2014
DOI: 10.1080/10986065.2014.921132
|View full text |Cite
|
Sign up to set email alerts
|

Fifth Through Eighth Grade Students’ Difficulties in Constructing Bar Graphs: Data Organization, Data Aggregation, and Integration of a Second Variable

Abstract: Abstract:Studies that consider the displays that students create to organize data are not common in the literature. This paper compares 5th through 8th graders' difficulties with the creation of bar graphs constructed using either raw data (Study 1, n=155) or a provided table (Study 2, n=152). Data in Study 1 showed statistical differences for the type of data organization but not for grade level. Students' primary problem was choosing a format that integrated a second variable and aggregating data. In contras… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
2

Citation Types

0
7
0
3

Year Published

2020
2020
2023
2023

Publication Types

Select...
5

Relationship

0
5

Authors

Journals

citations
Cited by 11 publications
(10 citation statements)
references
References 35 publications
0
7
0
3
Order By: Relevance
“…Regarding the collection and processing of data, it should be noted that this is not a common skill in literature and, therefore, has become a challenging issue for elementary students (Garcia-Mila, Marti, Gilabert, and Castells 2014). In the present work, this scientific skill was a statistically significant difference between the pre-and the post-test results (Table 2).…”
Section: Discussionmentioning
confidence: 45%
“…Regarding the collection and processing of data, it should be noted that this is not a common skill in literature and, therefore, has become a challenging issue for elementary students (Garcia-Mila, Marti, Gilabert, and Castells 2014). In the present work, this scientific skill was a statistically significant difference between the pre-and the post-test results (Table 2).…”
Section: Discussionmentioning
confidence: 45%
“…Indeed, previous research in science education has explored science process skills related to experimentation (Padilla, Okey, & Garrard, 1984) and cognitive processing of graphs (Wang et al, 2012; Wavering, 1989). However, longitudinal analyses of graph comprehension growth among middle and high school students are situated primarily in mathematics education (cf., Curcio, 1987; Garcia‐Mila, Marti, Gilabert, & Castells, 2014; Hattikudur et al, 2012; Nathan & Kim, 2007; Padilla, McKenzie, & Shaw, 1986). Our work, thus, contributes to science education by building on the longitudinal work in mathematics education and the previous work in science education around experimentation and graph comprehension.…”
Section: Discussionmentioning
confidence: 99%
“…1 In construction of statistical graphs, students continue to transit to an aggregate view of individual cases through quantification of aggregate properties (like ratios, portion, or percentage) of data values in statistical graphs (Wilson, n.d.) 2 Students are able to construct statistical graphs associated with a bivariate data set (Casey, 2015;Garcia-Mila et al, 2014) 3 With regard to the selection and usage of the appropriate graph form for the nature of a given data set, students develop additional statistical graphs to represent a (univariate or bivariate) numerical data set (Franklin et al, 2007;cf. Baker et al, 2001) Bar graph: In drawing a bar graph for a given bivariate data set (a list of 25 students who indicated gender and height), a student draws bars with frequencies of student height intervals on the y-axis (dependent variable) by student gender types on gender on the x-axis (two independent variables; Garcia-Mila et al, 2014) ↑ Transition from Level 4 to Level 5: Sufficient data aggregation (and data reduction) emerges and continues to mature into the upper levels of this progression 4…”
Section: Data Display Tasks For Data Presented In a Graphmentioning
confidence: 99%
“…3 With regard to the selection and usage of the appropriate graph form for the nature of a given data set, students are able to identify appropriate types of statistical graphs to represent numerical or categorical data (e.g., drawing a bar graph for categorical data and a histogram for numerical data; Franklin et al, 2007) Bar graph: A student draws bar graphs for major divisions of the data unit scale, like 10s or 50s (Åberg-Bengtsson, 2006) Scatterplot: Students place the informal line of best fit through the most points, or through the first and last points among all the points on a scatterplot, revealing the transition from case-oriented view to aggregate view of data; they attend to a subset of collinear points on the scatterplot, while thinking of the points that were not collinear as outliers (Casey, 2015) 1 In drawing a given form of statistical graphs, students construct graphical components that are appropriate for the given graph form and use scaling and labeling correctly with proper data format (Baker et al, 2001;Garcia-Mila et al, 2014;Lehrer, 2011, as cited in National Research Council, 2014, but this depends on the complexity of given data. For example, in bar graphing, the x-and y-axes are appropriately constructed and labeled; individual cases of categorical variables are structured on the x-axis and quantitative values are structured on the y-axis; the magnitude of the quantitative values on the y-axis are represented by the heights of bars with a conventionally graded scale…”
Section: Data Display Tasks For Data Presented In a Graphmentioning
confidence: 99%
See 1 more Smart Citation