Students
know tables, charts, illustrations and graphs can be used in making
arguments and claims in oral and written presentations. E/S
The ability to organize collected data and graphically represent it in a fashion that is not only accurate but informative is a necessary skill. The ability to collect, organize, analyze and display data is essential to a scientifically literate person. However, this skill transcends the boundaries of science and is commonly used in and is fundamental to daily life. People are constantly collecting data, (e.g. “How fast am I driving?”, “How far is it to the stop light?”, “What food do I need to buy at the grocery store?”) analyzing data, (e.g. “Can I stop if the light turns yellow?”, “Should I stop or will I be rear ended?”, “Am I committed to entering the intersection?”, “Can I wait one more day to go shopping?), and acting on that data. In the examples above, there was probably no graphical representation of data unless the person left a “line graph” from each of their rear tires (which could be data that the police collect and analyze). Although the oral presentation may not be fit to record here, there were probably no written presentations, unless the police were analyzing the data from the scene. Often times due to the common nature of this skill, teachers overlook its full importance. Science teachers will frequently concentrate their efforts on teaching the students the subskills of collecting and graphing data to the detriment of analyzing the data, and even making “data supported” arguments.
Students at all levels can and should be taught all aspects of this benchmark. There is no legitimate reason to concentrate on one or two subskills in exclusion of the others. Being able to (accurately) collect data and graph the data are necessary skills. However, lacking the skill to make sense of what that data means or not being able to use the data to support or refute an argument makes the skill of graphing just short of useless.
There are basically two types of data that a student can collect: qualitative or quantitative. Qualitative data is extremely varied in nature. It can include virtually any information that is not numerical. Examples of quantitative data include indepth interviews, direct observations and written documents. Quantitative data is numerical in nature. It is data that is measured or identified on a numerical scale. Quantitative data can be analyzed using statistical methods and the results can be graphically displayed using histograms, charts, tables and graphs.
For a detailed discussion on statistical analysis of data see TIPS Benchmark N.12.A.3
How the collected data is organized is dependent on the type of data being gathered. A data table is often the first step in organizing and recording the collected data. Although a data table can be used to organize qualitative data, the data table is essential when quantitative data is being gathered.
When provided with a written description of an experiment, the students should be able to determine what data will need to be collected and what if any data will need to be derived (calculated from the data gathered). Data tables provide an organized method of recording the data that has been collected. There are no absolute rules when it comes to the design and use of a data table, but there are some commonly used conventions that should be followed when using a data table. One of the first steps students should do when using a data table is to look at the experiment, survey or situation they are to be observing and try to determine what data will need to be recorded. The types of data and the relative quantity of data should first be determined before the students start to design and draw the required table. For example, the following questions may be considered: Will temperature and time be collected? If so, how long will the data be collected, and at what interval will the collection take place? Is the data more complex in nature with multiple variables being analyzed, and/or multiple trial runs?
The answers to the above questions will determine the number of columns and rows the data table must have in order to meet the needs of the data being collected. How the data table is organized to meet these needs is also very important. A data table has certain requirements:
1. Title: A descriptive title is needed to inform the reader as to what data has been collected and what, if any, manipulation has been done to that data.
2. Column Label: Each column of the data table must be labeled, with units noted, as to what data has been recorded. Example units include time (seconds), temperature (degrees C), speed (km/hr), mass (kg).
3. Data: The actual data collected. As part of the planning phase the student should have determined how many significant figures for each data value.
When setting up the data table, the Independent Variable should be in the left hand column, while the Dependent Variable in the right hand column (Figure 1). If there were multiple trials, the independent variable is not rerecorded for each run, but a new dependent variable column is added to the right of the previous trial run (Figure 2). After the data has been collected, and some mathematical/statistical manipulation is performed; the resulting value(s) is considered the derived data and is placed in the far righthand column of the data table. Sequencing the data in this order, independent, dependent and derived will help the students when it comes time to construct a graph. Ordering the data pairs (independent, dependent) is a convention used when graphing data.
It is important that the students understand the difference between independent and dependent variables. The Independent Variable, also known as the manipulated variable, is the variable that the scientist chooses to control. In the case of the Ball Drop experiment, the independent variable is the height the ball is dropped from, in a Temperature/Time experiment it would be the time interval at which measurements are taken that is being manipulated (e.g. The change in the temperature of a liquid is measured at one minute intervals). The Dependent Variable, also known as the responding variable, is the variable that changes in response to the independent variable. The height of the balls bounce is DEPENDENT on the height at which the ball was dropped. As the independent variable is manipulated, the height changed, the dependent variable responded accordingly.
