Instructional Science

, Volume 33, Issue 4, pp 341-366

First online:

Assessing schematic knowledge of introductory probability theory

  • Damian P. BirneyAffiliated withSchool of Psychology, University of Sydney Email author 
  • , Gerard J. FogartyAffiliated withUniversity of Southern Queensland
  • , Ashley PlankAffiliated withUniversity of Southern Queensland

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The ability to identify schematic knowledge is an important goal for both assessment and instruction. In the current paper, schematic knowledge of statistical probability theory is explored from the declarative-procedural framework using multiple methods of assessment. A sample of 90 undergraduate introductory statistics students was required to classify 10 pairs of probability problems as similar or different; to identify whether 15 problems contained sufficient, irrelevant, or missing information (text-edit); and to solve 10 additional problems. The complexity of the schema on which the problems were based was also manipulated. Detailed analyses compared text-editing and solution accuracy as a function of text-editing category and schema complexity. Results showed that text-editing tends to be easier than solution and differentially sensitive to schema complexity. While text-editing and classification were correlated with solution, only text-editing problems with missing information uniquely predicted success. In light of previous research these results suggest that text-editing is suitable for supplementing the assessment of schematic knowledge in development.


assessing schematic knowledge statistical probability theory text-editing