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Showing 1 to 15 of 78 results Save | Export
Liu, Ran; Stamper, John; Davenport, Jodi – Grantee Submission, 2018
Temporal analyses are critical to understanding learning processes, yet understudied in education research. Data from different sources are often collected at different grain sizes, which are difficult to integrate. Making sense of data at many levels of analysis, including the most detailed levels, is highly time-consuming. In this paper, we…
Descriptors: Intelligent Tutoring Systems, Learning, Data Analysis, Student Development
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Likens, Aaron D.; McCarthy, Kathryn S.; Allen, Laura K.; McNamara, Danielle D. – Grantee Submission, 2018
Self-explanations are commonly used to assess on-line reading comprehension processes. However, traditional methods of analysis ignore important temporal variations in these explanations. This study investigated how dynamical systems theory could be used to reveal linguistic patterns that are predictive of self-explanation quality. High school…
Descriptors: Reading Comprehension, High School Students, Content Area Reading, Sciences
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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
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Chen, Su; Lippert, Anne; Shi, Genghu; Fang, Ying; Graesser, Arthur C. – Grantee Submission, 2018
This paper describes a novel automated disengagement tracing system (DTS) that detects mind wandering in students using AutoTutor, an Intelligent Tutoring System (ITS) with conversational agents. DTS is based on an unsupervised learning method and thus does not rely on any self-reports of disengagement. We analyzed the reading time and response…
Descriptors: Learner Engagement, Intelligent Tutoring Systems, Reading Comprehension, Adult Literacy
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
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Fang, Ying; Shubeck, Keith; Lippert, Anne; Chen, Qinyu; Shi, Genghu; Feng, Shi; Gatewood, Jessica; Chen, Su; Cai, Zhiqiang; Pavlik, Philip; Frijters, Jan; Greenberg, Daphne; Graesser, Arthur – Grantee Submission, 2018
A common goal of Intelligent Tutoring Systems (ITS) is to provide learning environments that adapt to the varying abilities and characteristics of users. To do this, researchers must identify the learning patterns exhibited by those interacting with the system. In the present work, we use clustering analysis to capture learning patterns in over…
Descriptors: Intelligent Tutoring Systems, Educational Technology, Technology Uses in Education, Adult Literacy
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Shi, Genghu; Hampton, Andrew J.; Chen, Su; Fang, Ying; Graesser, Arthur C. – Grantee Submission, 2018
We developed a version of AutoTutor that helps struggling adult learners improve their comprehension strategies through conversational agents. We hypothesized that the accuracy and time to answer questions during the conversation could be diagnostic of their mastery of different reading comprehension components: words, textbase, situation model,…
Descriptors: Intelligent Tutoring Systems, Educational Technology, Technology Uses in Education, Reading Difficulties
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Shi, Genghu; Pavlik, Philip, Jr.; Graesser, Arthur – Grantee Submission, 2017
After developing an intelligent tutoring system (ITS), or any other class of learning environments, one of the first questions that should be asked is whether the system was effective in helping students learn the targeted skills or subject matter. In this study, we employed two educational data mining models (Additive Factor Model, AFM and…
Descriptors: Intelligent Tutoring Systems, Educational Technology, Technology Uses in Education, Program Effectiveness
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Hartmann, Christian; Olsen, Jennifer K.; Brand, Charleen; Aleven, Vincent; Rummel, Nikol – Grantee Submission, 2017
Social interdependence is a key concept in CSCL research. However, investigations of students' positive and negative interdependence during collaborative activities have often relied on self-report, rather than dialogue analysis. Bringing together politeness and social interdependence theory, we assessed "dialogue indicators" of positive…
Descriptors: Elementary School Students, Computer Uses in Education, Cooperative Learning, Intelligent Tutoring Systems
Doroudi, Shayan; Aleven, Vincent; Brunskill, Emma – Grantee Submission, 2017
The gold standard for identifying more effective pedagogical approaches is to perform an experiment. Unfortunately, frequently a hypothesized alternate way of teaching does not yield an improved effect. Given the expense and logistics of each experiment, and the enormous space of potential ways to improve teaching, it would be highly preferable if…
Descriptors: Teaching Methods, Matrices, Evaluation Methods, Models
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Graesser, Arthur C.; Forsyth, Carol M.; Lehman, Blair A. – Grantee Submission, 2017
Background: Pedagogical agents are computerized talking heads or embodied animated avatars that help students learn by performing actions and holding conversations with the students in natural language. Dialogues occur between a tutor agent and the student in the case of AutoTutor and other intelligent tutoring systems with natural language…
Descriptors: Intelligent Tutoring Systems, Computer Managed Instruction, Natural Language Processing, Instructional Design
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Olsen, Jennifer K.; Rummel, Nikol; Aleven, Vincent – Grantee Submission, 2017
Collaborative and individual learning are both frequently used in classrooms to support learning. However, little research has investigated the benefits of combining individual and collaborative learning, as compared to learning only individually or only collaboratively. With our study, we address this research gap. We compared a combined…
Descriptors: Cooperative Learning, Grade 4, Grade 5, Elementary School Students
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Stone, Melissa L.; Kent, Kevin M.; Roscoe, Rod D.; Corley, Kathleen M.; Allen, Laura K.; McNamara, Danielle S. – Grantee Submission, 2017
This chapter explores three broad principles of user-centered design methodologies: participatory design, iteration, and usability considerations. The authors highlight the importance of considering teachers as a prominent type of ITS end user, by describing the barriers teachers face as users and their role in educational technology design. To…
Descriptors: Intelligent Tutoring Systems, Design, Usability, Barriers
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Dascalu, Mihai; Jacovina, Matthew E.; Soto, Christian M.; Allen, Laura K.; Dai, Jianmin; Guerrero, Tricia A.; McNamara, Danielle S. – Grantee Submission, 2017
iSTART is a web-based reading comprehension tutor. A recent translation of iSTART from English to Spanish has made the system available to a new audience. In this paper, we outline several challenges that arose during the development process, specifically focusing on the algorithms that drive the feedback. Several iSTART activities encourage…
Descriptors: Spanish, Reading Comprehension, Natural Language Processing, Intelligent Tutoring Systems
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Johnson, Amy M.; Guerrero, Tricia A.; Tighe, Elizabeth L.; McNamara, Danielle S. – Grantee Submission, 2017
There is little empirical research available on the substantial problem of adult low literacy rates, and limited educational technologies are available to address distinct instructional needs of this population. This paper reports on development and testing of a version of Interactive Strategy Training for Active Reading and Thinking (iSTART) for…
Descriptors: Reading Comprehension, Reading Instruction, Intelligent Tutoring Systems, Reading Strategies
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