Abstract
A “Generative-Predicational Model” is proposed and applied to the generation of meanings of simple mathematical word-problems. The model suggests that a fundamental property of cognition is a generative process that takes arguments and that produces results, such as events, answers and inferences. This fundamental property, called predication, generates a task-environment i.e., a problem and its corresponding problem-space i.e., its solution. More precisely, a task-environment is a predication consisting of a written mathematical problem and a writer's life experience. A problem-space is a predication consisting of a leamer's problem solving schema and of the meaning that the learmer generates for the text.
The case with which relations can be established between a task-environment and a problem-space depends on the problem's “coherence” and “complexity” and the leamer's experiences and thought processes. Faceted definitions of task-environment and problem-space are used to analyze talk-aloud protocols of fifty Israeli sixth-graders tested with thirty word-problems. The empirical results support the proposed model.
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Peled, Z., Wittrock, M.C. Generated meanings in the comprehension of word problems in mathematics. Instr Sci 19, 171–205 (1990). https://doi.org/10.1007/BF00120195
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DOI: https://doi.org/10.1007/BF00120195