“…Focusing on statistical cognitions (Garfield & Franklin, 2011;Lei & Yang, 2012), which connect reader's cognition to statistical concepts and meanings of data (Idris & Yang, 2017), which connect reader's experience to statistical contexts.…”
Section: Voice Of Authorsmentioning
confidence: 99%
“…That is, the reader is drawn into the text to make sense of one abstract concept through several perspectives. Making sense of a statistical concept would include understanding statistical basic knowledge, reasoning, and thinking related to the concept (i.e., statistical cognitions; Garfield & Franklin, 2011;Lei & Yang, 2012), as well as understanding data and contexts used in presenting the concept (i.e., meanings of data; Idris & Yang, 2017). The statistical cognition presented in the text would connect the reader's cognition to the statistical concept, while the meanings of data would connect the reader's experience to the statistical contexts.…”
Section: The Voice Of Statisticsmentioning
confidence: 99%
“…For instance, when the term mean is introduced, data as numerical numbers without context might be addressed by focusing on the calculating procedures, while data as numbers in problem contexts can be addressed to elaborate and connect the results of calculation into the data context. How the meanings of data are addressed in statistics texts is essential since they are not only associated with statistical reasoning (Bakker & Gravemeijer, 2004;Konold et al, 2015;Pfannkuch, 2011) and the conception of statistics (Idris & Yang, 2017), but also reflect the attempt to prepare statistically literate citizens (Weiland, 2019), which is the goal of statistics education at the college level (Aliaga et al, 2005).…”
Section: The Voice Of Statisticsmentioning
confidence: 99%
“…How the meanings of data addressed in statistics texts may associate with reasoning (Bakker & Gravemeijer, 2004;Konold et al, 2015;Pfannkuch, 2011) and conceptions of statistics (Idris & Yang, 2017).…”
Section: Learners' Meanings Of Statistical Termsmentioning
This article develops an analytical framework for analysing college (tertiary) statistics textbooks in terms of text accessibility by integrating the text, the reader, and the content into the framework. Five accessibility attributes of science texts were adapted to conceptualize the accessibility of statistics texts. For each accessibility attribute, two components were proposed by referring to the literature on the readability of mathematics texts as well as the characteristics of statistics. The feasibility of the framework is demonstrated by analysing sample statistics texts. The contributions and potential of the framework are discussed.
“…Focusing on statistical cognitions (Garfield & Franklin, 2011;Lei & Yang, 2012), which connect reader's cognition to statistical concepts and meanings of data (Idris & Yang, 2017), which connect reader's experience to statistical contexts.…”
Section: Voice Of Authorsmentioning
confidence: 99%
“…That is, the reader is drawn into the text to make sense of one abstract concept through several perspectives. Making sense of a statistical concept would include understanding statistical basic knowledge, reasoning, and thinking related to the concept (i.e., statistical cognitions; Garfield & Franklin, 2011;Lei & Yang, 2012), as well as understanding data and contexts used in presenting the concept (i.e., meanings of data; Idris & Yang, 2017). The statistical cognition presented in the text would connect the reader's cognition to the statistical concept, while the meanings of data would connect the reader's experience to the statistical contexts.…”
Section: The Voice Of Statisticsmentioning
confidence: 99%
“…For instance, when the term mean is introduced, data as numerical numbers without context might be addressed by focusing on the calculating procedures, while data as numbers in problem contexts can be addressed to elaborate and connect the results of calculation into the data context. How the meanings of data are addressed in statistics texts is essential since they are not only associated with statistical reasoning (Bakker & Gravemeijer, 2004;Konold et al, 2015;Pfannkuch, 2011) and the conception of statistics (Idris & Yang, 2017), but also reflect the attempt to prepare statistically literate citizens (Weiland, 2019), which is the goal of statistics education at the college level (Aliaga et al, 2005).…”
Section: The Voice Of Statisticsmentioning
confidence: 99%
“…How the meanings of data addressed in statistics texts may associate with reasoning (Bakker & Gravemeijer, 2004;Konold et al, 2015;Pfannkuch, 2011) and conceptions of statistics (Idris & Yang, 2017).…”
Section: Learners' Meanings Of Statistical Termsmentioning
This article develops an analytical framework for analysing college (tertiary) statistics textbooks in terms of text accessibility by integrating the text, the reader, and the content into the framework. Five accessibility attributes of science texts were adapted to conceptualize the accessibility of statistics texts. For each accessibility attribute, two components were proposed by referring to the literature on the readability of mathematics texts as well as the characteristics of statistics. The feasibility of the framework is demonstrated by analysing sample statistics texts. The contributions and potential of the framework are discussed.
A statistics textbook should provide an opportunity for students to acquire skills in statistical cognition which involve statistical basic knowledge, reasoning and thinking. The meaning of data, on the other hand, is crucial for presenting the cognition. This study analyzed topic of data distributions presented in the English and Indonesian college statistics textbooks. While the Indonesian version heavily emphasizes on the basic knowledge related to procedures, the English version, variously elaborates each content with basic knowledge or reasoning. Data as numerical numbers are presented for showing procedures in both versions of textbooks. More diverse meaning of data, however, was found in the English version, including data as numbers with meaningful contexts to represent problems, and as information to be evaluated or criticized using reasoning and thinking.
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