One of the most interesting educational uses of the Internet is as a method for engaging science students. By collecting and sharing data with students in other locations around the world, students can undertake a type of inquiry that more closely resembles real science than what they typically do in the science classroom. There are now several “network science” projects of this sort, involving thousands of students across a range of grade levels. How are these projects doing? What are teachers and students likely to find on project sites when they go there? If they download data from one of these sites, what are they likely to find in those data and what kind of tools will they need to explore them? This set of technical reports tries to answer these questions by examining and critiquing instructional tasks and data from five projects: EnviroNet, Estuary Net, GLOBE, Journey North, and Water on the Web. These reports, written by Rolf Biehler and Stefan Schweynoch, are part of a study funded by the National Science Foundation to learn more about the challenges that statistics and data analysis pose to teachers and students. A primary audience for these reports are the designers of the specific projects reviewed. They will be interested, in particular, in what the report authors found in project data and what skills and tools were necessary to answer some of the questions that the projects’ curricula pose to students. In short, Biehler and Schweynoch found some interesting trends in the data, but getting at these were typically not a simple matter and probably beyond the abilities of students if unassisted. These reports will also be useful to developers of other current and future network science projects and to educational designers working to involve K-12 students in the analysis of real data. One of the most critical and difficult aspects of creating successful data-centered curricula is selecting data and questions that are appropriate for students. This is much harder than many imagine. The analyses Biehler and Schewynock perform and the questions they raise in these reports can serve as useful examples of what designers should be asking themselves as they consider using various problems and data sets with students. The report on each project is self contained; thus you can read them in any order. Furthermore, you can read each section of a report without reading the others sections. I’ve included below additional information about network science, the writing of these reports, and the NSF-funded research project which undertook this study.
Clifford Konold, Principal Investigator Associate Research Professor Scientific Reasoning Research Institute Lederle Graduate Research Center University of Massachusetts Amherst, MA 01003-3725
This set of technical reports is one component of the NSF-funded project “A study of student investigations in data-sharing projects” (Grant No. REC-9725228). In this research project, we worked with several of the growing number of "network science" curricula, as well as a few other selected sites, to research conceptual challenges students face in analyzing data.
Network science projects (as Feldman, Konold and Coulter, 2000 refer to them) 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. Part of the rationale of this approach is that student interest in and understanding of science will be enhanced by involving them in genuine scientific inquiry. But at a deeper level, this approach to science education is based on a view of the nature of work and learning in the emerging information age. According to this view, what is becoming increasingly important in our society is not a fixed set of skills or knowledge held by individuals acting in isolation, but the ability of groups of individuals to cooperate in the construction of knowledge (Hunter & Richards, 1996). What 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 these projects 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 the challenges that students face in analyzing data, we concentrated on two sources: 1) students ideas and conceptions and 2) data and other instructional resources that projects offer students. This report shares some of our findings from the later. Our reason for looking closely at the content of network science projects is well captured by the observation of Feldman, et al. (2000):
Looking across a range of network science curricula, we see the same problems time and again. Too often students are working with contexts foreign to them, with plots they struggle to interpret, with data short on interesting trends and so full of errors that even these trends are hopelessly masked. (p. 115)
In their study, they looked briefly at data from two network science projects. From these two projects they made a number of recommendations about the kinds of data that are suitable for students. In this current study, we look more closely at data made available by projects and across a wider range of projects, to
· evaluate how difficult it is with these data and the tools available to students to address the questions posed by the curricula
· evaluate the reliability of the data
· determine whether the trends and patterns in the data are sufficiently strong that we think students would attend to them
· uncover trends in the data of which the projects may be unaware and which could be fruitfully explored by students.
We considered a number of factors in selecting the five projects to investigate. First, we wanted to look at projects ranging across various grade levels. Also, we wanted to look at projects using 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. Given that Feldman et al. (2000) had looked rather closely at data from EnergyNet and Global lab, we excluded these.
About the Report Authors
Rolf Biehler is Professor for Mathematics Education, Universität Gesamthochschule Kassel. In addition to teaching Exploratory Data Analysis to both students and future teachers, he has directed several research and development projects in the area of statistics education, as well as projects on designing and evaluating software for learning and teaching mathematics and statistics. He is a member of the international BaCoMET group of researchers in mathematics education and is currently co-director of the BaCoMET V project on The distinctive professional knowledge of mathematics teachers. He is also interested in Mathematics education as a scientific discipline, a title of a book he recently edited.
Stefan Schweynoch is a secondary mathematics teacher and has been working as a Researcher at the Institut für Didaktik der Mathematik, Universität Bielefeld and at the Dept.of Math. and Informatics at the University of Kassel in 1999 and 2000.
Hunter, B., & Richards, J. (1996). Learner contributions to knowledge, community, and learning. inet.ed.gov/~gsolomon.
Feldman, A., Konold, C., & Coulter, R., with Conroy, B., Hutchison, C., & London, N. (2000). Network science, a decade later: The internet and classroom learning. Mahwah, NJ: Lawrence Erlba