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Arroyo, Ivon; Woolf, Beverly Park; Burelson, Winslow; Muldner, Kasia; Rai, Dovan; Tai, Minghui – International Journal of Artificial Intelligence in Education, 2014
This article describes research results based on multiple years of experimentation and real-world experience with an adaptive tutoring system named Wayang Outpost. The system represents a novel adaptive learning technology that has shown successful outcomes with thousands of students, and provided teachers with valuable information about…
Descriptors: Intelligent Tutoring Systems, Multimedia Instruction, Mathematics Instruction, Learner Engagement
Matsuda, Noboru; Yarzebinski, Evelyn; Keiser, Victoria; Raizada, Rohan; Stylianides, Gabriel J.; Koedinger, Kenneth R. – International Journal of Artificial Intelligence in Education, 2013
In this paper we investigate how competition among tutees in the context of learning by teaching affects tutors' engagement as well as tutor learning. We conducted this investigation by incorporating a competitive Game Show feature into an online learning environment where students learn to solve algebraic equations by teaching a synthetic…
Descriptors: Teaching Methods, Competition, Educational Games, Equations (Mathematics)
Baker, Ryan S. J. D.; Goldstein, Adam B.; Heffernan, Neil T. – International Journal of Artificial Intelligence in Education, 2011
Intelligent tutors have become increasingly accurate at detecting whether a student knows a skill, or knowledge component (KC), at a given time. However, current student models do not tell us exactly at which point a KC is learned. In this paper, we present a machine-learned model that assesses the probability that a student learned a KC at a…
Descriptors: Intelligent Tutoring Systems, Mastery Learning, Probability, Knowledge Level
Gong, Yue; Beck, Joseph E.; Heffernan, Neil T. – International Journal of Artificial Intelligence in Education, 2011
Student modeling is a fundamental concept applicable to a variety of intelligent tutoring systems (ITS). However, there is not a lot of practical guidance on how to construct and train such models. This paper compares two approaches for student modeling, Knowledge Tracing (KT) and Performance Factors Analysis (PFA), by evaluating their predictive…
Descriptors: Intelligent Tutoring Systems, Factor Analysis, Performance Factors, Models
Rowe, Jonathan P.; Shores, Lucy R.; Mott, Bradford W.; Lester, James C. – International Journal of Artificial Intelligence in Education, 2011
A key promise of narrative-centered learning environments is the ability to make learning engaging. However, there is concern that learning and engagement may be at odds in these game-based learning environments. This view suggests that, on the one hand, students interacting with a game-based learning environment may be engaged but unlikely to…
Descriptors: Problem Solving, Educational Technology, Virtual Classrooms, Educational Environment
Arroyo, Ivon; Royer, James M.; Woolf, Beverly P. – International Journal of Artificial Intelligence in Education, 2011
This article integrates research in intelligent tutors with psychology studies of memory and math fluency (the speed to retrieve or calculate answers to basic math operations). It describes the impact of computer software designed to improve either strategic behavior or math fluency. Both competencies are key to improved performance and both…
Descriptors: Computer Software, Short Term Memory, Tutors, Mathematics Instruction

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