Table 1. Learning goals for QMBC.
| Thinking |
| Students will be able to |
| - recognize situations that call for computational methods |
| - conceptualize a problem so it becomes amenable to computational solution |
| - use simulations to build intuition about biological systems |
| - compare the outcome of simulations to real-world data |
| - formulate and test hypotheses |
| - understand a project as a collection of smaller parts |
| - plan steps needed to solve a problem |
| - think of ways to test the validity of a computational approach |
| Doing |
| Students will be able to |
| - import large datasets into MATLAB |
| - parse such datasets into appropriate computational structures |
| - visualize a dataset in multiple ways |
| - compute summary statistics |
| - use elements of programming to implement problem-solving strategies |
| - use trial and error to design a computational approach |
| - read and understand MATLAB documentation |
| - read and understand someone else’s code |
| - find and fix errors in a piece of code |
| - write a program to automatize data analysis |
| - document their code and use programming style in naming variables |
| Feeling |
| Students will |
| - appreciate the value of computational and quantitative approaches |
| - feel confident about approaching and solving a computational problem |
| - persevere when they find a problem difficult or do not immediately understand it |
| - recognize that successful coding can be fun as well as useful |
| - know when to ask for help and where to find support when needed |
| - be willing and ready to learn more |
| - evaluate the quality of computational and quantitative methods in scientific studies |
| - influence the work of others by setting examples of good practice in this domain |
Learning goals for QMBC, categorized into the three domains of thinking, doing, and feeling.