The Effect of Drop Height on Bounce Height of a Rubber Ball 
Height of Drop (cm) 
Height of Bounce (cm) 
5 
4 
10 
6 
15 
11 
20 
13 
25 
16 
30 
21 

Figure 1. Sample Data Table for One Trial Run 
The Effect of Drop Height on Bounce Height of a Rubber Ball 
Height of Drop (cm) 
Height of Bounce (cm) 
Average Bounce Height for all
Trials (cm) 
Trial 1 
Trial 2 
Trial 3 
5 
4 
3 
2 
3 
10 
6 
6 
5 
5.6 
15 
11 
12 
11 
11.3 
20 
13 
14 
14 
13.7 
25 
16 
15 
16 
15.7 
30 
21 
20 
21 
20.7 
Figure 2. Sample Data Table for Three Trial Runs
When attempting to graph the data from the above example, the following data pairs would be used for graphing Trial #1 (5, 4), (10, 6), (15, 11), (20, 13), (25, 16) and (30, 21).
Graphs communicate data in a pictorial form and thus may allow the viewer to quickly and clearly see trends and patterns that may exist in the data collected. There are many types of graphs that students should be familiar with making, reading and analyzing. These graphs include line, bar, histograms, scatter plots, and pie charts or circle graphs.
Line Graph
Line graphs are best used when the there is a connection between each of the dependent variable’s data points. (Figure 3, “The Effect of Drop Height on Bounce Height of a Rubber Ball.”) In other words, when the independent variable is manipulated the dependent variable will respond in a predictable fashion. In Figure 2 and Figure 3, we see that when the drop height for the ball increases the bounce height also increases. There is a relationship between the drop height and bounce height of the ball, students could reasonably ascertain the approximate formula that would be able to mathematically predict what the given bounce height would be for an untested drop height.
Figure 3. A graph depicting “The Effect of Drop Height on Bounce Height of a Rubber Ball”
As with data tables, there are no hard and fast rules to graphing, but there are widely accepted conventions that should be followed.
Title: The graph’s title should be descriptive of what data has been plotted. The title along with the axis labels should allow the reader to develop a clear understanding what will be graphically represented. An appropriate title for a graph of Figure #1 Ball Bounce Data Table might be: A Graph of the Effect of Drop Height on the Bounce Height of a Rubber Ball. The title of the graph is very similar to that of the data table as both are representing the same information, just in two different formats.
Xaxis: The horizontal axis used to plot the independent (or manipulated) variable. The independent variable is the first data value in an ordered pair. If the data being plotted was from the Ball Bounce Height Data Table shown above, the Drop Height would be plotted on the xaxis. If the experiment were a time verses anything graph, time would be plotted on the xaxis.
Yaxis: The vertical axis used to plot the dependent (responding) variable. Using the Ball Bounce data table example, the Bounce Height would be plotted on the yaxis.
Axis Labels: Should be descriptive of what is being plotted and must include the measurement units. For example, Drop Height (cm), Bounce Height (cm), Time (s), Distance Traveled (km).
Interval Scale: The distance between each hatch mark on the vertical or horizontal axes. Although the axes do not need to have the same interval, the interval that is chosen for an axis must be consistent over the length of the axis. This means that if an interval of 5 units is used (e.g. 0, 5, 10, 15, 20) between two of the hatch marks, the interval can NOT be changed for a different part of the axis. The interval value chosen should be one that is fairly easy to count by, therefore, 1, 2, 3, 5 and multiples of 10 generally work well. The interval of the axis is largely determined by the range in the data being plotted. As a general rule or point of preference, 4 or 5 interval marks allow for ease of reading the graph. In order to determine the interval, divide the range by 4 or 5. For example: if the largest value is 62 and the smallest is 14, the range would be 62 – 14 = 48. 48 / 5 = 9.6. For the ease of counting, 10 may be an appropriate value for each interval.
Determining the interval for the graph is an important skill. Two basic conventions concerning the interval is that it is evenly spaced, and that there are typically only 4 and 6 interval marks. The 1st interval mark is “less than” the 1st data point (i.e. if graphing the ordered pair (6, 4) the 1st interval mark on the Xaxis could be any number less than 6), but for ease of counting the interval would probably be 1, 2, 3, 5, or 10 with the exact interval to be determined by the total range of data for that particular axis and end after the last data point. If the data for the yaxis ranged from 3 at the low end to 37 at the high end the total range would be 34 (37 – 3 = 34). In this example an appropriate interval might be 5 units. The 1st interval mark would be zero (0) on the scale and the last would be 40. Although in this example we have exceeded the suggested number of interval marks, five (5) is an easy number to count by, and thus makes this an appropriate interval. When drawing the graph, the entire space for the graph should be used, so the interval should be appropriate to maximize the use of available space.
