Final imagineit report. May 3, 2016
I have to start this reflection by mentioning that two days after the last update, my daughter Mae Eileen was born. She is healthy and perfect...and like any newborn, a lot of work! I took a month of paternity leave. As a result, it has been difficult to move the ImagineIT project forward. I do plan on continuing with some of the plan but with the school year winding down it may be impossible to complete the project as it was originally designed. Sadly the presentations may have to be sacrificed.
Looking back, I can see how design in education is tricky due to the time restraints and conflicting responsibilities. We come up with plans in the summer and once the school year starts things come up, days are poached for field trips, topics take longer to cover, babies happen, etc. Then it’s May and you have to drop key aspects of the original plan. It’s easy to be disappointed, but at the same time, I am proud of the accomplishments. I have taken time to talk with students and a Math colleague. The math teacher and I discussed chisquared tests being our next collaborative effort. When I mentioned that there should be more teamwork between Math and Science he was positive. Both of us are rookies with these advanced IB courses. We are trying figure out so many required activities that any chance for teamwork is embraced. The students appreciated the efforts to coordinate. They said they liked how we taught the same ideas in two ways. Something I’ve learned about teaching is to put the math concepts at the center of lab practicals. This project has helped me see how important the underlying math skills and concepts are to science  especially when we look at the nature of science and inquiry. Conclusions need to be grounded in data. It is such an obvious thing, but it must be reflected in the lab activities. Students have to understand the math, do the math and then connect the math back to the question that the lab sets out to answer. 
spring update #2 march 4, 2016
The project is moving forward nicely. Students in my 11th grade IB biology class have completed their chromatography experiment on green and red lettuce. Five or six groups in each class collected data, calculated Rf values, shared results, and completed ttest calculations to determine if mean Rf values of the pigments in green and red are statistically different or the same. The results that they found looked something like this:
Table 5: Ttest data
Chromatogram Band Number ttest value (P) Is null hypothesis supported or rejected?
1 .563521 Supported
2 .456973 Supported
3 .984887 Supported
4 .811679 Supported
(note: data copied from one of my students submitted lab reports)
As you can see, the null hypotheses were all supported, clearly indicating that, as one of my student’s reported in his conclusion, “...the relationship between the pigments found in the red and green lettuce is that they are practically the same, the null hypothesis is true, and that there is no difference in the pigments.”
Reflecting on this lab, I am thrilled with the my students’ demonstration of both their lab and excel skills. But more important than these technical skills, I am thrilled with the number of students who conceptually grasp the importance of the statistics in this process. During the lab there were many times where I overheard students helping their peers understand some statistical concepts like the null hypothesis or statistical significance. These calculations can be very intimidating for students and are often poorly understood even if they are correctly done. By collaborating with the math teacher to work simultaneously on these skills, we have been able to give students a better chance of understanding the principles behind these calculations.
Moving forward I am going to tackle chisquared tests in much the same manner. Keep an eye on the twitter feed for updates!
Table 5: Ttest data
Chromatogram Band Number ttest value (P) Is null hypothesis supported or rejected?
1 .563521 Supported
2 .456973 Supported
3 .984887 Supported
4 .811679 Supported
(note: data copied from one of my students submitted lab reports)
As you can see, the null hypotheses were all supported, clearly indicating that, as one of my student’s reported in his conclusion, “...the relationship between the pigments found in the red and green lettuce is that they are practically the same, the null hypothesis is true, and that there is no difference in the pigments.”
Reflecting on this lab, I am thrilled with the my students’ demonstration of both their lab and excel skills. But more important than these technical skills, I am thrilled with the number of students who conceptually grasp the importance of the statistics in this process. During the lab there were many times where I overheard students helping their peers understand some statistical concepts like the null hypothesis or statistical significance. These calculations can be very intimidating for students and are often poorly understood even if they are correctly done. By collaborating with the math teacher to work simultaneously on these skills, we have been able to give students a better chance of understanding the principles behind these calculations.
Moving forward I am going to tackle chisquared tests in much the same manner. Keep an eye on the twitter feed for updates!
Spring update # 1  Feb 16, 2016My IMAGINEIT project is getting real traction as the second semester takes off. Last week I met with the students math teacher and we hashed out some plans to coordinate instruction for the next few weeks. Our first statistical challenge will be the ttest. In Math class, students will practice the calculations involved in the ttest while in biology, we will be using this tool in our lab practical. The plan is as follows:
Right now we are covering photosynthesis and the science of chromatography. Students have learned the principles of how different pigments can be separated out from a sample of plant pigments using solvent and filter paper. They have also learned that a simple calculation can yield a Rf values for each pigment in a sample. Rf values are like signatures for different chemicals. In this way, chromatography is an important technique for chemists trying to identify the makeup of unknown samples. Here's where the stats come in: Will two different breeds of lettuce produce chromatography results with the same Rf values? If they do, then both breeds contain the exact same chemical pigments, if not, then each has it's own unique chemical pigments. It's also possible that some pigments will be in both, but other pigments might be unique to one breed. But how can we test this? What if the results are different, but kinda close? What will give us the confidence to make any conclusions? To find out, students will run chromatography tests on green and red lettuce and calculate the Rf values for each pigment found in the lettuce samples (plants have multiple pigments). Then they will combine the results from the whole class and compare the results of the green lettuce to the results of the red lettuce. Since it is impossible to expect each group to find exactly the same Rf values, we need to determine if the differences between the pigments found in the red and the green lettuce are the result of random variation or is the difference more meaningful. Well, that is exactly what the ttest tells us! Keep an eye on the twitter feed over the next couple of weeks for pics and updates on our progress! 
