“Make it matter” for students by experimenting with new and interesting topics for assignments and projects, and by using varied examples in your lectures and other materials. Students are more likely to persist in the face of a challenge when what they are learning is relevant to their life experiences and goals. Use examples that have broad appeal, place assignments in contexts that interest students, and explain how a particular idea is used in different contexts.

Some suggestions

Don’t assume what’s meaningful; find out! Don’t rely on your notion of what’s interesting and meaningful, and certainly don’t rely on stereotypes. Find out from your students--and from the students you want to recruit--what is meaningful to them! Surveys and clicker polls are a great tools for this.

Keep keeping it real. Don’t relegate the discussion of larger context to the beginning of a course. Keep bringing students back to the real world application of what they are learning. This can be as simple as showing how a concept is used in a familiar application or program (e.g., how hash maps are used in natural language processing to predict what a user will type into a search engine).

Highlight the people. To help students see the people behind the concepts, refer to the contributions of an individual or group. A great story is Grace Hopper and her team at the US Navy coining the phrase, "computer bug.”

Examples from the collection

File I/O - Benford's Law

In this lab, students experiment with input and output files using real-world population data to see if they follow Benford's Law. At the end of the lab, students are asked discussion questions which help to extend their thinking.

Engagement Excellence

Structs with application in Ecology- Bears!

In this activity, students work through an extended problem applying data structures to ecology. Students begin by defining data structures to define characteristics about different types of bears, write templates for functions over these data structures, and then write functions that take in the data structure constructed. This activity is excellent for students learning to construct and use new data structures in Scheme.

Engagement Excellence

K-means clustering

In this activity, students use hierarchical clustering and k-means clustering to find clusters of similar genes, which can be used to predict genes that can affect certain cancers. Students use a priority queue to find close pairs of objects to use in clustering, and then use other data structures to perform the algorithm. This assignment is excellent for students that would appreciate synthesizing several data structures with a non-trivial algorithm with real-world applications.

Engagement Excellence