Line Breaks: Used to show when the axes of the graph are not a continuous number line from origin to the final value. The scale on the axis is not required to start at the origin (0,0) and count continuously to the final interval value. It is important to note that if there is any portion of the axis range removed for ease of drawing the graph or plotting the data, the axis line MUST show a line break symbol “≠” indicating that a portion of the axis is not drawn.
The Origin: Graphs usually start at the origin (0,0) however it is not required. Based on the total range of data, the value of the last interval mark and the interval unit will all contribute to the decision to start the graph at the origin, or to start it at some value close to but less than the lowest number in the data range.
Bar Graphs
Line graphs are best used when the there is NOT a connection between each of the dependent variable’s data points. For example the data collected in Figure #4 was from a survey and consisted of such questions as your favorite movie, food and music for a given group of students. In this case there is my not be a relationship between these variables. As with data tables and line graphs there are no hard and fast rules, simply guidelines that should be followed when ever possible. One major difference between Bar Graphs and Line Graphs is that the dependent and independent variables can be graphed on either the X or the Y axis, depending on the desired effect. If the variables are plotted in the traditional manner, xaxis for the independent and yaxis for the dependent then the bars will have a vertical orientation. If the variables are reversed, the bars will have a horizontal orientation. The orientation of the bars can be used to better illustrate the data. A vertical orientation MAY be more appropriate when attempting to show price differences between items, or which variable was more popular than another. A horizontal orientation MAY be more appropriate when comparing such things as the difference between distances traveled, or times spent on a task. The strength of a bar graph is to get a quick idea of how the data is distributed, not necessarily to be able to determine the exact numerical value of any particular bar.
Steps to making a Bar Graph
1. Draw the X and Y Axis
When setting up a Bar Graph, the 1st step is to draw the X and Y axis. If we graph the sample data provided, Figure #4 “Sample Bar Graph Data”, the 5 favorite foods are the independent variable for this data set, and thus would be plotted on the xaxis. The xaxis is the horizontal and represents Independent Variable; the independent variable is the variable that is chosen by the experimenter. The yaxis is the vertical axis and represents Dependent Variable. In Figure #4, the dependent variable would be the number of respondents to each food type.
Data Table of 5 Favorite Foods in My 8th Grade Class 
Pizza 
Hamburgers 
Sushi 
Pop Tarts 
Broccoli 
14 
6 
27 
25 
24 
Figure 4. Sample Bar Graph Data, “5 Favorite foods in My 8th Grade Class”
2. Set the Scale of the Graph
The scale for the Xaxis will be the independent variable labels. In this case the food items; Pizza, Hamburgers, Sushi, Pop Tarts, and Broccoli would be the labels that would divide the xaxis. The yaxis scale would be set based on the number of data point or responses recorded for each of the independent variables. See Figure #4 (Sample Data for a Bar Graph) for the number of data points recorded for each of the independent variables. Generally, the scale for the yaxis extends from zero to one interval mark greater than the highest recorded number of respondents in the data collected. With the sample data provided, the greatest number of respondents, 27, stated they prefer Sushi as their favorite food. With the interval set at 5 per interval mark, the Yaxis must have a scale that extends from 0 to 30 in order to appropriately accommodate the data to be plotted.
3. Plot the data on the graph
Make a bar extending from the xaxis up to the appropriate height on the yaxis for the data corresponding to each of the independent variables. Make sure to double check the numbers to be plotted, to avoid a common mistake of misplotting. If the value in question splits an interval mark it may be necessary to estimate the exact height of the bar. Error in estimation can lead to “data creep” which can be common when the interval is fairly large compared to the how “tight” the data is. For example, Broccoli and Pop Tarts are tied for 2nd and 3rd most popular foods with 24 and 26 respondents, respectively.
4. A Descriptive Title
With all carefully constructed graphs and data tables, a descriptive title is essential to help eliminate possible confusion on the part of the reader. The title should provide the reader with a reasonable understanding of what the graph is portraying.
