Using Project EDDIE modules in Global Challenges, Scientific Solutions: Climate Change
About this Course
Global Challenges, Scientific Solutions: Climate Change
EDDIE Module Developed
We found this case study to have an outsized impact on our students. The students reported feeling more confident with data, spreadsheets, and analysis, and were surprised with what they could do. They were also surprised by the biological findings, some events were happening earlier and some were not affected by slightly warmer temperatures.
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Relationship of EDDIE Module(s) to my Course
We used this module in multiple sections of an introductory-level majors courses (with some non-major students) and taught both online and in person. By the time we used this module, our course had covered the basics of climate change and reading figures, but the students had not worked with any datasets, manipulated any spreadsheets, nor created any figures. We used the module in the last month of the semester when we wanted to focus on the impact of climate change on biological systems. By this point in the semester the students were able to work more independently on activities and were willing to ask for help from instructors or classmates when they needed it.
Teaching Details
What key suggestions would you give to a colleague before they used the activity in their teaching?
We would suggest assigning the pre-homework so students become familiar with the datasets and manipulating them before class. Those who completed the pre-homework were ready to jump into the class activity. We would also suggest using the same software system for all students, if possible, as it reduces the diversity of questions about technology and frees up more time to talk about data and phenology. Allowing students to work at their own pace is also key. Confident students will work ahead and can then act as peer helpers during the activity.
How did you address challenges in teaching with the module?
We ran the first iteration of this module the week after we went virtual for the first time (spring 2020). We were amazed at how well the students did overall. There was some trouble shooting with different versions of Excel and Google Sheets, but with shared screens and shared google drive documents we figured it out. We taught both online and in person the following semesters and found the technology remained the biggest hurdle. The activity went well once students were in the desktop versions of Excel and now have access to the XLMiner ToolPak in Google Sheets.
Student Outcomes
The module gave the students a good opportunity to manipulate data on their own and interpret "messy" data. They had to think about trends rather than individual data points, evaluate whether their predictions matched the actual data (and why it might be different), interpret what the inferential statistics meant, and draw a conclusion. Most were able to do this well after working through the activity.
We were impressed at how much confidence the students gained from this one activity. Although this was the only data manipulation and analysis activity they did, they self-reported major gains in data analysis ability and were surprised at how they could now manipulate data and do their own analysis.