127
Views
1
CrossRef citations to date
0
Altmetric
Articles

Understanding 15-year-old students’ conceptions of randomness through their ‘potential worlds’: a qualitative analysis

ORCID Icon &
Pages 237-258 | Received 02 Jun 2019, Published online: 20 Oct 2019
 

Abstract

In this paper, we investigate the ways in which 15 year-old students conceive interrelated issues of randomness. We deal with these issues of randomness as a whole and not separately from each other, in contrast to the research so far. In order to analyse the students’ ways we introduce a modification of Kyburg’s Schema [(1974). The logical foundations of statistical inference. Boston: Reidel] for subjective interpretation of randomness, together with the notion of a student’s potential world as a new theoretical framework. This last notion comprises all views, beliefs and ideas a student has at a specific time about the notion of randomness, which have been formed from his/her individual interpretation of experiences. Our analysis of responses from students’ interviews reveals that few students conceive the notions of randomness in a uniform way, while the rest of them conceive these notions in different ways. It turns out that this variety of students’ conceptions depends heavily on their own potential worlds, which are grounded on social, personal or institutional sources.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 This question is similar to Four-heads problem in Konold et al. (Citation1993).

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 372.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.