Figure 5. Sample Bar Graph, “5 Most Favorite Foods in My 8th Grade Science Class”
Histograms
Histograms are similar to the Bar Graph. “The histogram is a form of bar graph where the heights of the bars show the number of observations in an interval or group of numerical values. The intervals can be ten units wide, as they are here, or any other value the creator desires. The width of the interval will depend on how spread out the data is and how many data are present. There’s no hard and fast rule, but it’s best to pick something easy to read; ten is better then nine, five is better than six” (Take It to The MAT, April/May 2004, Elementary Edition). In a histogram the lengths of the bars is equivalent to the number of observations in each interval.
Batting Average for Players On the Team 
0.0  .999 
3 
.100  .199 
7 
.200  .299 
14 
.300  .399 
5 
.400  .499 
0 
.500+ 
1 
Figure 6. Sample Frequency Table for Constructing a Histogram
It is important to note in the sample data provided in Figure #6, that there is NO DATA for the 400499 range of data. When this occurs it is important that the graph accurately reflect that there was not data. In essence a bar with a height of ZERO (0) would be graphed. It is NOT appropriate to skip this data range as the axis scale goes from 0 – 500+ the lack of data in one interval needs to be reflected. It is as important to know where data is, as it is to know where the data IS NOT.
Figure 7. Sample Histogram, “Batting Averages for Team Members”
Pie Chart/Circle Graphs
Pie charts, also know as circle graphs are used when the data collect is to be displayed as a percentage. The pie chart is circular in nature with pieshaped wedges representing the percent of data with the greater the percent indicated by a larger slice. The full pie represents 100%, one half the pie is 50% and so forth. An effective and easy way for student use, and practice with pie charts is to practice with Dyna Zikestyle paper circles.
1. Starting with several different colored sheets of paper trace a moderately large circle (i.e. 10cm – 20cm in diameter) on the top sheet.
2. With the paper stacked, cut out the circle, taking precautions that the papers don’t slip.
3. Once the circles are cut out, cut the circles along a radius line (or fold) you have made on the circle.
4. Include as many circles/colors stacked together as the pie chart has wedges or variables to be graphed.
5. Interlock the circles by inserting each of the circles into the slit of the other circle.
6. Start spinning or turning the secondtotop most circle, while holding the top one, so it will not rotate.
As each consecutive circle is rotated, more or less of the top most circle is covered. Although the exact percentage of the pieshaped wedge will not be accurate, a close approximation will be easy to find. If the students have difficulty spinning the correct amount, a simple exercise of having the student start at 50% and then work their way up or down in percent until they get to the desired amount. It is fairly quick and easy if the students spin half the total remaining percentage at a time. For example, if the students need one wedge of the graph to be 14 percent. Start with a full circle. Spin in the 2nd circle to cover 50% of the graph. Once 50% is covered the students can split the remaining 50% in half and spin that in, so now 75% of the circle is the original color, the remaining 25% is now the 2nd color. We are still not at the desired 14%, so we split the remaining portion in half and spin that in. The wedge is now at 12.5% or so. To get the desired 14 % simply turn the piece just a smidge more to make that wedge slightly larger. At this point that wedge is very close to being accurate. Put one small drop of glue on the back of the top piece. This will hold the circles in place. At this point another circle can be spun in, to represent yet another pie shaped wedge.
Figure 8. Sample Pie Chart, “Land Ownership in Nevada”
Performance Benchmark N.12.A.1
Students know tables, charts, illustrations and graphs can be used in making arguments and claims in oral and written presentations. E/S
Common misconceptions associated with this benchmark.
1. Students commonly fail to follow conventions when constructing and using data tables. Some of the more simple mistakes consist of the student not taking enough time to analyze the experiment or survey and fully understand what data they will be collecting. This inadequate or improper planning can result in the data table being improperly drawn and labeled, thus forcing students to squeeze in extra column(s) or row(s) to accommodate the required data. Another common mistake is to not place the independent variable in the far lefthand column. Although there are not hard and fast rules, failure to follow this convention can lead to confusion when setting up the resulting graph.
For a brief explanation on how to make a date table see: http://findarticles.com/p/articles/mi_m1590/is_2_59/ai_99554833
2. Students make errors when constructing line graphs because they fail to follow conventions associated with labeling axes, determining scale, and write nondescriptive titles.
One of the most common mistake students make is to reverse the ordered pairs when graphing the data. The 1st number in an ordered pair is the independent variable and thus would be plotted on the xaxis. The second number in the ordered pair is the dependent variable and is plotted on the yaxis. This mistake can be fairly easily avoided IF the data table and graphing conventions are followed. If a student fails to follow those conventions, confusion is almost inevitable and mistakes will be made.
