Data Sharing

Overview The primary objectives of this research project were to a) identify the core ideas in rudimentary data analysis, b) research the methods students typically employ to compare two groups or judge the relationship between two variables, and c) identify features of data and tasks that we should attend to in designing instruction. Our research was aimed at informing teachers as well as the development of future data analysis projects, materials, software, and teacher development efforts.

We focused in particular on the data collection and analysis practices of students participating in "network science" projects. Network science projects use the Internet to link distant classrooms so that they can pool locally-collected data for aggregate analyses. In Global Lab, for example, at noon of the fall equinox students from all over the world collect a variety of local information and use these data to study such things as the relation between geographic location (sun angle) and measured light intensity. One important advantage of this approach is that the focal point of student investigations is not the data analytic techniques per se, but the phenomena students are exploring.

One of the things that prompted us to undertake this research were reports, both formal and informal, from several Network Science projects that their students were having considerable difficulty analyzing data. Given that the objectives of Network Science depend on students not only collecting and sharing data, but also reasoning about and learning from data, these difficulties present a serious barrier to fostering authentic science and mathematics learning. To explore these difficulties, we looked both at the projects (the data and materials they provided students) and at the students (the capabilities they brought to the task of data analysis).

We studied five Network Science projects: EnviroNet, GLOBE, Journey North and Water on the Web. We chose sites that a) ranged across various grade levels and b) used a variety of data types, including geographical based data, time series data, and case-based data sets that included multiple variables which students could explore. As part of the study conducted at TERC (and reported in Feldman et al, 2000) we also looked at Global Lab and EnergyNet.

In our studies of student reasoning, we are working with the following data sources:

• Interviews and final project reports of 9th grade science students involved with EnviroNet in Derry, NH (4 groups of 2 students).
• Interviews and final project reports of 7th grade students involved with EnviroNet in Rye, NH (3 groups of 4 students).
• Interviews and final project reports of 12th graders at Holyoke High School, Holyoke, MA (2 groups of 2 students; about 12 final projects).
• Pre-post instruction interviews of 7th and 8th grade students in a teaching experiment at Vanderbilt University in Nashville, TN (approximately 15 individual interviews, and 6 interviews with pairs of students.)
• Analysis of a casebook of teachers' reflections on reasoning of K-6 students from the "Teaching to the Big Ideas" project directed by Deborah Shifter, Virginia Bastable, and Susan Jo Russell (34 written case studies).
• Interviews with psychology students at LaTrobe University, Melbourne, Australia (13 individual interviews with 1st and 4th year undergraduates).

Major Findings

1. Network Science projects tend to underestimate the complexity of real data analysis and the amount of support they therefore need to provide to both students and their teachers. While we find some interesting and interpretable trends in some of their data, the skill required to detect these trends is typically beyond the unaided ability of even the best of students. This may help explain why we were able to find few participating classrooms that have an established history of analyzing data downloaded from Network Science sites. Our analysis of the problems and specific recommendations about how to address them are spelled out in Feldman, Konold, and Coulter (2000), and in a series of technical reports (see introduction).

2. Through our analysis of student thinking in classrooms, we have a much better understanding of the ideas that young students bring to their early experiences with data, and how these ideas might be further developed with instruction. Our research is described in various books and articles, several of them written specifically for teacher audiences. In two articles by Konold and Pollastek, we describe what we have come to regard as the core idea in data analysis and statistics -- the idea of central tendency. We analyze the nature of this idea in various contexts, trace its historical development, and suggest how the idea might be better developed instructionally.

Our understanding of students in the elementary grades how they learn to shift their focus from data as pointers, to case values, to aggregates was based primarily on our study of case studies written by elementary school teachers participating in the teacher development project "Teaching to the Big Ideas" directed by Deborah Shifter, Virginia Bastable, and Susan Jo Russell. These results are summarized in two articles by Konold and Higgins.

Our understanding of student thinking at the middle school, and how they begin to see data as an aggregate, was based primarily on our interviews with students participating in the RoadKill project at two schools in New Hampshire. These are being summarized in an article by Konold, Robinson, Khalil, Pollatsek, Well, Wing, & Mayr, (in preparation).

This project is supported in part by a grant from the National Science Foundation (REC-9725228). Opinions expressed here are those of the project staff and do not necessarily reflect the views of the Foundation.