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Walker, Erin; Rummel, Nikol; Koedinger, Kenneth R. – International Journal of Artificial Intelligence in Education, 2014
Adaptive collaborative learning support (ACLS) involves collaborative learning environments that adapt their characteristics, and sometimes provide intelligent hints and feedback, to improve individual students' collaborative interactions. ACLS often involves a system that can automatically assess student dialogue, model effective and…
Descriptors: Algebra, Peer Teaching, Tutoring, Cooperative Learning
Boyer, Kristy Elizabeth; Phillips, Robert; Ingram, Amy; Ha, Eun Young; Wallis, Michael; Vouk, Mladen; Lester, James – International Journal of Artificial Intelligence in Education, 2011
Identifying effective tutorial dialogue strategies is a key issue for intelligent tutoring systems research. Human-human tutoring offers a valuable model for identifying effective tutorial strategies, but extracting them is a challenge because of the richness of human dialogue. This article addresses that challenge through a machine learning…
Descriptors: Markov Processes, Intelligent Tutoring Systems, Tutoring, Program Effectiveness
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
Heilman, Michael; Collins-Thompson, Kevyn; Callan, Jamie; Eskenazi, Maxine; Juffs, Alan; Wilson, Lois – International Journal of Artificial Intelligence in Education, 2010
The REAP tutoring system provides individualized and adaptive English as a Second Language vocabulary practice. REAP can automatically personalize instruction by providing practice readings about topics that match interests as well as domain-based, cognitive objectives. While most previous research on motivation in intelligent tutoring…
Descriptors: Incentives, Cognitive Objectives, Intelligent Tutoring Systems, Motivation
Suraweera, Pramuditha; Mitrovic, Antonija; Martin, Brent – International Journal of Artificial Intelligence in Education, 2010
Intelligent Tutoring Systems (ITS) are effective tools for education. However, developing them is a labour-intensive and time-consuming process. A major share of the effort is devoted to acquiring the domain knowledge that underlies the system's intelligence. The goal of this research is to reduce this knowledge acquisition bottleneck and better…
Descriptors: Intelligent Tutoring Systems, Programming, Engineering, Tutoring
Bratt, Elizabeth Owen – International Journal of Artificial Intelligence in Education, 2009
This paper describes the role of simulation-based training in the military. Interviews and observations of military instructors in the damage control and shiphandling domains provide examples of how the instructors extend the student's training beyond the well-defined simulated world with qualitative reasoning about context, hypothetical variants,…
Descriptors: Intelligent Tutoring Systems, Military Training, Simulation, Tutoring
Siler, Stephanie Ann; VanLehn, Kurt – International Journal of Artificial Intelligence in Education, 2009
Face-to-face (FTF) human-human tutoring has ranked among the most effective forms of instruction. However, because computer-mediated (CM) tutoring is becoming increasingly common, it is instructive to evaluate its effectiveness relative to face-to-face tutoring. Does the lack of spoken, face-to-face interaction affect learning gains and…
Descriptors: Feedback (Response), Undergraduate Students, Student Motivation, Tutoring

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