Stefanutti (2019) recently developed procedures and a related theory for deriving learning spaces from problem spaces. The approach provides a deterministic model for partially ordering individuals, on the basis of their performances in problem-solving tasks. This deterministic model accounts for both the accuracy of the responses and, especially, the sequence of ”moves” (observable solution process) made by the problem solver. A Markov model of the solution process of a problem-solving task is proposed, that provides a stochastic framework for the empirical test of the deterministic model and the related problem-space-derived learning space. This type of model allows for making predictions with respect to both the observable solution process, and the unobservable knowledge state on which the solution process is assumed to be based. The Tower of London test has been chosen as the problem-solving task for the empirical validation of the model. The results of a simulation study and of two different empirical studies are presented and discussed.

Stefanutti (2019) 最近开发了从问题空间推导出学习空间的程序和相关理论。该方法提供了一个确定性模型，用于根据个人在解决问题任务中的表现对个人进行部分排序。这种确定性模型既考虑了响​​应的准确性，又考虑了问题解决者所做的“移动”（可观察的解决过程）的顺序。提出了一个问题解决任务的求解过程的马尔科夫模型，为确定性模型和相关问题空间衍生学习空间的实证检验提供了一个随机框架。这种类型的模型允许对可观察的求解过程和假定求解过程所基于的不可观察的知识状态进行预测。伦敦塔测试已被选为模型实证验证的问题解决任务。介绍和讨论了模拟研究和两个不同的实证研究的结果。