Publication Date
In 2024 | 0 |
Since 2023 | 1 |
Since 2020 (last 5 years) | 9 |
Since 2015 (last 10 years) | 29 |
Since 2005 (last 20 years) | 33 |
Descriptor
Natural Language Processing | 18 |
Intelligent Tutoring Systems | 15 |
Reading Comprehension | 14 |
High School Students | 11 |
Essays | 9 |
Prediction | 9 |
Accuracy | 8 |
Artificial Intelligence | 8 |
Feedback (Response) | 7 |
Models | 7 |
Automation | 6 |
More ▼ |
Author
Publication Type
Speeches/Meeting Papers | 33 |
Reports - Research | 30 |
Reports - Descriptive | 3 |
Education Level
High Schools | 11 |
Higher Education | 9 |
Postsecondary Education | 8 |
Secondary Education | 7 |
Grade 9 | 1 |
Junior High Schools | 1 |
Middle Schools | 1 |
Audience
Location
Arizona | 1 |
Florida | 1 |
Massachusetts | 1 |
Laws, Policies, & Programs
Assessments and Surveys
Flesch Kincaid Grade Level… | 1 |
Flesch Reading Ease Formula | 1 |
Gates MacGinitie Reading Tests | 1 |
Massachusetts Comprehensive… | 1 |
Writing Apprehension Test | 1 |
What Works Clearinghouse Rating
Balyan, Renu; Arner, Tracy; Taylor, Karen; Shin, Jinnie; Banawan, Michelle; Leite, Walter L.; McNamara, Danielle S. – International Educational Data Mining Society, 2022
The National Council of Teachers of Mathematics (NCTM) has been emphasizing the importance of teachers' pedagogical communication as part of mathematical teaching and learning for decades. Specifically, NCTM has provided guidance on how teachers can foster mathematical communication that positively impacts student learning. A teacher may have…
Descriptors: Tutoring, Guidelines, Mathematics Instruction, Computer Assisted Instruction
Corlatescu, Dragos; Watanabe, Micah; Ruseti, Stefan; Dascalu, Mihai; McNamara, Danielle S. – 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
Allen, Laura K.; Jacovina, Matthew E.; Johnson, Adam C.; McNamara, Danielle S.; Roscoe, Rod D. – Grantee Submission, 2016
Revising is an essential writing process yet automated writing evaluation systems tend to give feedback on discrete essay drafts rather than changes across drafts. We explore the feasibility of automated revision detection and its potential to guide feedback. Relationships between revising behaviors and linguistic features of students' essays are…
Descriptors: Revision (Written Composition), Automation, Writing Evaluation, Feedback (Response)
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
Peer reviewed
McNamara, Danielle S.; Arner, Tracy; Reilley, Elizabeth; Alvarado, Paul; Clark, Chani; Fikes, Thomas; Hale, Annie; Weigele, Betheny – Grantee Submission, 2022
Accounting for complex interactions between contextual variables and learners' individual differences in aptitudes and background requires building the means to connect and access learner data at large scales, across time, and in multiple contexts. This paper describes the ASU Learning@Scale (L@S) project to develop a digital learning network…
Descriptors: Electronic Learning, Educational Technology, Networks, Learning Analytics
McNamara, Danielle S.; Jacovina, Matthew; Allen, Laura K. – AERA Online Paper Repository, 2016
Within the context of comprehension and education, there has been a heavy emphasis placed on an individual's ability to construct a coherent and elaborated mental representation of text content. Previous research has aimed to establish the theoretical basis behind the comprehension process, as well as the most effective interventions for improving…
Descriptors: Individual Differences, Thinking Skills, Reading Comprehension, Cognitive Processes
McCarthy, Kathryn S.; Likens, Aaron D.; Kopp, Kristopher K.; Perret, Cecile A.; Watanabe, Micah; McNamara, Danielle S. – Grantee Submission, 2018
The current study explored relations between non-cognitive traits (Grit, Learning Orientation, Performance Orientation), reading skill, and performance across three experiments conducted in the context of two intelligent tutoring systems, iSTART and Writing Pal. Results showed that learning outcomes (comprehension score, holistic essay score) were…
Descriptors: Persistence, Individual Characteristics, Reading Skills, Performance
Botarleanu, Robert-Mihai; Dascalu, Mihai; Allen, Laura K.; Crossley, Scott Andrew; McNamara, Danielle S. – Grantee Submission, 2022
