We all have data in almost every class we teach, and CODAP is a versatile (if somewhat confusing) little program that lets us investigate it all in a fun and friendly way. Science teachers can explore population data or use the map function to look at climate change impact across the world. Math teachers will appreciate the ease with which students can explore linear relations and how they can use the equation capabilities to explore a variety of mathematical concepts. Even history teachers can look at the relationships between historical events and their effects on the world during that time period. As long as you have data or equations you can use CODAP to investigate them.Continue reading Show less
Similar to other spreadsheet/graphing programs, CODAP allows students to create datasets and investigate relationships by dragging different "cases" and "attributes" to each axis on a graph. CODAP provides some sample sets to get started and introduces users to what type of investigations are possible. Teachers or students can input their own data collected from experiments in real-world settings or import data from Google Drive or a local file, making it much easier to share a single set of data with the whole class.
Users can quickly change the data being compared by simply clicking and dragging the headings to the desired axis. There's also the ability to include sliders that let students dynamically change the data to see what effect it has on what they're studying. Students can export their images and graphs to include in projects and presentations for assessment, and CODAP files can be stored locally or saved directly to Google Drive for sharing and collaboration.
Overall, CODAP is a very interesting program that can make learning dynamic if students are given the right questions and problems to solve; with carefully guided questions and varied datasets, students can have a lot of fun exploring data. As a bonus, the sample datasets provided are great for getting ideas flowing on what's possible. CODAP also promotes digital literacy and encourages students to look at data in a number of different ways. If you have data you want to explore, students could gain a lot out of this unique and engaging tool.
That being said, as a program on its own, CODAP can be frustrating for students: They may get lost and overwhelmed with the number of variations and comparisons available to them. The interface itself can also be cluttered at times, especially if you're on a smaller screen like a laptop or tablet (even cutting off required buttons and functions). The way the data is organized into "cases" and "attributes" can also be a bit confusing as can the way tables are broken down into "parent" and "child" datasets. There were a few issues with selecting and highlighting specific data -- and then switching the selections -- as well. When a graph is initially created, random data is selected and provided in an unlabeled and unorganized scatter plot, which may be confusing for students until they get used to it. This means that CODAP relies a lot on the teacher to create authentic learning experiences and provide support for students.
Key Standards Supported
Create equations in two or more variables to represent relationships between quantities; graph equations on coordinate axes with labels and scales.
Represent constraints by equations or inequalities, and by systems of equations and/or inequalities, and interpret solutions as viable or non- viable options in a modeling context. For example, represent inequalities describing nutritional and cost constraints on combinations of different foods.
Expressions And Equations
Use variables to represent two quantities in a real-world problem that change in relationship to one another; write an equation to express one quantity, thought of as the dependent variable, in terms of the other quantity, thought of as the independent variable. Analyze the relationship between the dependent and independent variables using graphs and tables, and relate these to the equation. For example, in a problem involving motion at constant speed, list and graph ordered pairs of distances and times, and write the equation d = 65t to represent the relationship between distance and time.
Interpreting Categorical And Quantitative Data
Represent data with plots on the real number line (dot plots, histograms, and box plots).
Use statistics appropriate to the shape of the data distribution to compare center (median, mean) and spread (interquartile range, standard deviation) of two or more different data sets.
Interpret differences in shape, center, and spread in the context of the data sets, accounting for possible effects of extreme data points (outliers).
Use the mean and standard deviation of a data set to fit it to a normal distribution and to estimate population percentages. Recognize that there are data sets for which such a procedure is not appropriate. Use calculators, spreadsheets, and tables to estimate areas under the normal curve.
Summarize categorical data for two categories in two-way frequency tables. Interpret relative frequencies in the context of the data (including joint, marginal, and conditional relative frequencies). Recognize possible associations and trends in the data.
Represent data on two quantitative variables on a scatter plot, and describe how the variables are related.
Fit a function to the data; use functions fitted to data to solve problems in the context of the data. Use given functions or choose a function suggested by the context. Emphasize linear, quadratic, and exponential models.
Informally assess the fit of a function by plotting and analyzing residuals.
Fit a linear function for a scatter plot that suggests a linear association.
Interpret the slope (rate of change) and the intercept (constant term) of a linear model in the context of the data.
Compute (using technology) and interpret the correlation coefficient of a linear fit.
Distinguish between correlation and causation.
Using Probability To Make Decisions
(+) Define a random variable for a quantity of interest by assigning a numerical value to each event in a sample space; graph the corresponding probability distribution using the same graphical displays as for data distributions.
(+) Calculate the expected value of a random variable; interpret it as the mean of the probability distribution.
(+) Develop a probability distribution for a random variable defined for a sample space in which theoretical probabilities can be calculated; find the expected value. For example, find the theoretical probability distribution for the number of correct answers obtained by guessing on
(+) Develop a probability distribution for a random variable defined for a sample space in which probabilities are assigned empirically; find the expected value. For example, find a current data distribution on the number of TV sets per household in the United States, and calculate the expected number of sets per household. How many TV sets would you expect to find in 100 randomly selected households?
(+) Weigh the possible outcomes of a decision by assigning probabilities to payoff values and finding expected values.
Find the expected payoff for a game of chance. For example, find the expected winnings from a state lottery ticket or a game at a fast- food restaurant.
Evaluate and compare strategies on the basis of expected values. For example, compare a high-deductible versus a low-deductible automobile insurance policy using various, but reasonable, chances of having a minor or a major accident.
(+) Use probabilities to make fair decisions (e.g., drawing by lots, using a random number generator).
(+) Analyze decisions and strategies using probability concepts (e.g., product testing, medical testing, pulling a hockey goalie at the end of a game).
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