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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
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
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
Magliano, Joseph P.; Lampi, Jodi P.; Ray, Melissa; Chan, Greta – Grantee Submission, 2020
Coherent mental models for successful comprehension require inferences that establish semantic "bridges" between discourse constituents and "elaborations" that incorporate relevant background knowledge. While it is established that individual differences in the extent to which postsecondary students engage in these processes…
Descriptors: Reading Comprehension, Reading Strategies, Inferences, Reading Tests
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
Johnson, Amy M.; McCarthy, Kathryn S.; Kopp, Kristopher J.; Perret, Cecile A.; McNamara, Danielle S. – Grantee Submission, 2017
Intelligent tutoring systems for ill-defined domains, such as reading and writing, are critically needed, yet uncommon. Two such systems, the Interactive Strategy Training for Active Reading and Thinking (iSTART) and Writing Pal (W-Pal) use natural language processing (NLP) to assess learners' written (i.e., typed) responses and provide immediate,…
Descriptors: Reading Instruction, Writing Instruction, Intelligent Tutoring Systems, Reading Strategies
Jackson, Tanner; Boonthum-Denecke, Chutima; McNamara, Danielle – Journal of Interactive Learning Research, 2015
Intelligent Tutoring Systems (ITSs) are situated in a potential struggle between effective pedagogy and system enjoyment and engagement. iSTART (Interactive Strategy Training for Active Reading and Thinking), a reading strategy tutoring system in which students practice generating self-explanations and using reading strategies, employs two devices…
Descriptors: Intelligent Tutoring Systems, Educational Technology, Reading Strategies, Tutoring
Jackson, G. Tanner; Boonthum-Denecke, Chutima; McNamara, Danielle S. – Grantee Submission, 2015
Intelligent Tutoring Systems (ITSs) are situated in a potential struggle between effective pedagogy and system enjoyment and engagement. iSTART, a reading strategy tutoring system in which students practice generating self-explanations and using reading strategies, employs two devices to engage the user. The first is natural language processing…
Descriptors: Natural Language Processing, Feedback (Response), Intelligent Tutoring Systems, Reading Strategies
Varner, Laura K.; Jackson, G. Tanner; Snow, Erica L.; McNamara, Danielle S. – Grantee Submission, 2013
This study expands upon an existing model of students' reading comprehension ability within an intelligent tutoring system. The current system evaluates students' natural language input using a local student model. We examine the potential to expand this model by assessing the linguistic features of self-explanations aggregated across entire…
Descriptors: Reading Comprehension, Intelligent Tutoring Systems, Natural Language Processing, Reading Ability
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
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
Graesser, Arthur; McNamara, Danielle – Educational Psychologist, 2010
This article discusses the occurrence and measurement of self-regulated learning (SRL) both in human tutoring and in computer tutors with agents that hold conversations with students in natural language and help them learn at deeper levels. One challenge in building these computer tutors is to accommodate, encourage, and scaffold SRL because these…
Descriptors: Intelligent Tutoring Systems, Metacognition, Tutors, Natural Language Processing