There are a slew of common mistakes when it comes to the actual drawing and labeling of the graph and its axes. The most common errors include;
1. Not setting a proper scale or interval. Students tend to choose intervals that are either to small for the data set (i.e. the data range is 35 or 40 units, and they set the interval to 1 or 2 units, thus making a graph that has 15, 20, or even more interval marks). Or making the interval to large for the data set (i.e. the data range is actually fairly tight say 10 – 15 units, but the numbers large, such as 105 – 120 units total). This mistakenly leads the students to start the graph at the origin, and set intervals that are too large (20, 40, or 50 units), in so doing the data that gets plotted is “smashed” together in such a way that it is very difficult to read the data points, and graph the data. One of the easiest solutions to the problem is to place a line break in the “offending” axis so that the graph can focus on the portion where the data is concentrated.
2. Axes accurately labeled. This mistake can consist of something as simple as not putting the units to the variable (i.e. Labeling the axis Time without one of the appropriate units such as: S, Min, Hour, Days, Years) or giving a generic and uninformative label such as “DATA” or “Dependent Variable.”
3. Uninformative title. The graph is titled in such a way that the reader has no clue as to what the graph is about. The classic example is “Line Graph” or simply “Graph.” One of the simplest solutions to this is to have the students practice writing titles for graphs that incorporate the independent and dependent variables so that the title is descriptive and to the point.
Graphs which are plotted to visually skew the results, lead to a false impression of the data. This is often done when two independent variables are being compared, and the results did not yield a substantial difference between the two independent variables being compared. Pie charts are particularly prone to this sort of false representation when a complete (100%) set of data is not graphed.
3. Students omit columns or rows in histograms if no data was collected for the value range.
Histograms suffer from all the common errors associated with bar graphs, but have one that is unique to them. This unique error consists of not leaving blank columns (or rows) for any data frequency of zero. This error leads to reader misinterpretation and can be easily avoided, if the students understand that the lack of data in one interval of data, is really a bar with a value of zero that still needs to be plotted.
4. Students misrepresent data in a pie/circle graph by inaccurately drawing wedges or failing to show data that equals 100%.
Along with not having an accurate or descriptive title or labels, circle graphs are prone to misrepresentation if they are accidentally or intentionally misdrawn. This usually occurs if the pie wedges do not add up to 100%, or if the percentages add up, but the actual size of each wedge is not draw to scale. Due diligence is a way to correct this issue. If using the paper cut out circles to generate the pie graph, some degree of error is inherent and may be overlooked, if the intent of the graph is to get the “big picture” and not the exact percentages.
Students may mistakenly hold the idea that the wedges or parts of Circle/Pie graphs do not need to equal to 100%. This form of graph is specifically designed to illustrate the percent to whole ratio of all the variables being compared.
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Performance Benchmark N.12.A.1
Students know tables, charts, illustrations and graphs can be used in making arguments and claims in oral and written presentations. E/S
Sample Test Questions
1st Item Specification: Given a choice of several graphs, select the one most appropriate to display a collection of data or to illustrate a concept or conclusion.
Depth of Knowledge Level 1
 Study the following data chart.
5 Favorite Foods in My 8th Grade Class 
Pizza 
Hamburgers 
Sushi 
Pop Tarts 
Broccoli 
14 
6 
27 
25 
24 
Which of the following graphs would best represent this data?
 Line graph
 Pie graph
 Scatter plot
 Bar graph
 Study the graph below.
Pie graphs are used for
 data that is presented as percentages.
 demonstrates the dependent variable.
 showing the frequency of variables.
 differentiating between variables.
Depth of Knowledge Level 2
 Use the chart to answer the following question.
The Effect of Drop Height on Bounce Height of a Rubber Ball 
Height of Drop (cm) 
Height of Bounce (cm) 
5 
4 
10 
6 
15 
11 
20 
13 
25 
16 
30 
21 
Which of the following graphs would best represent this data?
 Bar graph
 Pie graph
 Histogram
 Line graph
 Use the table below to answer the following question.
Distance vs Time
Time ( seconds ) 
Distance ( meters ) 
0 
0 
1 
2 
2 
8 
3 
18 
4 
32 
5 
50 
6 
72 
7 
98 
8 
128 
9 
162 
10 
200 
Select the graph that best represents the data and describes the relationship between the variables.
 Bar graph demonstrating acceleration.
 Line graph demonstrating velocity.
 Histogram demonstrating speed.
 Frequency chart demonstrating force.
