https://iase-pub.org/ojs/SERJ/issue/feed 2025-01-30T23:15:25+00:00 Susan Peters s.peters@louisville.edu Open Journal Systems <p><em>SERJ</em> is a peer-reviewed electronic journal of the International Association for Statistical Education (IASE) and the International Statistical Institute (ISI). <em>SERJ</em> is published three times year and is open access and publication cost free.</p> https://iase-pub.org/ojs/SERJ/article/view/587 2024-01-30T08:04:56+00:00 ANELISE SABBAG asabbag@calpoly.edu ANDREW ZIEFFLER zief0002@umn.edu CASEY NG cng27@calpoly.edu

<p class="AbstractBody" style="text-indent: 0cm;"><span lang="EN-US">One of the most important goals in a statistics class is to develop students who are statistically literate and can reason with statistical concepts. The REALI instrument was designed to concurrently assess statistical literacy and reasoning in introductory statistics students. This paper reports a measurement analysis of the statistical literacy and reasoning subscores from the REALI assessment and the extent to which they are reliable and distinct. Investigation of these subscores is used clarify the relationship between the constructs of statistical literacy and statistical reasoning and to what extent they overlap. The results of this analysis, under a Multidimensional Item Response Theory framework, show that the statistical literacy and reasoning subscores provide no added value over a single general statistical knowledge score. This indicates the two constructs might be indistinguishable from one another.</span></p>

2025-01-30T00:00:00+00:00 Copyright (c) 2025 STATISTICS EDUCATION RESEARCH JOURNAL https://iase-pub.org/ojs/SERJ/article/view/714 2024-09-02T08:04:49+00:00 ANNE PATEL a.patel@auckland.ac.nz MAXINE PFANNKUCH m.pfannkuch@auckland.ac.nz

<p>Statistics education researchers have been challenged to consider the theory of inferentialism in understanding concept formation in students. A critique of inferentialism is that no comprehensive method has been formulated to use the theory in practice. In this paper an inferentialism-based framework is presented that appears to be capable of explicating the development of statistical concepts during learning. By following six 11-year-olds’ learning over several statistical modelling cycles using TinkerPlots, the framework was used to capture their interrogative cycles of noticing and wondering, giving and asking for reasons, and sanctioning and censuring, as well as oscillations between concretising language about actions and conceptualising language towards concept formation. Five teaching episodes occurring near the beginning of a 12-week learning sequence are used to illustrate how the framework might be able to capture student concept formation over time.</p>

2025-02-12T00:00:00+00:00 Copyright (c) 2025 STATISTICS EDUCATION RESEARCH JOURNAL