Topics 11 & 21
11 ( 10 h) & 21 ( 2 h) : Measurement, Data processing & Analysis
Background
Topic 11 - Measurement and data processing - is really two completely separate topics under the one heading. The first two sub-topics deal with error and uncertainties (including graphing techniques) and the third sub-topic is concerned with spectroscopy. Topic 21 - Measurement and analysis is additional higher level material mainly on the identification of compounds and 1H NMR spectroscopy splitting patterns. Some teachers are of the view that the first two sub-topics on Measurement and data processing are an important part of the chemistry syllabus, others question why they are on the syllabus at all. In fact they were not on any of the past syllabuses prior to the previous one which started in 2007 for first examinations in 2009. They were put on the syllabus as students were being asked to calculate and comment on their uncertainties and errors in their practical write-ups. Since these were being assessed some teachers claimed that it was unfair to assess them on material that was not on the syllabus. The third sub-topic 11.3 - Spectroscopic identification of organic compounds - is unrelated to the first two and is the reappearance of core coverage of spectroscopy which had been left off the 2007 programme - a fact that was lamented by many teachers. Since there are now ten hours of teaching for Topic 11 (and a further two hours for Topic 21) there will now be four multiple choice questions at Standard Level and five at Higher Level which is a big increase on the single multiple choice question at both SL and HL on this topic in the last programme. The measurement and data part of the topic will be further examined in Papers 2 and 3 and in the Individual Scientific Investigation so effectively measurement and data processing 'punches above its weight' and is probably assessed more than its allotted 4 hours teaching deserves.
Validity
Should measurement and data processing be on the syllabus? Personally I think that the chemistry syllabus should reflect real chemistry as much as possible rather than the sometimes arid and often simplified 'school' chemistry with little meaning to the real world. If you are working in CERN and measuring the velocity of neutrinos and claiming that you have found that they travel faster than the speed of light then clearly the uncertainties are of paramount importance (see blog on E = m(c + a little bit)2. However, in a school where we use relatively simple but quite accurate equipment such as pipettes, burettes and an analytical balance (see image on right) are the uncertainties associated with them really relevant? Probably the only common measuring instrument we use when doing accurate work which does have a high degree of uncertainty is the simple thermometer - particularly when two readings are taken to measure a temperature difference which may only be accurate to ± 1 oC. For a temperature difference of 10 oC this equates to a ten per cent uncertainty. If you look at scientific papers in chemistry the error or uncertainty associated with results is often not given. Similarly virtually all data books, which often give values that disagree with each other, give no associated uncertainties with the data. It is sad to see Extended Essays in chemistry where the student has obviously spent hours working out all the uncertainties associated with the apparatus and not talked about the real uncertainties and assumptions behind their results. In fact sometimes their results and all their associated uncertainties are not even scientific as they have not been repeated and an average taken. The same is true of many laboratory reports. Often the reaction does not go to completion, other products are formed, the products have not been properly purified and impure chemicals and solutions have been used to start with. The uncertainties caused by these are far greater than the uncertainties of the instruments used. Many teachers, and hence many students, are clearly confused about how to apply uncertainty calculations correctly and one wonders if the time might not be better spent on a topic with a more obvious chemistry bias.
However the other parts of Topic 11 and 21 are clearly extremely important and it is so good that they now form part of the core programme. Understanding the basics of spectroscopy will benefit so many students in their future studies - not only in chemistry but in medicine and other scientific disciplines. The ability to deduce an unambiguous structure by analysing spectroscopic data is a real skill and a good application of logical deduction.
Teaching the topic
Probably the best way to teach measurement and data analysis is not to include it as a separate topic, in the same way as the other ten core topics, but bring it in as, and when, it is relevant in practical work. For the first few practicals done by students I usually do not pay any attention to uncertainties and errors. Once some practical manipulative skills have been learned I then introduce the ideas of uncertainty and error when carrying out simple titrations and then gradually extend it to other areas such as enthalpy experiments. This forms part of the scaffolding in preparation for the Individual Scientific Investigation but it is not necessary for students to always work out associated uncertainties in every practical and I do not insist upon it for work. I would prefer to get them to question the underlying chemistry rather than endlessly work out small percentage uncertainties associated with their apparatus.
The spectroscopic part of Topics 11 and 21 will need to be taught more formally and students given practice on recognising and interpreting spectra.
The links on the left give you teaching tips, resources etc. etc. for each of the sub-topics together with questions and answers for each sub-topic.
Once you have finished teaching the whole topic you can give the appropriate multiple choice tests on Measurement and data processing and analysis (together with the answers):
Topic 11 Measurement & data processing (1)
Topic 11 Measurement & data processing (2)