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Dorça, Fabiano Azevedo; Lima, Luciano Vieira; Fernandes, Márcia Aparecida; Lopes, Carlos Roberto – Informatics in Education, 2012
Considering learning and how to improve students' performances, an adaptive educational system must know how an individual learns best. In this context, this work presents an innovative approach for student modeling through probabilistic learning styles combination. Experiments have shown that our approach is able to automatically detect and…
Descriptors: Cognitive Style, Models, Automation, Probability
Wu, Chung-Hsien; Su, Hung-Yu; Liu, Chao-Hong – Computer Assisted Language Learning, 2013
This study presents an efficient approach to personalized mispronunciation detection of Taiwanese-accented English. The main goal of this study was to detect frequently occurring mispronunciation patterns of Taiwanese-accented English instead of scoring English pronunciations directly. The proposed approach quickly identifies personalized…
Descriptors: Pronunciation, Pronunciation Instruction, English (Second Language), Second Language Instruction
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
Moore, David Richard – Journal of Educational Multimedia and Hypermedia, 2007
Instructional strategies for teaching concepts have long been identified. Less commonly studied is a learner's level of confidence and certitude in their knowledge based upon exposure to these instructional treatments. This experimental research study used an instrument referred to as the Spatial Probability Measure (SPM) to solicit levels of…
Descriptors: Probability, Educational Strategies, Computer Assisted Instruction, Concept Formation
Lehrer, Richard; Kim, Min-joung; Schauble, Leona – International Journal of Computers for Mathematical Learning, 2007
New capabilities in "TinkerPlots 2.0" supported the conceptual development of fifth- and sixth-grade students as they pursued several weeks of instruction that emphasized data modeling. The instruction highlighted links between data analysis, chance, and modeling in the context of describing and explaining the distributions of measures that result…
Descriptors: Computer Assisted Instruction, Concept Formation, Statistical Analysis, Statistics
Peer reviewedHancock, Thomas E.; And Others – Machine-Mediated Learning, 1995
In machine-mediated learning environments, there is a need for more reliable methods of calculating the probability that a learner's response will be correct in future trials. A combination of domain-independent response-state measures of cognition along with two instructional variables for maximum predictive ability are demonstrated. (Author/LRW)
Descriptors: Academic Achievement, Cognitive Style, Computer Assisted Instruction, Educational Environment
Bar-On, Ehud; Or-Bach, Rachel – 1985
The development of an instructional model for teaching formal mathematical concepts (probability concepts) to disadvantaged high school students through computer programming and some results from a field test are described in this document. The instructional model takes into account both learner characteristics (cognitive, affective, and…
Descriptors: Abstract Reasoning, Adolescents, Cognitive Style, Computation
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

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