A Framework for Characterizing Middle School Students' Statistical Thinking

Mooney, Edward S.

doi:10.1207/s15327833mtl0401_2

Cited by 75 publications

(139 citation statements)

References 23 publications

Supporting

Mentioning

109

Contrasting

Unclassified

Order By: Relevance

“…This result is parallel to the findings of the study by Mooney (2002) on secondary school students. Mooney reported that no student was found on the fourth level in the study.…”

Section: Discussionsupporting

confidence: 87%

“…However, while a student on this level is working on a problem, he does not gather the related mathematical ideas. Reasoning of students on this level may be explained by an example used by Mooney (2002). In this example, numbers of medals (gold, silver, bronze) won by five countries in the 1998 Winter Olympics were shown in bar graphs and tables.…”

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

confidence: 99%

See 1 more Smart Citation

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

View full text Add to dashboard Cite

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 ...

show abstract

“…This result is parallel to the findings of the study by Mooney (2002) on secondary school students. Mooney reported that no student was found on the fourth level in the study.…”

Section: Discussionsupporting

confidence: 87%

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

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…The profile showed a strong internal consistency in students' statistical thinking across the four processes. Mooney (2002) found that 5/12 of the students achieved the same level of thinking for any three out of the four statistical processes. Based on this model, it can be concluded that the four statistical processes closely linked although examined separately.…”

Section: A Statisticsmentioning

confidence: 88%

Assessing Algebraic Solving Ability: A Theoretical Framework

Lian¹,

Yew²

2012

IES

View full text Add to dashboard Cite

Algebraic solving ability had been discussed by many educators and researchers. There exists no definite definition for algebraic solving ability as it can be viewed from different perspectives. In this paper, the nature of algebraic solving ability in terms of algebraic processes that demonstrate the ability in solving algebraic problem is discussed. A theoretical framework of algebraic solving ability was formulated based on three phases of algebraic processes, historical development of algebra and SOLO model (Structured of the Observed Learning Outcome). The three phases of algebraic processes included investigating the pattern by collecting the numerical data, representing and generalizing the pattern into a table and an equation, and interpreting and applying the equation to the related or new situation. There are four levels (unistructural, multistructural, relational and extended abstract) of structure response of SOLO model that had been applied to assess students' algebraic solving ability incorporate two content domains of algebraic equation, namely direct variation and inverse variation.

show abstract

“…The model has been used effectively by other researchers to help identify various levels of sophistication in statistical thinking. The statistical thinking framework for elementary school students of Jones et al (2000) and the Middle School Students Statistical Thinking (M3ST) framework (Mooney, 2002) are both based upon the SOLO Taxonomy. In addition, Watson, Moritz, and colleagues have conducted several studies in which the cognitive theory of Biggs and Collis was used to describe the relative sophistication of students' responses to statistical thinking tasks (e.g., Watson, Collis, Callingham, & Moritz, 1995;Watson & Moritz, 1999a, 1999b, 2000a, 2000b.…”

Section: Theoretical Perspectivementioning

confidence: 99%

High school students’ levels of thinking in regard to statistical study design

Groth

2003

Math Ed Res J

View full text Add to dashboard Cite

The study describes levels of thinking in regard to the design of statistical studies. Clinical interviews were conducted with 15 students who were enrolled in high school or were recent high school graduates, and who represented a range of mathematical backgrounds. During the clinical interview sessions students were asked how they would go about designing studies to answer several different quantifiable questions. Several levels of sophistication were identified in their responses, and are discussed in terms of the Biggs and Collis (1982, 1991) cognitive model.

show abstract

A Framework for Characterizing Middle School Students' Statistical Thinking

Cited by 75 publications

References 23 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

Assessing Algebraic Solving Ability: A Theoretical Framework

High school students’ levels of thinking in regard to statistical study design

Contact Info

Product

Resources

About