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Showing all 13 results
Matsuda, Noboru; Cohen, William W.; Koedinger, Kenneth R. – International Journal of Artificial Intelligence in Education, 2015
SimStudent is a machine-learning agent initially developed to help novice authors to create cognitive tutors without heavy programming. Integrated into an existing suite of software tools called Cognitive Tutor Authoring Tools (CTAT), SimStudent helps authors to create an expert model for a cognitive tutor by tutoring SimStudent on how to solve…
Descriptors: Intelligent Tutoring Systems, Programming, Computer Simulation, Models
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
Adamson, David; Dyke, Gregory; Jang, Hyeju; Rosé, Carolyn Penstein – International Journal of Artificial Intelligence in Education, 2014
This paper investigates the use of conversational agents to scaffold on-line collaborative learning discussions through an approach called Academically Productive Talk (APT). In contrast to past work on dynamic support for collaborative learning, where agents were used to elevate conceptual depth by leading students through directed lines of…
Descriptors: Cooperative Learning, Scaffolding (Teaching Technique), Intelligent Tutoring Systems, Electronic Learning
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
Pardos, Zachary A.; Dailey, Matthew D.; Heffernan, Neil T. – International Journal of Artificial Intelligence in Education, 2011
The well established, gold standard approach to finding out what works in education research is to run a randomized controlled trial (RCT) using a standard pre-test and post-test design. RCTs have been used in the intelligent tutoring community for decades to determine which questions and tutorial feedback work best. Practically speaking, however,…
Descriptors: Feedback (Response), Intelligent Tutoring Systems, Pretests Posttests, Educational Research
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
Chieu, Vu Minh; Luengo, Vanda; Vadcard, Lucile; Tonetti, Jerome – International Journal of Artificial Intelligence in Education, 2010
Cognitive approaches have been used for student modeling in intelligent tutoring systems (ITSs). Many of those systems have tackled fundamental subjects such as mathematics, physics, and computer programming. The change of the student's cognitive behavior over time, however, has not been considered and modeled systematically. Furthermore, the…
Descriptors: Foreign Countries, Medical Students, Surgery, Human Body
D'Mello, Sidney K.; Lehman, Blair; Person, Natalie – International Journal of Artificial Intelligence in Education, 2010
We explored the affective states that students experienced during effortful problem solving activities. We conducted a study where 41 students solved difficult analytical reasoning problems from the Law School Admission Test. Students viewed videos of their faces and screen captures and judged their emotions from a set of 14 states (basic…
Descriptors: Video Technology, Electronic Learning, Handheld Devices, Student Attitudes
Weerasinghe, Amali; Mitrovic, Antonija; Martin, Brent – International Journal of Artificial Intelligence in Education, 2009
One of the critical factors contributing to the effectiveness of human tutoring is the conversational aspect of the instruction. Our goal is to develop a general model for supporting dialogues with menu-based input that could be used in both well- and ill-defined instructional tasks. We have previously studied how human tutors provide additional…
Descriptors: Intelligent Tutoring Systems, Dialogs (Language), Databases, Design
Le, Nguyen-Thinh; Menzel, Wolfgang – International Journal of Artificial Intelligence in Education, 2009
In this paper, we introduce logic programming as a domain that exhibits some characteristics of being ill-defined. In order to diagnose student errors in such a domain, we need a means to hypothesise the student's intention, that is the strategy underlying her solution. This is achieved by weighting constraints, so that hypotheses about solution…
Descriptors: Intelligent Tutoring Systems, Logical Thinking, Programming, Models
Pinkwart, Niels; Ashley, Kevin; Lynch, Collin; Aleven, Vincent – International Journal of Artificial Intelligence in Education, 2009
Argumentation is a process that occurs often in ill-defined domains and that helps deal with the ill-definedness. Typically a notion of "correctness" for an argument in an ill-defined domain is impossible to define or verify formally because the underlying concepts are open-textured and the quality of the argument may be subject to discussion or…
Descriptors: Persuasive Discourse, Law Students, Intelligent Tutoring Systems, Problem Solving
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
Aleven, Vincent; McLaren, Bruce M.; Sewall, Jonathan; Koedinger, Kenneth R. – International Journal of Artificial Intelligence in Education, 2009
The Cognitive Tutor Authoring Tools (CTAT) support creation of a novel type of tutors called example-tracing tutors. Unlike other types of ITSs (e.g., model-tracing tutors, constraint-based tutors), example-tracing tutors evaluate student behavior by flexibly comparing it against generalized examples of problem-solving behavior. Example-tracing…
Descriptors: Feedback (Response), Student Behavior, Intelligent Tutoring Systems, Problem Solving

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