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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
Allen, Laura K.; Likens, Aaron D.; McNamara, Danielle S. – Grantee Submission, 2018
The assessment of writing proficiency generally includes analyses of the specific linguistic and rhetorical features contained in the singular essays produced by students. However, researchers have recently proposed that an individual's ability to flexibly adapt the linguistic properties of their writing might more closely capture writing skill.…
Descriptors: Writing Evaluation, Writing Tests, Computer Assisted Testing, Writing Skills
Katz, Sandra; Albacete, Patricia; Jordan, Pamela – Grantee Submission, 2016
This poster reports on a study that compared three types of summaries at the end of natural-language tutorial dialogues and a no-dialogue control, to determine which type of summary, if any, best predicted learning gains. Although we found no significant differences between conditions, analyses of gender differences indicate that female students…
Descriptors: Natural Language Processing, Intelligent Tutoring Systems, Reflection, Dialogs (Language)
Allen, Laura K.; Jacovina, Matthew E.; Dascalu, Mihai; Roscoe, Rod D.; Kent, Kevin M.; Likens, Aaron D.; McNamara, Danielle S. – International Educational Data Mining Society, 2016
This study investigates how and whether information about students' writing can be recovered from basic behavioral data extracted during their sessions in an intelligent tutoring system for writing. We calculate basic and time-sensitive keystroke indices based on log files of keys pressed during students' writing sessions. A corpus of prompt-based…
Descriptors: Writing Processes, Intelligent Tutoring Systems, Natural Language Processing, Feedback (Response)
Ziegler, Nicole; Meurers, Detmar; Rebuschat, Patrick; Ruiz, Simón; Moreno-Vega, José L.; Chinkina, Maria; Li, Wenjing; Grey, Sarah – Language Learning, 2017
Despite the promise of research conducted at the intersection of computer-assisted language learning (CALL), natural language processing, and second language acquisition, few studies have explored the potential benefits of using intelligent CALL systems to deepen our understanding of the process and products of second language (L2) learning. The…
Descriptors: Interdisciplinary Approach, Second Language Learning, Language Acquisition, Intelligent Tutoring Systems
Khodeir, Nabila Ahmed; Elazhary, Hanan; Wanas, Nayer – International Journal of Information and Learning Technology, 2018
Purpose: The purpose of this paper is to present an algorithm to generate story problems via controlled parameters in the domain of mathematics. The generation process is performed in the problem generation module in the context of an intelligent tutoring system suggested in this paper. Controlling the question parameters allows for adapting the…
Descriptors: Problem Solving, Teaching Methods, Difficulty Level, Natural Language Processing
Allen, Laura K.; Likens, Aaron D.; McNamara, Danielle S. – Grantee Submission, 2018
The assessment of argumentative writing generally includes analyses of the specific linguistic and rhetorical features contained in the individual essays produced by students. However, researchers have recently proposed that an individual's ability to flexibly adapt the linguistic properties of their writing may more accurately capture their…
Descriptors: Writing (Composition), Persuasive Discourse, Essays, Language Usage
Suleman, Raja M.; Mizoguchi, Riichiro; Ikeda, Mitsuru – International Journal of Artificial Intelligence in Education, 2016
Negotiation mechanism using conversational agents (chatbots) has been used in Open Learner Models (OLM) to enhance learner model accuracy and provide opportunities for learner reflection. Using chatbots that allow for natural language discussions has shown positive learning gains in students. Traditional OLMs assume a learner to be able to manage…
Descriptors: Metacognition, Intelligent Tutoring Systems, Natural Language Processing, Models
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
Michalenko, Joshua J.; Lan, Andrew S.; Waters, Andrew E.; Grimaldi, Philip J.; Baraniuk, Richard G. – International Educational Data Mining Society, 2017
An important, yet largely unstudied problem in student data analysis is to detect "misconceptions" from students' responses to "open-response" questions. Misconception detection enables instructors to deliver more targeted feedback on the misconceptions exhibited by many students in their class, thus improving the quality of…
Descriptors: Data Analysis, Misconceptions, Student Attitudes, Feedback (Response)
Crossley, Scott; Ocumpaugh, Jaclyn; Labrum, Matthew; Bradfield, Franklin; Dascalu, Mihai; Baker, Ryan S. – International Educational Data Mining Society, 2018
A number of studies have demonstrated strong links between students' language features (as found in spoken and written production) and their math performance. However, no studies have examined links between the students' language features and measures of their Math Identity. This project extends prior studies that use natural language processing…
Descriptors: Correlation, Speech Communication, Written Language, Mathematics Achievement
Crossley, Scott; Liu, Ran; McNamara, Danielle – Grantee Submission, 2017
A number of studies have demonstrated links between linguistic knowledge and performance in math. Studies examining these links in first language speakers of English have traditionally relied on correlational analyses between linguistic knowledge tests and standardized math tests. For second language (L2) speakers, the majority of studies have…
Descriptors: Predictor Variables, Mathematics Achievement, English (Second Language), Natural Language Processing
Dzikovska, Myroslava; Steinhauser, Natalie; Farrow, Elaine; Moore, Johanna; Campbell, Gwendolyn – International Journal of Artificial Intelligence in Education, 2014
Within STEM domains, physics is considered to be one of the most difficult topics to master, in part because many of the underlying principles are counter-intuitive. Effective teaching methods rely on engaging the student in active experimentation and encouraging deep reasoning, often through the use of self-explanation. Supporting such…
Descriptors: Intelligent Tutoring Systems, Electronics, Energy, Science Instruction
Graesser, Arthur; Li, Haiying; Forsyth, Carol – Grantee Submission, 2014
Learning is facilitated by conversational interactions both with human tutors and with computer agents that simulate human tutoring and ideal pedagogical strategies. In this article, we describe some intelligent tutoring systems (e.g., AutoTutor) in which agents interact with students in natural language while being sensitive to their cognitive…
Descriptors: Intelligent Tutoring Systems, Teaching Methods, Computer Simulation, Dialogs (Language)

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