Students’ Emerging Reasoning about Data Tables of Large-Scale Data

Prodromou, Theodosia

doi:10.5539/ijsp.v4n3p181

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…These typically contain only some of the information collected and are organized to facilitate comparing groups or detecting trends. A number of researchers have explored the use of statistical summary tables (Estrella, 2014;Martí, Garcia-Mila, Gabucio, & Konstantinidou, 2011;Prodromou, 2015) and developed criteria for producing easy-to-read displays (Ehrenberg, 1977). In this article, we explore the ways in which students record and organize data, and thus the tables that we will consider are case-data tables.…”

Section: Figure 2 Clay Tablet From Ur Earliest Known Use Of a Tablementioning

confidence: 99%

Modeling as a Core Component of Structuring Data

Konold

Finzer

Kreetong

2017

SERJ

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We gave participants diagrams of traffic on two roads with information about eight attributes, including the type of each vehicle, its speed, direction and the width of the road. Their task was to record and organize the data to assist city planners in its analysis. Successfully encoding the information required the creation of a case, a physical record of one repetition of a repeatable observational process. We analyzed data sheets participants created including the methods they used to bind information together into cases. Overall, 79% of their data sheets successfully encoded the data. Even 62% of the middle school students were able to create a bound structure that could hold the critical information from the diagrams. A majority of these structures involved a hierarchy of cases rather than the “flat” case-by-attribute structure that virtually all statistical software require. Our sense is that participants strove to create a structure that modeled the real-world as closely as they could, constructing cases that corresponded to the different sorts of objects they perceived—vehicles with their characteristics nested within road segments with their characteristics. First published November 2017 at Statistics Education Research Journal Archives

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Section: Figure 2 Clay Tablet From Ur Earliest Known Use Of a Tablementioning

confidence: 99%

Modeling as a Core Component of Structuring Data

Konold

Finzer

Kreetong

2017

SERJ

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“…Multiscale readability: Tables support efficient comparison and are good for looking up values [34]. One study [74] showed that students effectively used different access patterns (whole table scan, horizontal and vertical reading, and targeted comparison) to develop a deep understanding of a multidimensional dataset.…”

Section: Using Rich Tablesmentioning

confidence: 99%

Untidy Data: The Unreasonable Effectiveness of Tables

Bartram

Correll

Tory³

2022

IEEE Trans. Visual. Comput. Graphics

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Fig. 1: An example spreadsheet (shared with permission of Cornerstone Architects) showing various "rich" table features that our participants employed, including (1) A Master Table of base data that is often left untouched, with manipulations happening in a copy or other area separate from the base data; (2) Marginalia such as comments or derived rows or columns in the periphery of the base table, often taking the form of freeform natural language comments; (3) Annotations such as highlighting or characters with specific meaning (e.g., a dash denotes missing values) to flag particular cells as anomalous or requiring action; and (4) Multi-cell features such as labels or even data that span multiple rows or columns of the sheet.

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“…and 5: WHY do they change? Recently, Prodromou (2015) has demonstrated how Curcio's original conceptualization can be applied as part of group-work of students interpreting large 2-way tables comparing population trends (demographic changes, emigration and immigration, etc) across European countries.…”

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confidence: 99%

Understanding basic demographic trends: connecting table reading, task design, and context

Gal¹,

Trostianitser²

2016

Promoting Understanding of Statistics About Society IASE Roundtable Conference

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The paper aims to inform the conceptualization of teaching/learning goals and instructional design related to understanding multivariate statistics that describe trends in society. The paper presents a conceptual analysis leading to identification of a gap in current analyses and instructional sequences focused on interpretation of information in tables [and graphs]. The paper advocates for the need to develop students' ability to reflect about the factors that cause or are related to observed trends, and about their societal implications, and highlights the need to develop task design principles that can promote such understanding. We then present an empirical demonstration of a possible class sequence, and results from an exploratory class activity with 43 students whose responses were content-analysed. Based on these results and the prior analysis, we reflect on benefits in terms of increased motivation and relevance of statistics instruction, and call for the use of task design principles that directly connect data, statistics, and class activities to the actual societal context in which students have to operate as adults, in order to enhance students' statistical literacy and understanding of statistics about society.

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Students’ Emerging Reasoning about Data Tables of Large-Scale Data

Cited by 5 publications

References 13 publications

Modeling as a Core Component of Structuring Data

Modeling as a Core Component of Structuring Data

Untidy Data: The Unreasonable Effectiveness of Tables

Understanding basic demographic trends: connecting table reading, task design, and context

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