Gabrielle Oslington

Dr. Gabrielle Oslington

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Research Focus & Metrics

Academic Career Length

10Years (2017-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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20 February 2020 in ZDM – Mathematics Education
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Journal Article
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JMJoanne Therese Mulligan
Joanne Therese Mulligan
GOGabrielle Oslington
Gabrielle Oslington
LELyn D. English
Lyn D. English

16 May 2020 in Educational Studies in Mathematics
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Journal Article
Closed access
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GOGabrielle Oslington
Gabrielle Oslington
JMJoanne Mulligan
Joanne Mulligan
PBPenny Van Bergen
Penny Van Bergen

13 December 2017
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Book Chapter
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GOGabrielle Oslington
Gabrielle Oslington
JMJoanne T. Mulligan
Joanne T. Mulligan
PBPenny Van Bergen
Penny Van Bergen

26 May 2023 in Mathematics Education Research Journal
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Journal Article
Open access
PDF file
GOGabrielle Oslington
Gabrielle Oslington
JMJoanne Therese Mulligan
Joanne Therese Mulligan
PBPenny Van Bergen
Penny Van Bergen
In this exploratory descriptive study, changes in one cohort’s responses to an authentic statistical investigation at the commencement of years 3 and 4 were analysed. Forty-four students made predictions by interpreting a data table of historical monthly temperatures, represented these data and explained their reasoning. An Awareness of Mathematical Pattern and Structure (AMPS) framework was extended to analyse students’ responses at five increasing levels of predictive reasoning. More developed predictive reasoning was observed in year 4 than for year 3, as well as large individual differences in both years. Most year 4 students (87%) made predictions within the historical range, relative to half the same cohort in year 3 (54%). More year 4 students (79%) made predictions based on extraction, clustering and aggregation of these data than those in year 3 (51%). Year 4 students noticed patterns such as seasonal trends and variability in these data and observed measures of central tendency. By year 4, 57% of students’ representations demonstrated transnumeration using extracted data from the table, including pictorial, column and line graphs. However, most students’ representations and explanations of these data lagged behind their predictions at both year levels.