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Sanz Ausin, Markel; Maniktala, Mehak; Barnes, Tiffany; Chi, Min – International Journal of Artificial Intelligence in Education, 2023
While Reinforcement learning (RL), especially Deep RL (DRL), has shown outstanding performance in video games, little evidence has shown that DRL can be successfully applied to human-centric tasks where the ultimate RL goal is to make the "human-agent interactions" productive and fruitful. In real-life, complex, human-centric tasks, such…
Descriptors: Artificial Intelligence, Intelligent Tutoring Systems, Teaching Methods, Learning Activities
Zhou, Guojing; Azizsoltani, Hamoon; Ausin, Markel Sanz; Barnes, Tiffany; Chi, Min – International Journal of Artificial Intelligence in Education, 2022
In interactive e-learning environments such as Intelligent Tutoring Systems, pedagogical decisions can be made at different levels of granularity. In this work, we focus on making decisions at "two levels": whole problems vs. single steps and explore three types of granularity: "problem-level only" ("Prob-Only"),…
Descriptors: Electronic Learning, Intelligent Tutoring Systems, Decision Making, Problem Solving
Cody, Christa; Maniktala, Mehak; Lytle, Nicholas; Chi, Min; Barnes, Tiffany – International Journal of Artificial Intelligence in Education, 2022
Research has shown assistance can provide many benefits to novices lacking the mental models needed for problem solving in a new domain. However, varying approaches to assistance, such as subgoals and next-step hints, have been implemented with mixed results. Next-Step hints are common in data-driven tutors due to their straightforward generation…
Descriptors: Comparative Analysis, Prior Learning, Intelligent Tutoring Systems, Problem Solving
Maniktala, Mehak; Cody, Christa; Barnes, Tiffany; Chi, Min – International Journal of Artificial Intelligence in Education, 2020
Within intelligent tutoring systems, considerable research has investigated hints, including how to generate data-driven hints, what hint content to present, and when to provide hints for optimal learning outcomes. However, less attention has been paid to "how" hints are presented. In this paper, we propose a new hint delivery mechanism…
Descriptors: Intelligent Tutoring Systems, Cues, Computer Interfaces, Design
Price, Thomas W.; Dong, Yihuan; Zhi, Rui; Paaßen, Benjamin; Lytle, Nicholas; Cateté, Veronica; Barnes, Tiffany – International Journal of Artificial Intelligence in Education, 2019
In the domain of programming, a growing number of algorithms automatically generate data-driven, next-step hints that suggest how students should edit their code to resolve errors and make progress. While these hints have the potential to improve learning if done well, few evaluations have directly assessed or compared the quality of different…
Descriptors: Comparative Analysis, Programming Languages, Data Analysis, Evaluation Methods
Mostafavi, Behrooz; Barnes, Tiffany – International Journal of Artificial Intelligence in Education, 2017
Deductive logic is essential to a complete understanding of computer science concepts, and is thus fundamental to computer science education. Intelligent tutoring systems with individualized instruction have been shown to increase learning gains. We seek to improve the way deductive logic is taught in computer science by developing an intelligent,…
Descriptors: Artificial Intelligence, Problem Solving, Educational Technology, Technology Uses in Education
Stamper, John; Eagle, Michael; Barnes, Tiffany; Croy, Marvin – International Journal of Artificial Intelligence in Education, 2013
We have augmented the Deep Thought logic tutor with a Hint Factory that generates data-driven, contextspecific hints for an existing computer aided instructional tool. We investigate the impact of the Hint Factory's automatically generated hints on educational outcomes in a switching replications experiment that shows that hints help students…
Descriptors: Intelligent Tutoring Systems, Logical Thinking, Problem Solving, College Students
Stamper, John; Barnes, Tiffany; Croy, Marvin – International Journal of Artificial Intelligence in Education, 2011
The Hint Factory is an implementation of our novel method to automatically generate hints using past student data for a logic tutor. One disadvantage of the Hint Factory is the time needed to gather enough data on new problems in order to provide hints. In this paper we describe the use of expert sample solutions to "seed" the hint generation…
Descriptors: Cues, Prompting, Learning Strategies, Teaching Methods