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Schussler, Deborah; Frank, Jennifer; Lee, Tsan-Kuang; Mahfouz, Julia – Grantee Submission, 2017
Nearly one in three students in the United States today is negatively impacted by bullying. Teachers can play a critical role in stopping bullying-related violence, but many struggle with how to engage students in difficult conversations. Traditional classroom-based pedagogy used to teach communication skills (e.g., modeling & role-play) is…
Descriptors: Computer Simulation, Artificial Intelligence, Role Playing, Teaching Methods
DeRocchis, Anthony M.; Michalenko, Ashley; Boucheron, Laura E.; Stochaj, Steven J. – Grantee Submission, 2018
This Innovative Practice Category Work In Progress paper presents an application of machine learning and data mining to student performance data in an undergraduate electrical engineering program. We are developing an analytical approach to enhance retention in the program especially among underrepresented groups. Our approach will provide…
Descriptors: Engineering Education, Data Analysis, Undergraduate Students, Artificial Intelligence
Heffernan, Neil T.; Ostrow, Korinn S.; Kelly, Kim; Selent, Douglas; Van Inwegen, Eric G.; Xiong, Xiaolu; Williams, Joseph Jay – Grantee Submission, 2016
Due to substantial scientific and practical progress, learning technologies can effectively adapt to the characteristics and needs of students. This article considers how learning technologies can adapt over time by crowdsourcing contributions from teachers and students -- explanations, feedback, and other pedagogical interactions. Considering the…
Descriptors: Artificial Intelligence, Educational Technology, Student Needs, Electronic Publishing
Sao Pedro, Michael A.; Baker, Ryan S. J. d.; Gobert, Janice D.; Montalvo, Orlando; Nakama, Adam – Grantee Submission, 2013
We present work toward automatically assessing and estimating science inquiry skills as middle school students engage in inquiry within a physical science microworld. Towards accomplishing this goal, we generated machine-learned models that can detect when students test their articulated hypotheses, design controlled experiments, and engage in…
Descriptors: Artificial Intelligence, Inquiry, Middle School Students, Physical Sciences
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
Peer reviewed Peer reviewed
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Sao Pedro, Michael A.; Baker, Ryan S. J. d.; Gobert, Janice D. – Grantee Submission, 2012
Data-mined models often achieve good predictive power, but sometimes at the cost of interpretability. We investigate here if selecting features to increase a model's construct validity and interpretability also can improve the model's ability to predict the desired constructs. We do this by taking existing models and reducing the feature set to…
Descriptors: Content Validity, Data Interpretation, Models, Predictive Validity
Peer reviewed Peer reviewed
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Ward, W.; Cole, R.; Bolanos, D.; Buchenroth-Martin, C.; Svirsky, E.; Van Vuuren, S.; Weston, T.; Zheng, J.; Becker, L. – Grantee Submission, 2011
This paper describes My Science Tutor (MyST), an intelligent tutoring system designed to improve science learning by students in 3rd, 4th and 5th grades (7 to 11 years old) through conversational dialogs with a virtual science tutor. In our study, individual students engage in spoken dialogs with the virtual tutor Marni during 15 to 20 minute…
Descriptors: Elementary School Science, Elementary School Students, Science Education, Intelligent Tutoring Systems
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