| In 2025 | 0 |
| Since 2024 | 0 |
| Since 2021 (last 5 years) | 3 |
| Since 2016 (last 10 years) | 6 |
| Since 2006 (last 20 years) | 6 |
Descriptor
| Reading Comprehension | 4 |
| Inferences | 3 |
| Language Processing | 3 |
| Memory | 3 |
| Natural Language Processing | 3 |
| Teaching Methods | 3 |
| Comparative Analysis | 2 |
| Individual Differences | 2 |
| Models | 2 |
| Reading Instruction | 2 |
| Reading Research | 2 |
| More ▼ | |
Author
Publication Type
| Reports - Descriptive | 6 |
| Speeches/Meeting Papers | 4 |
| Opinion Papers | 2 |
| Journal Articles | 1 |
Education Level
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
| Flesch Reading Ease Formula | 1 |
What Works Clearinghouse Rating
Peer reviewed
Direct linkMcNamara, Danielle S. – Discourse Processes: A Multidisciplinary Journal, 2021
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
Download full textBotarleanu, Robert-Mihai; Dascalu, Mihai; Watanabe, Micah; McNamara, Danielle S.; Crossley, Scott Andrew – Grantee Submission, 2021
The ability to objectively quantify the complexity of a text can be a useful indicator of how likely learners of a given level will comprehend it. Before creating more complex models of assessing text difficulty, the basic building block of a text consists of words and, inherently, its overall difficulty is greatly influenced by the complexity of…
Descriptors: Multilingualism, Language Acquisition, Age, Models
Crossley, Scott A.; Kim, Minkyung; Allen, Laura K.; McNamara, Danielle S. – Grantee Submission, 2019
Summarization is an effective strategy to promote and enhance learning and deep comprehension of texts. However, summarization is seldom implemented by teachers in classrooms because the manual evaluation of students' summaries requires time and effort. This problem has led to the development of automated models of summarization quality. However,…
Descriptors: Automation, Writing Evaluation, Natural Language Processing, Artificial Intelligence
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
Dascalu, Mihai; Jacovina, Matthew E.; Soto, Christian M.; Allen, Laura K.; Dai, Jianmin; Guerrero, Tricia A.; McNamara, Danielle S. – Grantee Submission, 2017
iSTART is a web-based reading comprehension tutor. A recent translation of iSTART from English to Spanish has made the system available to a new audience. In this paper, we outline several challenges that arose during the development process, specifically focusing on the algorithms that drive the feedback. Several iSTART activities encourage…
Descriptors: Spanish, Reading Comprehension, Natural Language Processing, Intelligent Tutoring Systems
