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Approaches to Teaching Data Analysis
The way in which instructors expose students to data and data analysis depends on the style of experience they want students to have. Instructors can vary how students engage with data, the strategies students use to collect of analyze data, and the types of data used. These strategies, developed and described in Pedagogy in Action, are summarized below:
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Student engagement with data
Examples from workshop participants:
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Data Analysis Challenges (and Solutions)
Teaching students proper coding and computational thinking skills
- Start with fully functioning code; use to illustrate and discuss
- Introduce well commented "skeleton code" and have students fill in blanks
- Code interpretation exercise: provide code similar to one students have seen and have them add detailed comments
- Write framework of comments intended to guide code writing, have instructor check it, and then have students write it
- Write a complete code based on a specific question
- Example: Modeling an Neuron Action Potential in Matlab by Marjorie Hubbard (North Carolina School of Science and Math). In this activity students use a "skeleton code" to complete a model and investigation of neuron action potential.
Assessing what makes an effective data visualization
- Print out and critique plots for effectiveness in a gallery walk or "Rogues" gallery walk (series of plots, with purposeful mistakes)
- Make a range of plot types and styles and have students discuss which ones are relevant to the problem
- Use real data appropriate to the problem, bonus for societally relevant hook
- Guide student interpretation with examples and interpretive questions
- Example: Gravity prospecting by James Conder (Southern Illinois University - Carbondale). Students are given a set of gravity data with the aim of finding and visualizing high density anomalies in the subsurface.
- Example: Data Analysis Activity Using MATLAB by Michael Ray (California State University-Sacramento). Students perform an experiment, collect the data, analyze the data, and produce a high quality graph that is used to show the results of their experiment.
- Teach referencing by manipulating/modifying large datasets (e.g. make a black stripe on an image in a given area)
- Demonstrate efficiencies of different methods by measuring run-time of for-loop indexing vs. indexing (via tic and toc)
- Example: Solution of an Equation by Using MATLAB by Mahmud Akelbek (Weber State University). Students construct computer code to find he solution of an eqaution and test their results for different problems.
- Give students exercises to write low level text read functions
- Teach students how to pre-condition data before use by MATLAB to make it readable by the built-in functions, or supply preconditioned data
- Student exercises can address methods of ingesting different file formats
- Example: Monitoring Algal Blooms with Landsat (OLI) by Andrew Fischer (University of Tasmania). Students utilize MATLAB to access, process and extract data from a Landsat 8 OLI remote sensing data to investigate the cause an management of algal blooms.
Resources for Developing Students' Data Analysis Skills
- Teaching with Data from Pedagogy in Action
- Teaching with Data Simulations from Pedagogy in Action
- Teaching with Models
- Teaching with Data, Simulations, and Models
- Join the Teaching Computation in the Sciences Using MATLAB community to discuss ideas and ask experts questions