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Matthew T. McCrudden; Linh Huynh; Bailing Lyu; Jonna M. Kulikowich; Danielle S. McNamara – Grantee Submission, 2024
Readers build a mental representation of text during reading. The coherence building processes readers use to build a mental representation during reading is key to comprehension. We examined the effects of self- explanation on coherence building processes as undergraduates (n =51) read five complementary texts about natural selection and…
Descriptors: Reading Processes, Reading Comprehension, Undergraduate Students, Evolution
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
Muhsin Menekse – Grantee Submission, 2023
Generative artificial intelligence (AI) technologies, such as large language models (LLMs) and diffusion model image and video generators, can transform learning and teaching experiences by providing students and instructors with access to a vast amount of information and create innovative learning and teaching materials in a very efficient way…
Descriptors: Educational Trends, Engineering Education, Artificial Intelligence, Technology Uses in Education
Kylie L. Anglin; Vivian C. Wong; Arielle Boguslav – Grantee Submission, 2021
Though there is widespread recognition of the importance of implementation research, evaluators often face intense logistical, budgetary, and methodological challenges in their efforts to assess intervention implementation in the field. This article proposes a set of natural language processing techniques called semantic similarity as an…
Descriptors: Natural Language Processing, Program Implementation, Measurement Techniques, Intervention
Patience Stevens; David C. Plaut – Grantee Submission, 2022
The morphological structure of complex words impacts how they are processed during visual word recognition. This impact varies over the course of reading acquisition and for different languages and writing systems. Many theories of morphological processing rely on a decomposition mechanism, in which words are decomposed into explicit…
Descriptors: Written Language, Morphology (Languages), Word Recognition, Reading Processes
Razvan Paroiu; Stefan Ruseti; Mihai Dascalu; Stefan Trausan-Matu; Danielle S. McNamara – Grantee Submission, 2023
The exponential growth of scientific publications increases the effort required to identify relevant articles. Moreover, the scale of studies is a frequent barrier to research as the majority of studies are low or medium-scaled and do not generalize well while lacking statistical power. As such, we introduce an automated method that supports the…
Descriptors: Science Education, Educational Research, Scientific and Technical Information, Journal Articles
Ying Fang; Tong Li; Linh Huynh; Katerina Christhilf; Rod D. Roscoe; Danielle S. McNamara – Grantee Submission, 2023
Literacy assessment is essential for effective literacy instruction and training. However, traditional paper-based literacy assessments are typically decontextualized and may cause stress and anxiety for test takers. In contrast, serious games and game environments allow for the assessment of literacy in more authentic and engaging ways, which has…
Descriptors: Literacy, Student Evaluation, Educational Games, Literacy Education
Lippert, Anne; Shubeck, Keith; Morgan, Brent; Hampton, Andrew; Graesser, Arthur – Grantee Submission, 2020
This article describes designs that use multiple conversational agents within the framework of intelligent tutoring systems. Agents in this case are computerized talking heads or embodied animated avatars that help students learn by performing actions and holding conversations with them in natural language. The earliest conversational intelligent…
Descriptors: Intelligent Tutoring Systems, Man Machine Systems, Natural Language Processing, Educational Technology
Balyan, Renu; McCarthy, Kathryn S.; McNamara, Danielle S. – Grantee Submission, 2020
For decades, educators have relied on readability metrics that tend to oversimplify dimensions of text difficulty. This study examines the potential of applying advanced artificial intelligence methods to the educational problem of assessing text difficulty. The combination of hierarchical machine learning and natural language processing (NLP) is…
Descriptors: Natural Language Processing, Artificial Intelligence, Man Machine Systems, Classification
Sonia, Allison N.; Joseph, Magliano P.; McCarthy, Kathryn S.; Creer, Sarah D.; McNamara, Danielle S.; Allen, Laura K. – Grantee Submission, 2022
The constructed responses individuals generate while reading can provide insights into their coherence-building processes. The current study examined how the cohesion of constructed responses relates to performance on an integrated writing task. Participants (N = 95) completed a multiple document reading task wherein they were prompted to think…
Descriptors: Natural Language Processing, Connected Discourse, Reading Processes, Writing Skills
Konishi, Haruka; Brezack, Natalie; Golinkoff, Roberta Michnick; Hirsh-Pasek, Kathy – Grantee Submission, 2019
Infants appear to progress from universal to language-specific event perception. In Japanese, two different verbs describe a person crossing a "bounded ground" (e.g., street) versus an unbounded ground (e.g., field) while in English, the same verb -- "crossing" -- describes both events. Interestingly, Japanese "and"…
Descriptors: Infants, Cognitive Processes, Verbs, Japanese
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
Li, Haiying; Cai, Zhiqiang; Graesser, Arthur – Grantee Submission, 2018
In this study we developed and evaluated a crowdsourcing-based latent semantic analysis (LSA) approach to computerized summary scoring (CSS). LSA is a frequently used mathematical component in CSS, where LSA similarity represents the extent to which the to-be-graded target summary is similar to a model summary or a set of exemplar summaries.…
Descriptors: Computer Assisted Testing, Scoring, Semantics, Evaluation Methods
Chen, Su; Fang, Ying; Shi, Genghu; Sabatini, John; Greenberg, Daphne; Frijters, Jan; Graesser, Arthur C. – Grantee Submission, 2021
This paper describes a new automated disengagement tracking system (DTS) that detects learners' maladaptive behaviors, e.g. mind-wandering and impetuous responding, in an intelligent tutoring system (ITS), called AutoTutor. AutoTutor is a conversation-based intelligent tutoring system designed to help adult literacy learners improve their reading…
Descriptors: Intelligent Tutoring Systems, Artificial Intelligence, Attention, Adult Literacy
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