Interpreting and creating graphs plays a critical role in scientific practice. The K-12 Next Generation Science Standards call for students to use graphs for scientific modeling, reasoning, and communication. To measure progress on this dimension, we need valid and reliable measures of graph understanding in science. In this research, we designed items to measure graph comprehension, critique, and construction and developed scoring rubrics based on the knowledge integration (KI) framework. We administered the items to over 460 middle school students. We found that the items formed a coherent scale and had good reliability using both item response theory and classical test theory. The KI scoring rubric showed that most students had difficulty linking graphs features to science concepts, especially when asked to critique or construct graphs. In addition, students with limited access to computers as well as those who speak a language other than English at home have less integrated understanding than others. These findings point to the need to increase the integration of graphing into science instruction. The results suggest directions for further research leading to comprehensive assessments of graph understanding.
We present a new system for automated scoring of graph construction items that address complex science concepts, feature qualitative prompts, and support a range of possible solutions. This system utilizes analysis of spatial features (e.g., slope of a line) to evaluate potential student ideas represented within graphs. Student ideas are then scored with rubrics based upon the knowledge integration framework (Linn & Eylon, 2011). We tested the effectiveness of this system on graphs constructed by 397 8th-12th grade students preceding, during, and following a curriculum focusing on graphs of motion. Comparison with human-coded responses indicates that the automated scoring system is very accurate (k ¼ 0.9). Also, ideas represented in constructions were generally similar to those demonstrated in written explanations; although individual students often shifted ideas between items. Learning gains were similar in both written and graph construction formats. Overall, these results suggest that graph construction is a valid and efficient means of evaluating students' complex ideas about data representation in science. We discuss the opportunities for incorporating graph construction into new science content areas, such as graphs representing density. We consider the implications of this system for generating automated, adaptive guidance to support instruction.
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