I was made aware of using “jigsaws” as an effective active learning strategy at a Preparing Future Faculty class I took at the University of Minnesota (my alma mater). The jigsaw is a collaborative learning activity where students learn a topic of interest by splitting the task. The topic or class module is split amongst students, so that a set of students are responsible for a particular sub-topic. The students are expected to read, and study the sub-topic at sufficient depth so as to call themselves as experts of the particular topic. During class students first convene in expert groups, discuss the sub-topic amongst themselves. After strengthening their ideas about the particular topic students are asked to gather in mixed groups such that every group has at least one experts from all the sub-topics. Collectively each mixed group talk about the entire topic, discussing and learning from others.

I have tested this activity in two classes that I have been instructor for, and have found sufficient enthusiasm amongst the student population to pursue it next time as well. I introduced students to five multiple sequence alignment algorithms  in a bioinformatics class taught at University of Minnesota, and five clustering algorithms in a data mining class taught at George Mason University. The main advantage foreseen was that students gained information about five different methods by reading only one of the research papers. Here is an example of this activity for the clustering algorithms.

The author of this post, Huzefa Rangwala is an assistant professor Computer Science Department, George Mason University.