# An Assessment of Ninth Graders' Use of Graphs and Explanations to Communicate Scientific Ideas

8-2013

## Level of Access Assigned by Author

Campus-Only Thesis

## Degree Name

Master of Science (MS)

Teaching

Molly Schauffler

Natasha Speer

Sarah Nelson

## Abstract

Proficiency in science learning involves demonstrating data literacy, or mastering skills and language to communicate data. Components of data literacy include analyzing data tables, developing hypotheses, creating graphs, and explaining if and how those graphs support a hypothesis. New math and science education standards emphasize representing data and referring to evidence when discussing claims. Data from the National Assessment of Educational Progress (NAEP) suggests that student proficiency in the United States in secondary school is below district, state, and national target levels for Adequate Yearly Progress (AYP) on specific tasks related to data literacy. For example, many students learn to calculate the mean and median but do not learn how to discuss the variability of data sets. This study examines the ability of students to utilize key statistical concepts necessary for data literacy.

I examined the extent to which ninth grade students could (a) produce mechanically correct graphs, (b) use statistical vocabulary when discussing data and (c) interpret the meanings of graphs by way of producing scientific explanations. Producing mechanically correct graphs included (1) selecting an appropriate type of graph and (2) constructing that graph with prerequisite components as well as accurate representation of the data. Students were expected to use statistical vocabulary to effectively communicate data. An adequate and accurate scientific explanation required a claim with sufficient reference to the variability of the data.

Sixty-four students from a public high school in Maine participated in a written survey. The survey contained two sets of data, each with a given hypothesis: one compared two groups such as, “there are more earthworms in hardwood forests,” and the other correlated two phenomena such as, “older fish contain more mercury.” Participants were asked to create a graph to help them determine whether or not the data supported the hypotheses.

Students scored below the Maine Department of Education AYP target in Mathematics of 70%. The percentage of students who selected an appropriate graph was 14% for the comparison question and 45% for the correlation question. The average scores for constructing mechanically correct graphs were 67% for the comparison question and 75% for the correlation question. When producing scientific explanations, the percentage of students that came to a reasonable conclusion with a reference to variability was 9% on the comparison question and 8% on the correlation question.

An additional twelve students participated in follow-up interviews. I used stratified random sampling from the written survey results to select interview participants. When discussing data in interviews, more than 50% of students used only three out of fifteen words (20%) on the statistical vocabulary list: distribution, mean, and range.

Despite the widespread adaptation of the Common Core curriculum across the country, student proficiency may increase with an educational focus on mastering and effectively using fundamental statistical concepts. Students and teachers may benefit from engaging lessons involving data collection and analysis, facilitating better communication of data.