Inquiry Space: Using Graphs as a Tool to Understand Experiments

• In Event: Student Thinking, Learning, and Inquiry With the Common Online Data Analysis Platform

Abstract

This study explored what happens when students conduct data analysis using a unique tool to inform decisions related to science inquiry experiments. Research questions include:

(1) What about the software facilitates student reasoning about patterns in data and how graph features relate to their experimental results?
(2) How did students make use of the different kinds of graphs and other data representations?

Students at all levels have difficulty constructing, interpreting, and connecting graphs to real-world phenomena (Beichner 1994; Clement et al. 1986; McDermott et al. 1987) and often do not view graphs as a tool for thinking about data (Masnicki, et al 2007). However, students can reason about variation in data. When using a statistical visualization tool, students from third grade up used a process of “informal inference” (Rubin et al. 2006) to reason about signal and noise and other variability in data, and this occurred without their having been taught the mathematics of statistical inference (Ben-Zvi, 2006; Paparistodemou & Meletiou-Mavrotheris, 2008). We hypothesize that visualization tools and sensors can help with reasoning about physics and data analysis issues and that seeing graphs produced in real time can confer an advantage.
The team integrated into a single, browser-based environment: 1) software developed by the project to collect data from probeware, 2) computational models and 3) CODAP. We developed a sequence of increasingly open-ended computer-based activities using games, sensors, and computer models. Classes completed nine activities culminating in an independent project.

We collected 219 research screencasts from 21 small groups in classrooms of two teachers. Purposeful sampling was used to identify 15 screencasts for qualitative analysis. We identified episodes where students were working with the graphs and tables, responding to unexpected data patterns, or engaged in metacognition. We used a constant comparative method to identify themes, refining them iteratively as additional episodes of student reasoning were examined.

Students 1) had strong responses to graphical anomalies; 2) were able to coordinate use of data representations and features to make sense of puzzling data; 3) were able to use graphs as a tool to help understand experiments and 
concepts.

The tight link between experiment and data representation and the short loop between data collection and running another experiment appeared to help students ground their understandings in their own activity, map on-screen patterns to the experiments they had just run, formulate their own questions, and feel a connection with their data that led to surprisingly strong reactions to any anomalous patterns. Even frequently disengaged students were able to use informal inference to apply information in the graphs to their experiments. Scaffolds in the IS environment appeared to enable students to engage quickly in higher order reasoning.
Rather than requiring a technical understanding of graphs before using them as a tool, these students appeared to move in the opposite direction—from a somewhat facile use of graphs to an exploration of their specific features. This study has implications for the design of supports to help students engage in higher order reasoning with graphs.

Authors

• A. Lynn Stephens, University of Massachusetts

• Amy Pallant, Concord Consortium