Automated scoring of student language is a complex task that requires systems to emulate complex and multi-faceted human evaluation criteria. Summary scoring brings an additional layer of complexity to automated scoring because it involves two texts of differing lengths that must be compared. In this study, we present our approach to automate…
Descriptors: Automation, Scoring, Documentation, Likert Scales
Öncel, Püren; Flynn, Lauren E.; Sonia, Allison N.; Barker, Kennis E.; Lindsay, Grace C.; McClure, Caleb M.; McNamara, Danielle S.; Allen, Laura K. – Grantee Submission, 2021
Automated Writing Evaluation systems have been developed to help students improve their writing skills through the automated delivery of both summative and formative feedback. These systems have demonstrated strong potential in a variety of educational contexts; however, they remain limited in their personalization and scope. The purpose of the…
Descriptors: Computer Assisted Instruction, Writing Evaluation, Formative Evaluation, Summative Evaluation
Allen, Laura K.; Mills, Caitlin; Perret, Cecile; McNamara, Danielle S. – Grantee Submission, 2019
This study examines the extent to which instructions to self-explain vs. "other"-explain a text lead readers to produce different forms of explanations. Natural language processing was used to examine the content and characteristics of the explanations produced as a function of instruction condition. Undergraduate students (n = 146)…
Descriptors: Language Processing, Science Instruction, Computational Linguistics, Teaching Methods
Jacovina, Matthew E.; Snow, Erica L.; Allen, Laura K.; Roscoe, Rod D.; Weston, Jennifer L.; Dai, Jianmin; McNamara, Danielle S. – Grantee Submission, 2015
Intelligent tutoring systems (ITSs) have been successful at improving students' performance across a variety of domains. To help achieve this widespread success, researchers have identified important behavioral and performance measures that can be used to guide instruction and feedback. Most systems, however, do not present these measures to the…
Descriptors: Intelligent Tutoring Systems, Educational Technology, Technology Uses in Education, Feedback (Response)
Botarleanu, Robert-Mihai; Dascalu, Mihai; Allen, Laura K.; Crossley, Scott Andrew; McNamara, Danielle S. – Grantee Submission, 2021
Text summarization is an effective reading comprehension strategy. However, summary evaluation is complex and must account for various factors including the summary and the reference text. This study examines a corpus of approximately 3,000 summaries based on 87 reference texts, with each summary being manually scored on a 4-point Likert scale.…
Descriptors: Computer Assisted Testing, Scoring, Natural Language Processing, Computer Software
Panaite, Marilena; Ruseti, Stefan; Dascalu, Mihai; Balyan, Renu; McNamara, Danielle S.; Trausan-Matu, Stefan – Grantee Submission, 2019
Intelligence Tutoring Systems (ITSs) focus on promoting knowledge acquisition, while providing relevant feedback during students' practice. Self-explanation practice is an effective method used to help students understand complex texts by leveraging comprehension. Our aim is to introduce a deep learning neural model for automatically scoring…
Descriptors: Computer Assisted Testing, Scoring, Intelligent Tutoring Systems, Natural Language Processing
Balyan, Renu; McCarthy, Kathryn S.; McNamara, Danielle S. – Grantee Submission, 2018
While hierarchical machine learning approaches have been used to classify texts into different content areas, this approach has, to our knowledge, not been used in the automated assessment of text difficulty. This study compared the accuracy of four classification machine learning approaches (flat, one-vs-one, one-vs-all, and hierarchical) using…
Descriptors: Artificial Intelligence, Classification, Comparative Analysis, Prediction
Balyan, Renu; McCarthy, Kathryn S.; McNamara, Danielle S. – International Educational Data Mining Society, 2017
This study examined how machine learning and natural language processing (NLP) techniques can be leveraged to assess the interpretive behavior that is required for successful literary text comprehension. We compared the accuracy of seven different machine learning classification algorithms in predicting human ratings of student essays about…
Descriptors: Artificial Intelligence, Natural Language Processing, Reading Comprehension, Literature