Sunday, 10 August 2014

Experimental data vs theoretical predictions - a case of cognitive dissonance

Biology practical investigation changes my thinking about teaching science
I was teaching my VCE biology class on Friday and we were doing some practical work investigating optimum pH range for enzymes, as we have been learning about cellular structure, components and processes (FYI - we switch units 1 and 2 around to match up with unit 3 for year 12 for those who were concerned!).

The practical activity we were doing consisted of measuring the production of oxygen gas, as catalase, an enzyme present in liver tissue decomposed hydrogen peroxide:

H2O2(l) --> H2O(l) + O2(g)

The practical is meant to demonstrate that at a certain level of acidity (around neutral pH) the enzyme is most effective and produces the greatest amount of oxygen gas over the timeframe measured. When we graph the results (oxygen produced vs pH) we would expect to see a bump around neutral pH that represents that optimum range, i.e. most effective catalysis for our enzyme.

There are two VCE biology classes, and the other biology teacher had told me at lunchtime that her class results were quite inconsistent and the resultant curve they found was almost exactly the opposite of what is predicted by the theory and the literature. In addition she mentioned that the neutral pH buffer used gave the lowest result for oxygen produced (counter intuitively - it should be the highest, showing that the enzyme is adapted for use in a biological setting, i.e. the body). I began to think that the buffer might be suspect, so I ran the experiment alongside my students, and used plain water in place of  the buffer solution (to approximate neutral).

Data handling and sources of error
My results gave me the expected curve for optimum range for an enzyme, but I found that all my students had results that were incredibly inaccurate. I found this quite strange, but was able to turn the class into a discussion of experimental design, data handling and how our expectations can influence the results when we are not careful. I think the students found it quite powerful as well - I freehand sketched all their results on the board immediately (quicker than computer graphing in real time - although I have attached the graphs below) and we were able to discuss all the results. This, in conjunction with some recent student response to practical work (illogical conclusions, conflation of cause and effect, inaccuracies in handling data) has really made me think about what is important in teaching science.

There is a huge range of variation in results! I have not labelled the axes; vertical axis is cm of oxygen produced and horizontal is pH.

How important is visual/data literacy?
I have having a conversation on LinkedIn recently, after posting to ask about the essential elements of science pedagogy, and a recent question brought up in the discussion was around the importance of visual literacy. It really got me thinking about what students do as data interpretation. I have found that through my own science education, I have developed strong data analysis and interpreting skills. Many of my students do not have a particularly high level of ability in this skill.

In the case of this practical, had I not also conducted an accurate investigation, students would be left with unsatisfactory results and a theoretical concept that did not match their findings - how often does this happen with student experimentation? What conclusions would they draw from this? Would they try and fudge the results? Become confused? Find science frustrating?

I found it really powerful to be able to link the important practices of science such as being accurate in the lab and looking at experimental data and interpreting it, and it has prompted my thinking about what skills need to be taught in the science class. It makes feel that having these kind of experiences can be important formative experiences for science students. They get to create data and then critically evaluate whether or not it is accurate, and reflect on why it might be different. It also makes me realise how many of the skills I take for granted (representing data graphically, choosing the best representation, being aware of the limitations of data) I need to formally model and teach if I am to get the most out of my students. This is also making me think about critical thinking and other higher order thinking skills, but I will save that for other posts.

What elements of science that you take for granted are you not teaching your students explicitly? OR what elements have you come to realise that you do need to explicitly teach?