2nd Item Specification: Interpret a graph, table or chart and analyze the data display to reveal information.
Depth of Knowledge Level 1
 Use the graph to answer the following question.
How many players on the team have a batting average between .300  .399?
 1
 5
 7
 9
 Use the graph to answer the following question.
If the same ball used in the experiment were to be dropped from a height of 30 cm what would you expect the approximate height of the bounce to be?
 20 cm
 30 cm
 38 cm
 44 cm
Depth of Knowledge Level 2
 Use the data table to answer the following question.
The Effect of Drop Height on Bounce Height of a Rubber Ball 
Height of Drop (cm) 
Height of Bounce (cm) 
Average Bounce Height for all
Trials (cm) 
Trial 1 
Trial 2 
Trial 3 
5 
4 
3 
2 
3 
10 
6 
6 
5 
5.6 
15 
11 
12 
11 
11.3 
20 
13 
14 
14 
13.7 
25 
16 
15 
16 
15.7 
30 
21 
20 
21 
20.7 
What is the relationship between the height of the ball dropped to the height of the bounce of the ball when the ball is dropped from 20 centimeters?
 The average bounce height is greater than the ball drop height.
 The average ball bounce height and the ball drop height are equal.
 The ball bounce height is approximately 70% as high as drop height.
 The average ball bounce height is 25% greater than the drop height.
 Use the data table to answer the following question.
The Effect of Drop Height on Bounce Height of a Rubber Ball 
Height of Drop (cm) 
Height of Bounce (cm) 
Average Bounce Height for all
Trials (cm) 
Trial 1 
Trial 2 
Trial 3 
5 
4 
3 
2 
3 
10 
6 
6 
5 
5.6 
15 
11 
12 
11 
11.3 
20 
13 
14 
14 
13.7 
25 
16 
15 
16 
15.7 
30 
21 
20 
21 
20.7 
Which is the independent variable in this experiment?
 Height of Bounce
 Height of Drop
 Average Bounce Height
 Height of Bounce and Drop
 Use the following graph to answer the question.
Who is the 3rd largest land owner in the state of Nevada?
 State Lands
 US Dept of Agriculture
 US Dept of Defense
 US Dept of Interior
3rd Item Specification: Predict (extrapolate and interpolate) from a data display.
Depth of Knowledge Level 2
 Use the graph to answer the following question.
If the same ball used in the experiment were to be dropped from a height of 40 cm, what would you expect the approximate height of the bounce to be?
 20 cm
 30 cm
 38 cm
 44 cm
 Use the graph to answer the following question.
At what time will the water temperature reach 16 degrees?
 4 seconds
 5 seconds
 6 seconds
 7 seconds
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Performance Benchmark
N.12.A.1
Students know tables, charts, illustrations and graphs can be used in making arguments and claims in oral and written presentations. E/S
Answers to Sample Test Questions
 D, DOK Level 1
 A, DOK Level 1
 D, DOK Level 2
 B, DOK Level 2
 B, DOK Level 1
 A, DOK Level 1
 C, DOK Level 2
 B, DOK Level 2
 B, DOK Level 2
 B, DOK Level 2
 C, DOK Level 2
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Performance Benchmark
N.12.A.1
Students know tables, charts, illustrations and graphs can be used in making arguments and claims in oral and written presentations. E/S
Intervention Strategies and Resources
The following is a list of intervention strategies and resources that will facilitate student understanding of this benchmark.
1. Kids Zone Learning with NCES: Create a Graph
The National Center for Education Statistics has an easytouse website in which students can create a variety of graphs. The site provides students with sample line, bar, area, xy, and pie graphs. It assists the students in a stepbystep process to construct a graph with their data.
To access the Create a Graph website, go to http://nces.ed.gov/nceskids/createagraph
2. Take It To the MAT Newsletters
A complete index of the TITTM Newsletters that address the finer points in Mathematics Education can be located at the website below. These newsletters are separated into grade bands (elementary, middle, high) and by the content areas addressed. Each newsletter is a one to two page article that focuses on some of the subtleties of math education. The section on Data Analysis and Probability is most applicable to this Science Benchmark.
To access the TITTM Newsletters go to: http://rpdp.net/math.php
A comprehensive packet on how to make and interpret charts, graphs and diagrams
This 4 page packet is designed to be a review of how to make and interpret charts, graphs and diagrams. Each section starts off with the “How to” portion followed by several interpretation questions of the graph or chart type in question.
To access this resource go to: http://rpdp.net/adm/show.php?type=science&lvl=High+School
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