Models of Development in Statistical Reasoning

Jones, Graham A.; Langrall, Cynthia W.; Mooney, Edward S.; Thornton, Carol A.

doi:10.1007/1-4020-2278-6_5

Cited by 32 publications

(48 citation statements)

References 43 publications

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“…The reports and solution sheets obtained from the groups were examined together, and each group's reasoning, the statistical concepts they used, and how they used those concepts were presented in tables. Later, the data were analyzed in the scope of the theoretical framework regarding statistical reasoning developed by Jones et al (2004). Each group's reasoning was coded by both researchers using the determined codes (personal experience, looking only at flights that were not delayed, computing arithmetic mean only, computing central tendency measure, computing variation measure, having difficulty in making more than one comparisons and finding a relationship, determining the relationship between the context and the data, etc.).…”

Section: Discussionmentioning

confidence: 99%

“…Additionally, a student on this level focuses on only one side of a problem situation. In a problem used by Jones et al (2004) (Best Concert Tour problem), elementary school students were given three bar graphs including concert tour information of four music bands (Barbra Streisend, Boyz II Men, Eagles and Rolling Stones). The first of these graphs showed the total concert revenue for each band, these second showed total number of concerts, and the third showed the number of cities the concerts took place.…”

Section: The Theoretical Framework Developed By Jones Et Al (2004) Cmentioning

confidence: 99%

“…Statistical Reasoning Levels Jones et al (2004) described statistical reasoning of elementary school students in four processes: (1) describing data, (2) organizing data, (3) representing data, and (4) analyzing and interpreting data. The process of describing data includes reading the data presented as raw or in the form of a graph, table or scheme.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

“…Therefore, it is important to observe the statistical reasoning of teachers as much as it is important to observe that of students. In this study, statistical reasoning of pre-service teachers who were soon to become teachers was investigated in the theoretical framework for elementary school students developed by Jones, Langrall, Mooney and Thornton (2004). This theoretical framework was described in detail below.…”

Section: Introductionmentioning

confidence: 99%

“…The final process of analyzing and interpreting data includes determination of patterns and tendencies in the data and estimation and inference based on the data. In scope of this study, we approach the process of analyzing and interpreting data (Jones et al, 2004) which represents the essence of statistical reasoning. …”

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confidence: 99%

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Analyzing the Statistical Reasoning Levels of Pre-Service Elementary School Teachers in the Context of a Model Eliciting Activity

Ulusoy¹,

Altay²

2016

International Journal of Research in Education and Science

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The purpose of this study is to analyze the statistical reasoning levels of preservice elementary school teachers. With this purpose, pre-service teachers consisting of 29 groups worked on a model eliciting activity (MEA) in scope of an elective course they were taking. At the end of the class, they were asked to present their solutions while working on the MEA in form of a detailed report. The data of the study consisted of these reports and solution sheets. Content analysis method was used in the analysis of the data. As a result, it was found that when the participants were asked to interpret a data set in a table, they could not establish a relationship between measurements of central tendency and variation, and their reasoning was limited or mistaken. The general tendency when pre-service teachers encounter a data set is that they think the only value representing the data set is the arithmetic mean. Additionally, it was found that, although the pre-service teachers were able to correctly compute the measures of variation such as standard deviation and interquartile range, they did not have sufficient knowledge about what these measures tell us about the variation of the data set. Accepted: 27 October 2016 Keywords Statistical reasoning Model eliciting activity Pre-service teachers Central tendency Variation IntroductionEven if we are not a statistics expert, while we are examining data in our hands, some points might get our attention and inspire curiosity in us. Do the data consist of values close to each other, or are there outliers? If we wanted to represent this value with only one number, what would it be? How do we proceed to reach this value that will represent the data set and maybe lead us to reach the correct result? Such questions may run through our heads. As a matter of facts, all of us encounter large amount of data in our daily lives without knowing about it, and use statistics to make a correct decision regarding these data. In the decisions we make, while some of us use statistically correct reasoning, some of us may tend toward wrong decisions and be mistaken because of the limitations in our knowledge or experience. The importance of statistical reasoning, which is significantly effective in the decisions we make, is undeniable. So, how can we define statistical reasoning? Chervany, Collier, Fienberg, Johnson and Neter (1977), who were some of the people who defined statistical reasoning first, defined it as: a) what a student is able to do with statistical content (for instance, recalling, recognizing, distinguishing statistical concepts), and b) the skill shown by students in using statistical concepts in specific problem solving steps. On their definition, the researchers also added the property of statistical reasoning that it is not a directly observable process. The researchers, starting with this property of statistical reasoning, indicated that this skill may only be observed while working on a specific task. Statistical reasoning includes making sense of statistical information ...

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Section: Discussionmentioning

confidence: 99%

Section: The Theoretical Framework Developed By Jones Et Al (2004) Cmentioning

confidence: 99%

Section: Theoretical Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

Analyzing the Statistical Reasoning Levels of Pre-Service Elementary School Teachers in the Context of a Model Eliciting Activity

Ulusoy¹,

Altay²

2016

International Journal of Research in Education and Science

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Assessment of Learning, for Learning, and as Learning in Statistics Education

Garfield

Franklin

2011

Teaching Statistics in School Mathematics-Challenges for Teaching and Teacher Education

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A method for assessing students’ interpretations of contextualized data

Groth

Choi

2023

Educ Stud Math

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Learning to interpret data in context is an important educational outcome. To assess students’ attainment of this outcome, it is necessary to examine the interplay between their contextual and statistical reasoning. We describe a research method designed to do so. The method draws upon Toulmin’s ( 1958 , 2003 ) model of argumentation for the first stage of qualitative data analysis and the Structure of the Observed Learning Outcome (SOLO) (Biggs & Collis, 1991 ) model for the second stage. Toulmin analyses help identify the justifications and expressions of uncertainty students provide in their interpretive arguments. Subsequent analyses based on the multi-modal conceptualization of SOLO help characterize the quality of student arguments relative to one another. Existing literature and an empirical example are drawn upon to explain how the Toulmin and SOLO models can be used in tandem to analyze students’ interpretations of contextualized data. We also explain how pairing Toulmin and SOLO can address theoretical and practical limitations that arise when using just one of the two models on its own.

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Models of Development in Statistical Reasoning

Cited by 32 publications

References 43 publications

Analyzing the Statistical Reasoning Levels of Pre-Service Elementary School Teachers in the Context of a Model Eliciting Activity

Analyzing the Statistical Reasoning Levels of Pre-Service Elementary School Teachers in the Context of a Model Eliciting Activity

Assessment of Learning, for Learning, and as Learning in Statistics Education

A method for assessing students’ interpretations of contextualized data

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