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HyeJin Hwang; Panayiota Kendeou; Kristen L. McMaster – Grantee Submission, 2025
Successful comprehension is only possible when children draw inferences about ideas implicit or missing in discourse. Supporting inference-making with explicit instruction must start early given its importance in comprehension and knowledge development. However, students who experience difficulties with early reading skills often do not receive…
Descriptors: Inferences, Video Technology, Reading Difficulties, Reading Skills
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Dragos-Georgian Corlatescu; Micah Watanabe; Stefan Ruseti; Mihai Dascalu; Danielle S. McNamara – Grantee Submission, 2024
Modeling reading comprehension processes is a critical task for Learning Analytics, as accurate models of the reading process can be used to match students to texts, identify appropriate interventions, and predict learning outcomes. This paper introduces an improved version of the Automated Model of Comprehension, namely version 4.0. AMoC has its…
Descriptors: Computer Software, Artificial Intelligence, Learning Analytics, Natural Language Processing
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Linh Huynh; Danielle S. McNamara – Grantee Submission, 2025
We conducted two experiments to assess the alignment between Generative AI (GenAI) text personalization and hypothetical readers' profiles. In Experiment 1, four LLMs (i.e., Claude 3.5 Sonnet; Llama; Gemini Pro 1.5; ChatGPT 4) were prompted to tailor 10 science texts (i.e., biology, chemistry, physics) to accommodate four different profiles…
Descriptors: Natural Language Processing, Profiles, Individual Differences, Semantics
Bogdan Nicula; Mihai Dascalu; Tracy Arner; Renu Balyan; Danielle S. McNamara – Grantee Submission, 2023
Text comprehension is an essential skill in today's information-rich world, and self-explanation practice helps students improve their understanding of complex texts. This study was centered on leveraging open-source Large Language Models (LLMs), specifically FLAN-T5, to automatically assess the comprehension strategies employed by readers while…
Descriptors: Reading Comprehension, Language Processing, Models, STEM Education
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
Subramonyam, Hariharan; Seifert, Colleen; Shah, Priti; Adar, Eytan – Grantee Submission, 2020
Learning from text is a "constructive" activity in which sentence-level information is combined by the reader to build coherent mental models. With increasingly complex texts, forming a mental model becomes challenging due to a lack of background knowledge, and limits in working memory and attention. To address this, we are taught…
Descriptors: Visual Aids, Natural Language Processing, Reading Strategies, Educational Technology
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
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Kole A. Norberg; Husni Almoubayyed; Logan De Ley; April Murphy; Kyle Weldon; Steve Ritter – Grantee Submission, 2024
Large language models (LLMs) offer an opportunity to make large-scale changes to educational content that would otherwise be too costly to implement. The work here highlights how LLMs (in particular GPT-4) can be prompted to revise educational math content ready for large scale deployment in real-world learning environments. We tested the ability…
Descriptors: Artificial Intelligence, Computer Software, Computational Linguistics, Educational Change
Nicula, Bogdan; Dascalu, Mihai; Newton, Natalie N.; Orcutt, Ellen; McNamara, Danielle S. – Grantee Submission, 2021
Learning to paraphrase supports both writing ability and reading comprehension, particularly for less skilled learners. As such, educational tools that integrate automated evaluations of paraphrases can be used to provide timely feedback to enhance learner paraphrasing skills more efficiently and effectively. Paraphrase identification is a popular…
Descriptors: Computational Linguistics, Feedback (Response), Classification, Learning Processes
Lippert, Anne; Gatewood, Jessica; Cai, Zhiqiang; Graesser, Arthur C. – Grantee Submission, 2019
One out of six adults in the United States possesses low literacy skills. Many advocates believe that technology can pave the way for these adults to gain the skills that they desire. This article describes an adaptive intelligent tutoring system called AutoTutor that is designed to teach adults comprehension strategies across different levels of…
Descriptors: Intelligent Tutoring Systems, Educational Technology, Adult Literacy, Skill Development
Botarleanu, Robert-Mihai; Dascalu, Mihai; Watanabe, Micah; Crossley, Scott Andrew; McNamara, Danielle S. – Grantee Submission, 2022
Age of acquisition (AoA) is a measure of word complexity which refers to the age at which a word is typically learned. AoA measures have shown strong correlations with reading comprehension, lexical decision times, and writing quality. AoA scores based on both adult and child data have limitations that allow for error in measurement, and increase…
Descriptors: Age Differences, Vocabulary Development, Correlation, Reading Comprehension
Shi, Genghu; Lippert, Anne M.; Shubeck, Keith; Fang, Ying; Chen, Su; Pavlik, Philip, Jr.; Greenberg, Daphne; Graesser, Arthur C. – Grantee Submission, 2018
Reading comprehension is often assessed by having students read passages and administering a test that assesses their understanding of the text. Shorter assessments may fail to give a full picture of comprehension ability while more thorough ones can be time consuming and costly. This study used data from a conversational intelligent tutoring…
Descriptors: Reading Comprehension, Intelligent Tutoring Systems, Adults, Accuracy
Wijekumar, Kausalai; Beerwinkle, Andrea; McKeown, Debra; Zhang, Shuai; Joshi, R. Maletesha – Grantee Submission, 2020
Main idea and summary are essential elements of reading comprehension. We report results from Grades 4 and 5 student performance on two years of state-mandated standardized reading testing which indicate that students perform statistically significantly lower on main idea and summary questions on the tests than any other question category. In this…
Descriptors: Reading Comprehension, Grade 4, Grade 5, Elementary School Students
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Kendeou, Panayiota; McMaster, Kristen L; Butterfuss, Reese; Kim, Jasmine; Bresina, Britta; Wagner, Kyle – Grantee Submission, 2019
We present an integrated theoretical framework guiding the use of visual narratives in educational settings. We focus specifically on the use of static and dynamic visual narratives to teach and assess inference skills in young children and discuss evidence to support the efficacy of this approach. In doing so, first we review the basis of the…
Descriptors: Inferences, Language Processing, Feedback (Response), Visual Aids
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Roscoe, Rod D.; Crossley, Scott A.; Snow, Erica L.; Varner, Laura K.; McNamara, Danielle S. – Grantee Submission, 2014
Automated essay scoring tools are often criticized on the basis of construct validity. Specifically, it has been argued that computational scoring algorithms may be unaligned to higher-level indicators of quality writing, such as writers' demonstrated knowledge and understanding of the essay topics. In this paper, we consider how and whether the…
Descriptors: Correlation, Essays, Scoring, Writing Evaluation
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