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Poitras, Eric; Butcher, Kirsten R.; Orr, Matthew; Hudson, Michelle A.; Larson, Madlyn – Interactive Learning Environments, 2022
This study mined student interactions with visual representations as a means to automate assessment of learning in a complex, inquiry-based learning environment. Log trace data of 143 middle school students' interactions with an interactive map in Research Quest (an inquiry-based, online learning environment) were analyzed. Students used the…
Descriptors: Middle School Students, Electronic Learning, Maps, Science Instruction
Lyniesha Chanell Wright – ProQuest LLC, 2020
Effectively mastering organic chemistry means having the ability to recognize structural patterns, identify properties or behaviors as a result of patterns, manipulate and transform representations, and predict future outcomes. Often students rely on rote memorization of seemingly disconnected information instead of developing a sound…
Descriptors: Organic Chemistry, Science Instruction, Visualization, Models
Pavlik, Philip I., Jr.; Cen, Hao; Koedinger, Kenneth R. – Online Submission, 2009
Knowledge tracing (KT)[1] has been used in various forms for adaptive computerized instruction for more than 40 years. However, despite its long history of application, it is difficult to use in domain model search procedures, has not been used to capture learning where multiple skills are needed to perform a single action, and has not been used…
Descriptors: Performance Factors, Factor Analysis, Computer Software, Computer Assisted Instruction
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Barnes, Tiffany; Stamper, John – Educational Technology & Society, 2010
In building intelligent tutoring systems, it is critical to be able to understand and diagnose student responses in interactive problem solving. However, building this understanding into a computer-based intelligent tutor is a time-intensive process usually conducted by subject experts. Much of this time is spent in building production rules that…
Descriptors: Intelligent Tutoring Systems, Logical Thinking, Tutors, Probability
Stamper, John Carroll – ProQuest LLC, 2010
Intelligent Tutoring Systems (ITSs) that adapt to an individual student's needs have shown significant improvement in achievement over non-adaptive instruction (Murray 1999). This improvement occurs due to the individualized instruction and feedback that an ITS provides. In order to achieve the benefits that ITSs provide, we must find a way to…
Descriptors: Intelligent Tutoring Systems, Individualized Instruction, Adjustment (to Environment), Feedback (Response)
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Brand, James – CALICO Journal, 1987
Describes the language learning program "Acquire," which is a sample of grammar induction. It is a learning algorithm based on a pattern-matching scheme, using both a positive and negative network to reduce overgeneration. Language learning programs may be useful as tutorials for learning the syntax of a foreign language. (Author/LMO)
Descriptors: Artificial Intelligence, Computational Linguistics, Computer Assisted Instruction, Computer Software
Danforth, Douglas G.; And Others – 1974
This report describes the construction and testing of two "psychological" learning models for the purpose of computer recognition of human speech over the telephone. One of the two models was found to be superior in all tests. A regression analysis yielded a 92.3% recognition rate for 14 subjects ranging in age from 6 to 13 years. Tests…
Descriptors: Artificial Intelligence, Autoinstructional Aids, Computer Assisted Instruction, Dial Access Information Systems
International Association for Development of the Information Society, 2012
The IADIS CELDA 2012 Conference intention was to address the main issues concerned with evolving learning processes and supporting pedagogies and applications in the digital age. There had been advances in both cognitive psychology and computing that have affected the educational arena. The convergence of these two disciplines is increasing at a…
Descriptors: Academic Achievement, Academic Persistence, Academic Support Services, Access to Computers