Using the Project EDDIE Nutrient Monitoring in the Chesapeake Bay module in Foundations in Biology I - Biodiversity and Organismal Systems
About this Course
Foundations in Biology I - Biodiversity and Organismal Systems
EDDIE Module Developed
The dwindling seafood population in the Chesapeake Bay is connected to the increasing level of pollution such as high levels of nutrient inflow.
Efforts are now being geared towards the cleanup as well as the monitoring of the inflow to ensure adherence to standards.
This module is developed to create awareness in the scholars' population of the need to monitor and control permissible levels of nutrients that are allowed into the waters without causing any harm.
Jump to: Course Context | Teaching Details | Student Outcomes
Relationship of EDDIE Module(s) to my Course
This course covers, among other topics, an introduction to biology with quantitative scientific inquiry into organismal diversity, ecological interactions and ecosystem dynamics.
After discussing the topics of population ecology, ecosystems and energy, the topics of ecosystem dynamics and water (and nutrient) cycle follow. Nutrient pollution of the waters is mentioned and the activities of the module are involved.
One of the learning objectives of the course is for students to be able to understand, discuss and list the biological and geochemical processes that cycle nutrients and water in the ecosystems.
Teaching Details
What key suggestions would you give to a colleague before they used the activity in their teaching?
- It is important to start the discussion with water and nutrient cycling, nutrient pollution of waters, the permissible total maximum daily load (TMDL) of nutrients in waters.
- Check out the chesapeakebay.net website to see the available array of data that spatially and temporally collected for monitoring.
- Decide what dataset to use for teaching.
- Be sure that students are able to use Excel or teach them how to.
How did you address challenges in teaching with the module?
There was no major challenge while teaching the module. One of the minor challenge encountered was with students navigating to the data repository section of the website which has a separate address (link) different from the main site. This was however resolved by leading students to the "DataHub."
Another challenge that was resolved was making the selection of appropriate dataset to download and analyze, this was due to the large amount of dataset being collected on several parameters such as dissolved oxygen, total dissolved oxygen, carbon dioxide, dissolved and particulate nutrients etc. Making a choice dataset will depend on the matrix of interacting and affective parameters (or lack of it) that the instructor may want to teach.
Lastly, the use of Excel spreadsheet and what variables, dependent or independent, go on the y- and x- axes of the graph. Experimenting with these variables and the different types of plots may help in making choices. Some variables may interchangeably be considered dependent or independent depending on their interaction with others.
Student Outcomes
For some students who didn't know how to use Excel Spreadsheet (especially for basic mathematical calculations and applications), the experience became an eye opener.
For the majority of the students, they gained the awareness that quantitative water quality (and indeed environmental) data is being collected on daily basis, and that such data could be analyzed and interpreted to showcase real-world challenges that may be resolved with disciplinary or interdisciplinary approach.
Students have now come to the realization that data driven information could be more reliable, and have become knowledgeable about the need to collect data for decision making. Also, that data collection is possible in almost all of life situations.
Further, students learned that data need to be analyzed, after which there's need to follow where the data leads. Thus, students are now willing not only to work with data but to learn data collection methods as well as go a step further into learning computer programming that may aid data analysis and interpretation, if required.