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Waalkens, Maaike; Aleven, Vincent; Taatgen, Niels – Computers & Education, 2013
Intelligent tutoring systems (ITS) support students in learning a complex problem-solving skill. One feature that makes an ITS architecturally complex, and hard to build, is support for strategy freedom, that is, the ability to let students pursue multiple solution strategies within a given problem. But does greater freedom mean that students…
Descriptors: Intelligent Tutoring Systems, Problem Solving, Algebra, Mathematics Instruction
Nogry, S.; Jean-Daubias, S.; Guin, N. – Interactive Learning Environments, 2012
This article deals with evaluating an interactive learning environment (ILE) during the iterative-design process. Various aspects of the system must be assessed and a number of evaluation methods are available. In designing the ILE Ambre-add, several techniques were combined to test and refine the system. In particular, we point out the merits of…
Descriptors: Foreign Countries, Educational Technology, Elementary School Students, Mathematics Instruction
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
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
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)
Doering, Aaron; Scharber, Cassandra; Miller, Charles; Veletsianos, George – Contemporary Issues in Technology and Teacher Education (CITE Journal), 2009
GeoThentic, an online teaching and learning environment, focuses on engaging teachers and learners in solving real-world geography problems through use of geospatial technologies. The design of GeoThentic is grounded on the technology, pedagogy, and content knowledge (TPACK) framework as a metacognitive tool. This paper describes how the TPACK…
Descriptors: Geography Instruction, Metacognition, Educational Technology, Electronic Learning
Smith, Samuel – International Journal of Educational Leadership Preparation, 2008
Educational Theory into Practice Software (ETIPS) is an online case study program in the testbed stage of development intended for use by professors of education administration. The program is being developed by Sara Dexter and Pamela D. Tucker of the University of Virginia and is being tested by various other universities throughout Virginia. As…
Descriptors: Decision Making, Decision Making Skills, Case Studies, Context Effect
Morch, Anders; Nygard, Kathrine; Andersen, Renate; Mushtaq, Shazia; Nedic, Damir; Olsen, Espen; Hauge, Trond Eiliv; Vedoy, Gunn; Norenes, Svein Olav; Moen, Anne; Nes, Sturle; Olsen, Dorothy S.; Ludvigsen, Sten; Toiviainen, Hanna; Lallimo, Jiri; Toikka, Seppo; Paavola, Sami; Pohjola, Pasi; Hakkarainen, Kai – Online Submission, 2009
This deliverable has been produced in the context of the Knowledge-Practice Laboratory (KP-Lab) project. KP-Lab focuses on innovative practices of working with knowledge in higher education, teacher training, and workplaces. Participants of WP10 are University of Helsinki, University of Oslo and Poyry Forest Industry representing both researchers…
Descriptors: Foreign Countries, Higher Education, Program Evaluation, Educational Change
Gee, James Paul – E-Learning, 2005
This article asks how good video and computer game designers manage to get new players to learn long, complex and difficult games. The short answer is that designers of good games have hit on excellent methods for getting people to learn and to enjoy learning. The longer answer is more complex. Integral to this answer are the good principles of…
Descriptors: Video Games, Educational Games, Educational Principles, Computer System Design
Scarlatos, Lori L. – Interactive Technology and Smart Education, 2006
Educators recognize that group work and physical involvement with learning materials can greatly enhance the understanding and retention of difficult concepts. As a result, math manipulatives--such as pattern blocks and number lines--have increasingly been making their way into classrooms and children's museums. Yet without the constant guidance…
Descriptors: Museums, Mathematical Concepts, Mathematics Instruction, Problem Solving
Lu, Chun-Hung; Wu, Chia-Wei; Wu, Shih-Hung; Chiou, Guey-Fa; Hsu, Wen-Lian – Educational Technology & Society, 2005
This paper presents a new model for simulating procedural knowledge in the problem solving process with our ontological system, InfoMap. The method divides procedural knowledge into two parts: process control and action performer. By adopting InfoMap, we hope to help teachers construct curricula (declarative knowledge) and teaching strategies by…
Descriptors: Problem Solving, Teaching Methods, Models, Educational Games