With respect to the design of instruction, cognitive models of development
provide a coherent picture of the diverse range of statistical reasoning that a teacher
might expect students to bring to the classroom. The use of cognitive models in
designing instruction can be amplified by examining Simon’s (1995) notion of
hypothetical learning trajectory. By hypothetical learning trajectory, Simon means
the formulation of learning goals, learning activities, and a conjectured learning
process. In the first instance, many of the cognitive models discussed in this chapter
identify key processes and concept goals, by their very nature indicating where
children might be in relation to these goals. For example, the Jones et al. (2000)
model identifies key processes like describing data, organizing data, representing
data, and analyzing and interpreting data; it also documents, through the level
descriptors, the kind of goals that might be appropriate for individual children or the
class as a whole. In considering learning activities, the research on cognitive models
invariably incorporates tasks and activities that have been used to engage students’
statistical reasoning. For example, tasks like those incorporated in the technology
simulation on sampling distributions (Chapter 13) have widespread potential in
college and high school instructional settings. Finally, in relation to conjecturing the
possible direction of the classroom learning process, the cognitive model provides a
database for the teacher on the range of statistical reasoning that he or she might
expect to find during instruction. For example, in Grade 3 instruction dealing with
sampling, the Watson and Moritz (2000b) model suggests that all children would be
small samplers with more than 50% of them using idiosyncratic methods of selecting
samples.
With respect to the design of instruction, cognitive models of development
provide a coherent picture of the diverse range of statistical reasoning that a teacher
might expect students to bring to the classroom. The use of cognitive models in
designing instruction can be amplified by examining Simon’s (1995) notion of
hypothetical learning trajectory. By hypothetical learning trajectory, Simon means
the formulation of learning goals, learning activities, and a conjectured learning
process. In the first instance, many of the cognitive models discussed in this chapter
identify key processes and concept goals, by their very nature indicating where
children might be in relation to these goals. For example, the Jones et al. (2000)
model identifies key processes like describing data, organizing data, representing
data, and analyzing and interpreting data; it also documents, through the level
descriptors, the kind of goals that might be appropriate for individual children or the
class as a whole. In considering learning activities, the research on cognitive models
invariably incorporates tasks and activities that have been used to engage students’
statistical reasoning. For example, tasks like those incorporated in the technology
simulation on sampling distributions (Chapter 13) have widespread potential in
college and high school instructional settings. Finally, in relation to conjecturing the
possible direction of the classroom learning process, the cognitive model provides a
database for the teacher on the range of statistical reasoning that he or she might
expect to find during instruction. For example, in Grade 3 instruction dealing with
sampling, the Watson and Moritz (2000b) model suggests that all children would be
small samplers with more than 50% of them using idiosyncratic methods of selecting
samples.
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