Professor Anuj Kumar scales down data sets to give students a hands-on understanding of complex systems, which they piece together in a group activity.
Professor of Molecular, Cellular, and Developmental Biology, University of Michigan in Ann Arbor
Postdoctoral Fellow of the American Cancer Society, Leslie Warner Fellow in cancer research, PhD in Biomedical Sciences, BS in Biology
There is a line on Professor Anuj Kumar’s website that captures his personality perfectly: “You can’t spell ‘fungus’ without ‘F-U-N’!” As a professor of systems biology at the University of Michigan, Ann Arbor, Kumar likes to bring some fun to his teaching, too.
This was no small feat in his Genomics Biology course. “Genomic data requires a lot of background in manipulating data sets, which most biology students don’t have,” he says. “I thought, ‘If I can give students data sets in small groups, hopefully they can put them together and see something that wasn’t apparent from each of the data sets in isolation.’” That, he figured, would simplify the activity. As it turns out, this also made it fun.
“From the first time I used this approach, I was struck by how excited the students were to do it,” says Kumar. “They were genuinely engaged as they put together the data and interpreted it.”
He randomly assigns students to groups of five or six, where they interpret a sample data set describing a hypothetical signaling pathway. Students are only able to reconstruct the full pathway by integrating the data sets he gives them in a true systems biology approach. After a brief whole-class discussion, Kumar shows students the integrated pathway and how they could get to that result from integrating the data set, but not from any one data set alone.
The technique is a modified version of the Jigsaw Method, where students work in small groups to solve sections of a “puzzle,” then reunite to work together on the big picture. The collaborative approach enables students to help each other, gain confidence—and have an enjoyable learning experience. “Students like to solve puzzles,” Kumar says.
Below, Kumar shares some details on how the whole project plays out.
Going beyond the “how” and “why” in a complex subject
Kumar wanted a way to teach systems biology beyond just telling students how it works and why it is important. But many students did not have the background in data analysis necessary to analyze and meld information from multiple data sets.
Using a jigsaw approach to make complex puzzles solvable
To break the work into doable chunks, Kumar uses the Jigsaw Method. He divides the class into small groups and gives each an individual data set to manipulate. Then they assimilate their findings and, as a class, work to draw conclusions that they could not see when looking at one data set alone.
See resources shared by Anuj Kumar, PhDSee materials
“From the first time I used this approach, I was struck by how excited the students were to do it. They were genuinely engaged as they put together the data and interpreted it as a pathway.”— Anuj Kumar, PhD
Course: MCDB 408 Genomic Biology
Course description: This course will introduce students to the methods and research topics encompassed within the discipline of genomics. Students will investigate the methods used to sequence genomes as well as methods currently being developed to analyze gene and protein function on a large scale across a diverse spectrum of eukaryotes. Ultimately, this course will invest students with an understanding of the realistic promise and limitations in large-scale biology relating to a broad cross-section of the eukaryotic kingdom.
Kumar’s “Genomic Jigsaw” approach for systems biology
In Kumar’s Genomic Jigsaw exercise, students must reconstruct a cellular pathway using a modified version of the jigsaw learning method. Simply put, students break into small groups, each receiving a different data set to interpret. Each group presents its findings to the class, and then the class as a whole works together to develop an interpretation of the pathway (for example, a set of proteins that act together to enable a particular function in an organism.)
“Systems biology becomes concrete when students can see that they’re interpreting the data as fully as they can [in their small group], but a slightly different picture [emerges] when they look at the other data sets as well,” Kumar says.
Kumar wanted students to see that the conclusions they can draw by integrating the data from multiple sets will be more enlightening than the findings from any one set of data. By figuring this out through the Jigsaw Method, students were able to grasp this insight on their own.
Here are his tips for setting up a similar activity:
Ease into collaboration by having students answer lecture questions in pairs
“For each class, at the start, I give a brief 20-minute lecture, and I usually pose questions to the class that they can work on in groups or pairs,” Kumar says. “This helps the students keep up with the material better, but also get to know each other.”
Provide students with context on the individual pieces of the puzzle
Instead of letting students begin this activity totally cold, Kumar believes it is important that they have some prior experience that can guide them. “[Before the main activity] I give them a mini data set to look at. We spend time in class discussing the method for each of the techniques used to collect data and the kind of data you’d generate by using it,” Kumar says. This way, students are up to speed on the background, so they can focus on integrating the results when the time comes.
Work with real data that you know well so you can simplify it
Kumar suggests that educators work with real data they already know well. “I worked backward with some of the data I knew for a biological pathway. I took some of what I knew about these genes and pathways and then went backwards and said, ‘If we were to use this type of approach, what result would we get from this pathway?’” Kumar explains. “I tried to keep the data sets fairly realistic in that they’re based on real genes and proteins, but I simplified them, so I don’t have to spend the semester explaining the subtleties of each protein, and they could still understand the data,” he says. According to Kumar, using real data sets increases student excitement and interest, too.
Assess students on the spot and again in a culminating quiz
“I usually go from group to group to help students out as they work on the data sets,” Kumar says. “Then I ask one member from each group to put up their interpretation of each data set on a whiteboard.” This activity helps him gauge students’ understanding but is not graded.
“Usually at the start of the next class period, I will have a [graded] quiz that would have something to do with the overall point of the lesson,” he adds. “I might have students interpret some of the particular data sets from the class as homework, then give them a mini sample and ask them what the result would be.”
Overall, Kumar thinks that this approach is more fun for students, and he has seen that it helps the point of the lesson truly stick. In fact, he is so passionate about this approach that he published an article on it, “Teaching Systems Biology: An Active-Learning Approach,” in Cell Biology Education.
“With this approach, because they’re working with the data, they’re more involved with it,” he says. “As a result, the message about ‘what systems biology can do’ becomes concrete.”