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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
Bogdan Nicula; Marilena Panaite; Tracy Arner; Renu Balyan; Mihai Dascalu; Danielle S. McNamara – Grantee Submission, 2023
Self-explanation practice is an effective method to support students in better understanding complex texts. This study focuses on automatically assessing the comprehension strategies employed by readers while understanding STEM texts. Data from 3 datasets (N = 11,833) with self-explanations annotated on different comprehension strategies (i.e.,…
Descriptors: Reading Strategies, Reading Comprehension, Metacognition, 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
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Kopp, Kristopher J.; Johnson, Amy M.; Crossley, Scott A.; McNamara, Danielle S. – Grantee Submission, 2017
An NLP algorithm was developed to assess question quality to inform feedback on questions generated by students within iSTART (an intelligent tutoring system that teaches reading strategies). A corpus of 4575 questions was coded using a four-level taxonomy. NLP indices were calculated for each question and machine learning was used to predict…
Descriptors: Reading Comprehension, Reading Instruction, Intelligent Tutoring Systems, Reading Strategies
Stefan Ruseti; Mihai Dascalu; Amy M. Johnson; Renu Balyan; Kristopher J. Kopp; Danielle S. McNamara – Grantee Submission, 2018
This study assesses the extent to which machine learning techniques can be used to predict question quality. An algorithm based on textual complexity indices was previously developed to assess question quality to provide feedback on questions generated by students within iSTART (an intelligent tutoring system that teaches reading strategies). In…
Descriptors: Questioning Techniques, Artificial Intelligence, Networks, Classification
Benjamin D. Nye; Arthur C. Graesser; Xiangen Hu – Grantee Submission, 2014
AutoTutor is a natural language tutoring system that has produced learning gains across multiple domains (e.g., computer literacy, physics, critical thinking). In this paper, we review the development, key research findings, and systems that have evolved from AutoTutor. First, the rationale for developing AutoTutor is outlined and the advantages…
Descriptors: Intelligent Tutoring Systems, Natural Language Processing, Computer Software, Artificial Intelligence
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Benjamin D. Nye; Arthur C. Graesser; Xiangen Hu – International Journal of Artificial Intelligence in Education, 2014
AutoTutor is a natural language tutoring system that has produced learning gains across multiple domains (e.g., computer literacy, physics, critical thinking). In this paper, we review the development, key research findings, and systems that have evolved from AutoTutor. First, the rationale for developing AutoTutor is outlined and the advantages…
Descriptors: Intelligent Tutoring Systems, Natural Language Processing, Computer Software, Artificial Intelligence