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Showing 46 to 60 of 67 results
Muldner, Kasia; Conati, Cristina – International Journal of Artificial Intelligence in Education, 2010
Although worked-out examples play a key role in cognitive skill acquisition, research demonstrates that students have various levels of meta-cognitive abilities for using examples effectively. The Example Analogy (EA)-Coach is an Intelligent Tutoring System that provides adaptive support to foster meta-cognitive behaviors relevant to a specific…
Descriptors: Intelligent Tutoring Systems, Problem Solving, Cognitive Psychology, Thinking Skills
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
Johnson, W. Lewis – International Journal of Artificial Intelligence in Education, 2010
The Tactical Language and Culture Training System (TLCTS) helps learners acquire basic communicative skills in foreign languages and cultures. Learners acquire communication skills through a combination of interactive lessons and serious games. Artificial intelligence plays multiple roles in this learning environment: to process the learner's…
Descriptors: Second Language Learning, Educational Games, Intelligent Tutoring Systems, Artificial Intelligence
Hausmann, Robert G. M.; VanLehn, Kurt – International Journal of Artificial Intelligence in Education, 2010
Self-explaining is a domain-independent learning strategy that generally leads to a robust understanding of the domain material. However, there are two potential explanations for its effectiveness. First, self-explanation generates additional "content" that does not exist in the instructional materials. Second, when compared to comprehension,…
Descriptors: Instructional Design, Intelligent Tutoring Systems, College Students, Predictor Variables
Baschera, Gian-Marco; Gross, Markus – International Journal of Artificial Intelligence in Education, 2010
We present an inference algorithm for perturbation models based on Poisson regression. The algorithm is designed to handle unclassified input with multiple errors described by independent mal-rules. This knowledge representation provides an intelligent tutoring system with local and global information about a student, such as error classification…
Descriptors: Foreign Countries, Spelling, Intelligent Tutoring Systems, Prediction
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
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
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
Easterday, Matthew W.; Aleven, Vincent; Scheines, Richard; Carver, Sharon M. – International Journal of Artificial Intelligence in Education, 2009
Policy problems like "What should we do about global warming?" are ill-defined in large part because we do not agree on a system to represent them the way we agree Algebra problems should be represented by equations. As a first step toward building a policy deliberation tutor, we investigated: (a) whether causal diagrams help students learn to…
Descriptors: Causal Models, Protocol Analysis, Tutors, Inferences
Zhang, Li; Gillies, Marco; Dhaliwal, Kulwant; Gower, Amanda; Robertson, Dale; Crabtree, Barry – International Journal of Artificial Intelligence in Education, 2009
This paper describes a multi-user role-playing environment, referred to as "e-drama", which enables groups of people to converse online, in scenario driven virtual environments. The starting point of this research, is an existing application known as "edrama", a 2D graphical environment in which users are represented by static cartoon figures.…
Descriptors: Disabilities, Cartoons, Interpersonal Relationship, Interaction
Haake, Magnus; Gulz, Agneta – International Journal of Artificial Intelligence in Education, 2009
The paper presents a theoretical framework addressing three aspects of embodied pedagogical agents: visual static appearance, pedagogical role, and communicative style. The framework is then applied to a user study where 90 school children (aged 12-15) in a dummy multimedia program were presented with either an instructor or a learning companion…
Descriptors: Foreign Countries, Computer Assisted Instruction, Multimedia Materials, Computer Graphics
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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