Tips for Teaching with Pandem-Data

Pandem Data topics, such as Big Data and infectious disease modeling activities, may at first seem a bit daunting for you and for your students. Unless you regularly team-teach with a computer teacher or unless you do a lot of computer work as a hobby, you may not be familiar with Big Data or some of the concepts covered in this curriculum. You will most likely be much more comfortable with the infectious disease topics. The following tips will help you get your students started on investigations with Big Data and will help you easily implement the activities.

Introduce the topic as something your students probably know very little about but yet is one with which they have daily contact. Stress the significance of Big Data in their lives and let them know they will be using Big Data software to problem solve real-world disease simulations.

As future scientists, they will be working with Big Data and even if they are not future scientists, they will still be impacted by Big Data! Getting them off to an enthusiastic start will go a long way to overcoming any obstacles they encounter.

Invest some time in the beginning to explain exactly how large an influence Big Data is in their lives. The topic will become more significant to them and they will see that although it touches many aspects of their lives, they really know very little about it.

Describing the significance of Big Data will peak their interest and allow them to engage more fully with the subsequent lessons and activities.

Students must understand the disease process.

Students must understand the infectious disease process before they begin to model a disease outbreak using one of the programs in this curriculum package. Understanding what they are trying to model will help them to make the choices needed to build a simulation.

Teach the language.

Yes, you want to teach through critical thinking opportunities, and you will certainly do that with these materials, but your students will not be able to understand the topic if they don’t understand the science language you use to explain the concepts. A good bit of teaching and learning can occur through teaching the language.

For example, infectious means “ in a disease condition, likely to be transmitted to people and organisms through the environment”. But in the epidemiology of infectious disease, there is also infectious with symptoms, infectious without symptoms, and latent, just to name a few conditions which expand on “infectious”.

Ask students to give an example of each. They will probably easily name any illness that actually made them feel sick for “infectious with symptoms”, such as a cold, the flu, or strep throat. But they may struggle a bit with infectious without symptoms. Use Zika as an example as some people experience no symptoms, but are still infected with the virus. Another relevant example would be certain sexually transmitted diseases, such as HPV or chlamydia, in which most people do not experience symptoms, but can still pass the disease along to someone else.

A latent infection is one in which the virus can lie dormant within host cells and become active again in recurring episodes of symptomatic disease. The herpes virus is an example of an infection which students may recognize if they know someone that gets cold sores from time to time.

As they learn the language of infectious disease, the spread of infection will be more easily understood. The epidemiology of infectious disease will become something they can predict. And the next step—using Big Data to build a disease simulation and using software that visualizes the epidemic—becomes easier.

Take each topic within the unit one step at a time.

This is important to having your students build on knowledge—especially with the activities on building simulations using the disease modeling software. For example, have them build simulations in Excel before you have them use FRED or GLEAMviz. If your students have experience in Excel, you may not have to help them through this activity at all. Building a simulation in the GLEAMviz program is a bit more complicated, but the guidebooks that accompany the lesson plans and activities will help you through the process.  Again, one step at a time and they should be building simulations quickly!

Consider having students work in pairs.

Treat the modeling activities like you would any other lab situation when the students work in pairs to perform laboratory investigations. This will make them more comfortable with the experience and provide opportunities to problem-solve collaboratively. You will also be better able to manage the activity if they are partnered and know they will be expected to trouble-shoot problems and investigate issues before asking you for help.

Reassure students for success.

Reassure students that some Big Data topics or activities may seem difficult at first but they will be taking it one step at a time and breaking up the tasks. It won’t seem so difficult if approached in this way and they will have the chance to build on knowledge gained.