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Klingler, Severin; Käser, Tanja; Solenthaler, Barbara; Gross, Markus – International Educational Data Mining Society, 2015
Modeling student knowledge is a fundamental task of an intelligent tutoring system. A popular approach for modeling the acquisition of knowledge is Bayesian Knowledge Tracing (BKT). Various extensions to the original BKT model have been proposed, among them two novel models that unify BKT and Item Response Theory (IRT). Latent Factor Knowledge…
Descriptors: Intelligent Tutoring Systems, Knowledge Level, Item Response Theory, Prediction
Van Inwegen, Eric G.; Adjei, Seth A.; Wang, Yan; Heffernan, Neil T. – International Educational Data Mining Society, 2015
User modelling algorithms such as Performance Factors Analysis and Knowledge Tracing seek to determine a student's knowledge state by analyzing (among other features) right and wrong answers. Anyone who has ever graded an assignment by hand knows that some answers are "more wrong" than others; i.e. they display less of an understanding…
Descriptors: Knowledge Level, Performance Factors, Error Patterns, Mathematics
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Gong, Yue; Beck, Joseph E.; Heffernan, Neil T. – International Journal of Artificial Intelligence in Education, 2011
Student modeling is a fundamental concept applicable to a variety of intelligent tutoring systems (ITS). However, there is not a lot of practical guidance on how to construct and train such models. This paper compares two approaches for student modeling, Knowledge Tracing (KT) and Performance Factors Analysis (PFA), by evaluating their predictive…
Descriptors: Intelligent Tutoring Systems, Factor Analysis, Performance Factors, Models
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Schutte, Anne R.; Spencer, John P. – Child Development, 2002
Tested predictions of dynamic field theory in study of 3-year-olds' location memory errors in task with homogeneous task space. Found that young children's spatial memory responses are affected by delay- and experience-dependent processes as well as the geometric structure of the task space. Both dynamic field theory and category adjustment models…
Descriptors: Bias, Cognitive Development, Error Patterns, Memory
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Simon, Dorothea P. – Instructional Science, 1976
A typical school spelling task is analyzed in terms of an information processing model of spelling performance. (Author)
Descriptors: Computer Programs, Error Patterns, Information Processing, Mnemonics
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Ohlsson, Stellan – Psychological Review, 1996
A theory of how people detect and correct their own performance errors during skill practice is proposed. Blame assignment, error attribution, and knowledge revision are identified as three cognitive functions in explaining error correction. The theory is embodied in a computer model that learns cognitive skills in ecologically valid domains. (SLD)
Descriptors: Computer Software, Error Correction, Error Patterns, Feedback
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Field, Andy P. – Psychological Methods, 2005
One conceptualization of meta-analysis is that studies within the meta-analysis are sampled from populations with mean effect sizes that vary (random-effects models). The consequences of not applying such models and the comparison of different methods have been hotly debated. A Monte Carlo study compared the efficacy of Hedges and Vevea's…
Descriptors: Meta Analysis, Correlation, Effect Size, Models
Norman, Donald A. – 1982
Different aspects of human-machine interaction are discussed in the five brief papers that comprise this report. The first paper, "Some Observations on Mental Models," discusses the role of a person's mental model in the interaction with systems. The second paper, "A Psychologist Views Human Processing: Human Errors and Other…
Descriptors: Artificial Intelligence, Cognitive Processes, Computer Programs, Design Requirements