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Anglim, Jeromy; Wynton, Sarah K. A. – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2015
The current study used Bayesian hierarchical methods to challenge and extend previous work on subtask learning consistency. A general model of individual-level subtask learning was proposed focusing on power and exponential functions with constraints to test for inconsistency. To study subtask learning, we developed a novel computer-based booking…
Descriptors: Bayesian Statistics, Hierarchical Linear Modeling, Learning, Statistical Analysis
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Wynton, Sarah K. A.; Anglim, Jeromy – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2017
While researchers have often sought to understand the learning curve in terms of multiple component processes, few studies have measured and mathematically modeled these processes on a complex task. In particular, there remains a need to reconcile how abrupt changes in strategy use can co-occur with gradual changes in task completion time. Thus,…
Descriptors: Learning Strategies, Learning Processes, Bayesian Statistics, Computer Assisted Instruction