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Graphing Scientific Data

Why?. Most students in science courses know how to graph. Why?. Most students in science courses think they know how to graphThis exercise lets them discover what they know and don't knowSelf-assessment is usedResult better graphs!. Learning Outcomes. Define termsStudents create and assess g

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Graphing Scientific Data

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    1. Graphing Scientific Data From a Mathematics Across the Curriculum (MAC) coordinated studies class with Biology 201 (Fall 2000) at Edmonds Community College Melissa Mackay (Math) Jenny McFarland (Biology)

    2. Why? Most students in science courses know how to graph

    3. Why? Most students in science courses think they know how to graph This exercise lets them discover what they know and don’t know Self-assessment is used Result – better graphs!

    4. Learning Outcomes Define terms Students create and assess graphs Students create graphs in small groups Critique graphs in large group Students develop graphing rubric Students practice self-assessment Self-assess graphs in lab reports Students choose appropriate graphs for particular data sets Scatter plots & line graphs Bar graphs & histograms Pie charts

    5. Define the following terms: dependent variable independent variable scatter graph line graph bar graph pie graph or chart best fit line slope interpolation extrapolation

    6. Group Exercise For the set of data assigned, create a graph/chart that you think best displays the data. Use the graph paper (if needed) and colored pencils Groups will share graphs with large group.

    7. Data

    8. Graphing Rubric/Criteria Students in class or lab session develop rubric Appropriate, descriptive title Axes dependent & independent variables on correct axes labels units good scaling Clear data points (easy to differentiate different symbols) Key (if plotting more than one set of data) Appropriate line-fitting best-fit line curve-fitting connecting dots Use graph paper or computer-generated graphs

    9. Student Self-assessment Student self-assessment is crucial to student learning requires students to understand assessment criteria requires that students do some critical analysis of their work, usually improving the product allows students to take responsibility for their work empowers students - so they know when they have performed well and do not rely on the instructor’s assessment alone

    10. Self-assessment example From Biology 201 Photosynthesis Lab report: Graph the absorbance spectra measured in your lab session. What type of graph did you use? Why? (Explain your graph choice.) Evaluate your graphs for this lab report. Are they excellent, adequate or poor? Can a reader look at this graph and know what you did? What criteria are you using to evaluate your graphs? Be explicit; list the criteria and state how you met each one.

    11. Bad Graphs - Scaling Bad scaling is a common problem in student graphs. Students often need to see several examples. Presenting opportunities for students to assess graphs with bad scaling will help them recognize this and give them practice with better scaling alternatives. From: “Making Graphs” http://scipp.ucsc.edu/~mothra/ta/graph/graph.html

    12. Bad Graphs - missing information What is missing? Students often have information missing from their graphs. Self-assessment helps students catch these omissions. This problem is complicated by missing information in many textbook figures, where graph axes might not be labeled or may not have units. From: http://www.ecs.umass.edu/ece/ece211/ECE211_graphing_techniques.html

    13. Bad Graphs - area Multidimensional variation occurs where two-dimensional figures are used to represent one-dimensional values. The size of the graphic is scaled both horizontally and vertically according to the value being graphed. This results in the area of the graphic varying with the square of the underlying data, causing an exaggerated effect in the graph. This graph has a lie factor of about 2.8, (variation between the area of each doctor & the number it represents). Students do not usually generate this type of graph, but they often see it in the media. From: Pitfalls of Data Analysis (or How to Avoid Lies and Damned Lies) by Clay Helberg, M.S. Tufte, E.R. (1983). The Visual Display of Quantitative Information. Cheshire, CT: Graphics Press. p. 69

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