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Lynch, Collin; Ashley, Kevin D.; Pinkwart, Niels; Aleven, Vincent – International Journal of Artificial Intelligence in Education, 2009
In this paper we consider prior definitions of the terms "ill-defined domain" and "ill-defined problem". We then present alternate definitions that better support research at the intersection of Artificial Intelligence and Education. In our view both problems and domains are ill-defined when essential concepts, relations, or criteria are un- or…
Descriptors: Definitions, Artificial Intelligence, Problem Solving, Educational Research
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…
Descriptors: Instructional Design, Intelligent Tutoring Systems, College Students, Predictor Variables
Peer reviewedWu, Albert K. W.; Lee, M. C. – Computers in Human Behavior, 1998
Proposes the notion of intelligent tutoring systems (ITS) as design in order to engage ITS development with more rigor. Topics include engineering design versus ITS design; systems approach; design as problem solving; a hierarchy of paradigms; the emergence of an agent-theoretic approach; and the need for an ITS design notation. (Author/LRW)
Descriptors: Design, Intelligent Tutoring Systems, Problem Solving, Systems Approach
Peer reviewedStoyanov, Svetoslav; Kommers, Piet – Journal of Interactive Learning Research, 1999
Presents an experimental verification of a hypothetical construct explaining the basic mechanism behind the behavior of an intelligent agent implemented in the Solution, Mapping, Intelligent, Learning Environment (SMILE) performance supported system. Explains the SMILE concept mapping method and its role as a problem-solving tool. (Author/LRW)
Descriptors: Concept Mapping, Information Systems, Intelligent Tutoring Systems, Problem Solving
Krusberg, Zosia A. C. – Journal of Science Education and Technology, 2007
Three emerging technologies in physics education are evaluated from the interdisciplinary perspective of cognitive science and physics education research. The technologies--Physlet Physics, the Andes Intelligent Tutoring System (ITS), and Microcomputer-Based Laboratory (MBL) Tools--are assessed particularly in terms of their potential at promoting…
Descriptors: Intelligent Tutoring Systems, Physics, Science Laboratories, Educational Technology
Boyer, Kristy Elizabeth, Ed.; Yudelson, Michael, Ed. – International Educational Data Mining Society, 2018
The 11th International Conference on Educational Data Mining (EDM 2018) is held under the auspices of the International Educational Data Mining Society at the Templeton Landing in Buffalo, New York. This year's EDM conference was highly competitive, with 145 long and short paper submissions. Of these, 23 were accepted as full papers and 37…
Descriptors: Data Collection, Data Analysis, Computer Science Education, Program Proposals
Andersson, David; Reimers, Karl – Journal of Educational Technology, 2010
The field of education is experiencing a rapid shift as internet-enabled distance learning becomes more widespread. Often, traditional classroom teaching pedagogical techniques can be ill-suited to the online environment. While a traditional entry-level class might see a student attrition rate of 5-10%, the same teaching pedagogy in an online…
Descriptors: Computer Software, Computer Oriented Programs, Online Courses, Electronic Learning
Koedinger, Kenneth R.; Aleven, Vincent – Educational Psychology Review, 2007
Intelligent tutoring systems are highly interactive learning environments that have been shown to improve upon typical classroom instruction. Cognitive Tutors are a type of intelligent tutor based on cognitive psychology theory of problem solving and learning. Cognitive Tutors provide a rich problem-solving environment with tutorial guidance in…
Descriptors: Intelligent Tutoring Systems, Metacognition, Tutors, Cognitive Psychology
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
Kazi, Hameedullah; Haddawy, Peter; Suebnukarn, Siriwan – International Journal of Artificial Intelligence in Education, 2009
In well-defined domains such as Physics, Mathematics, and Chemistry, solutions to a posed problem can objectively be classified as correct or incorrect. In ill-defined domains such as medicine, the classification of solutions to a patient problem as correct or incorrect is much more complex. Typical tutoring systems accept only a small set of…
Descriptors: Foreign Countries, Problem Based Learning, Problem Solving, Correlation
Cobo, Pedro; Fortuny, Josep M.; Puertas, Eloi; Richard, Philippe R. – International Journal of Computers for Mathematical Learning, 2007
This paper aims, first, to describe the fundamental characteristics and workings of the AgentGeom artificial tutorial system, which is designed to help students develop knowledge and skills related to problem solving, mathematical proof in geometry, and the use of mathematical language. Following this, we indicate the manner in which a secondary…
Descriptors: Geometric Concepts, Mathematical Logic, Mathematics Instruction, Problem Solving
Cetintas, Suleyman; Si, Luo; Xin, Yan Ping; Hord, Casey – International Working Group on Educational Data Mining, 2009
This paper proposes a learning based method that can automatically determine how likely a student is to give a correct answer to a problem in an intelligent tutoring system. Only log files that record students' actions with the system are used to train the model, therefore the modeling process doesn't require expert knowledge for identifying…
Descriptors: Programming, Evidence, Intelligent Tutoring Systems, Regression (Statistics)
Hu, Xiangen, Ed.; Barnes, Tiffany, Ed.; Hershkovitz, Arnon, Ed.; Paquette, Luc, Ed. – International Educational Data Mining Society, 2017
The 10th International Conference on Educational Data Mining (EDM 2017) is held under the auspices of the International Educational Data Mining Society at the Optics Velley Kingdom Plaza Hotel, Wuhan, Hubei Province, in China. This years conference features two invited talks by: Dr. Jie Tang, Associate Professor with the Department of Computer…
Descriptors: Data Analysis, Data Collection, Graphs, Data Use
Arendasy, Martin; Sommer, Markus – Learning and Individual Differences, 2007
This article deals with the investigation of the psychometric quality and constructs validity of algebra word problems generated by means of a schema-based version of the automatic min-max approach. Based on review of the research literature in algebra word problem solving and automatic item generation this new approach is introduced as a…
Descriptors: Schemata (Cognition), Test Items, Intelligent Tutoring Systems, Construct Validity
Redondo, Miguel A.; Bravo, Crescencio; Ortega, Manuel; Verdejo, M. Felisa – Computers and Education, 2007
Experimental learning environments based on simulation usually require monitoring and adaptation to the actions the users carry out. Some systems provide this functionality, but they do so in a way which is static or cannot be applied to problem solving tasks. In response to this problem, we propose a method based on the use of intermediate…
Descriptors: Intelligent Tutoring Systems, Cooperative Learning, Experiential Learning, Educational Environment

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