On the assessment of procedural knowledge: From problem spaces to knowledge spaces
By generalizing and completing the work initiated by Stefanutti and Albert (2003, Journal of Universal Computer Science, 9, 1455), this article provides the mathematical foundations of a theoretical approach whose primary goal is to construct a bridge between problem solving, as initially conceived by Newell and Simon (1972, Human problem solving. Englewood Cliffs, NJ: Prentice‐Hall.), and knowledge assessment (Doignon and Falmagne, 1985, International Journal of Man‐Machine Studies, 23, 175; Doignon and Falmagne, 1999, Knowledge spaces. Berlin, Germany: Springer‐Verlag.; Falmagne et al., 2013, Knowledge spaces: Applications in education. New York, NY: Springer‐Verlag; Falmagne and Doignon, 2011, Learning spaces: Interdisciplinary applied mathematics. Berlin, Germany: Springer‐Verlag.). It is shown that the collection of all possible knowledge states for a given problem space is a learning space. An algorithm for deriving a learning space from a problem space is illustrated. As an example, the algorithm is used to derive the learning space of a neuropsychological test whose problem space is well known: the Tower of London (TOL; Shallice, 1982, Philosophical Transactions of the Royal Society of London B: Biological Sciences, 298, 199). The derived learning space could then be used for adaptively assessing individual planning skills with the TOL.
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