I have competed my analysis of the waveform.  What I realize I am missing is any error analysis.  I have al these values calculated, but how do I know if they mean anything.  A colleague was working on error analysis, so I am reminded I need to do it.

I can calculate standard deviation of the values, like wavelength or period, but I wonder if I need to think about the varying shape of the waveforms I have averaged?  Is there a corresponding property to correlation constant?

So this further work, and I am not likely to get it done this week end.  Put in some standard deviation and leave it at that.

I have always though the reason the students in my school do badly on tests, other than lack of test taking skills and inherent inflated egos, is that they don’t know how to read and generally lack perseverance.  Of course most people tend to approach problems with straight forward, brute force solutions so we use drill and kill test practice sessions, and train all our teachers as reading teachers. There is nothing, in particular, wrong with this other than the fact that drill and kill only teaches student to fry fish that are given to them, not to catch the fish, and I should no more be teaching reading than an english teacher should teach math.  This is not being insulting, it is just that on more than one occasion an humanities teacher has seen a math problem on the internet, taught it, and then I spend my class unteaching the misconception.

That does not mean that we all should be teaching kids to read, and to work problems creatively and with persistence.  We should, which is why I love using computers as a teaching tool and explicitly teaching computer skills and coding.

I can’t tell you the number of times when a students has asked me where to click a web page, and when I say look for the assignment, they just randomly scroll up and down, refusing to even decode.  They will do this for a long time, hoping to frustrate me until I do the reading for them.  I won’t do it.  They need to learn to read, and to make an effort to decode if they have not reached that level.  If they cannot decode the words on a web page, how can they hope to comprehend the questions on a test?

Coding is a similar situation.  I have been working on my lesson plan this week, which is a maker project to collect data and store it for later analysis.  I have several resources listed in the assignment.  These resources are written, and are a curated set of the documentation I used to learn the device and how to program the maker project.  To succeed the students are going to have read this documentation, comprehend it, and apply it to the problem.

Coding itself is an exercise in reading.  The grammar is very precise.  In python a misplaced space is an error.  Students must be able to read and comprehend the code, separating the tokens that are critical from the strings that informational.   When starting coding, students have a great deal of trouble because they are simply trying to copy, just like they do in all the other classes, without decoding or comprehending.  Breaking them of this habit, forcing them to work the problem, not memorize a solution, is a primarily goal in every class, not just in computer classes.

In this maker project, simply copying the code was not enough.  There was a bug in the code, or a misunderstand on my part.  The file was not saving, and most of my time was spent dealing with it.  This is the ultimate form of comprehension and problems solving that we should be teaching.  Perseverance.  Use failure a tool for learning.  Hope for a fortuitous accident where a clue emerges, and be vigilant enough to identify the clue.  As we learn when we do research, even if we do not solve the problem, identifying and dispersing a clue is a success in itself.

We went though diversity training this week.  As always, I was disappointed in that diversity was seen as an option and not a requirement.  I understand that from a pedagogical and social point of view it was necessary.  We as teachers have to start where the students, the public, the audience is, and the reality is that diversity is seen as a convenience and not a necessity.  They have not seen that limits on what a person can be based on if they were born with white parents are social constructs, not inherent to the person.

For example, one African American acquaintance told me that when he was a teacher he knew that the hispanics would be ok as they tended to be construction workers, and that the best bet for many others to make it is sport, so he tried to help them succeed in that way.  As my Hispanic relatives were doctors, surgeons, architects, teachers, pharmacists, and my neighbors or many skin colors were not athletes, but professors, lawyers, a wide range of professionals, I tended to disagree with him.

So I do think of diversity as a necessary condition for success, not an option.  I think the science agrees with me as well.  As we are doing medical research this week, I will focus on that.

We are working in the Scalable Health lab on a devices that may be able to collect and interpret the photoplethysmogram(PPG) in such a way to diagnose or predict medical outcomes. One issue that effect the collection of data is the quantity of melanin in the skin.  If there were only light skin participants, we might develop a machine that only worked on light skinned people, and it might go to market.  It reminds me of the time when most cardiovascular researchers and participants were men, This lead to results were the presentation of cardiac events in women were not well understood.  This may be happening in autism research now, where it is possible girls are are not receiving appropriate interventions as they are not being diagnosed, because we assume they present identically to boys.

As a science teacher, I take it as a directed and funded mandate to increase the diversity of the researchers in the world.  I find talent to be independent of any of the physical characteristics that so defined who has opportunity and who does not in this country.  It is not a just my opinion that diversity in research in critical.  I think as science teachers we need to take this challenge seriously. It is up to us to show some coordination in maximizing opportunity so that all who might do research have equal opportunity.

It is useful to approach a topic with which one is generally unfamiliar, or even  not generally interested in, such as  has occurred this summer.  I was trained in in science and research, but rebelled by studying physics instead of biology or medicine like much of my family.  Engaging in the opportunity to study the medical topics is provides not only valuable learning, but also a connection to the difficulty of my students who often are expected to engage with topics they may be uninterested in, often will much less background and opportunity.

In particular, the jargon for a particular topic can be jarring, not only new words but also refining the definition of existing words.  For instance, in physics we have to refine the definition of words such as heat, work, and especially temperature.  As an aside, this is why the overuse of sites such vocabulary.com worries me.  Sometimes words are not context sensitive, especially in math and science and computer science, and implying that some words can have more than one definition can be harmful.

In the current research, the jargon is extensive.  Words such as Dicrotic Notch and Diastolic Peak are unfamiliar and complex.  The methods and data needed to calculate each can be equally complex, not only to understand, but also to derive and gather.  One item I was told to focus on in the new year was to provide more scaffolding for students to help them in the problem solving process.

One thing I have learned over the past two weeks is that the various tools I have picked up over the years allows me to break up complex problems into small pieces, then put those pieces back together into a overall solution.  I can eliminate extraneous information for the current step, then put it back if I need to later on.  I can adjust my thinking to the current rules, like the fact that gol has no ‘a’, so that we can efficiently approach the problem at hand.

I recall many years ago when we all were so excited about the possibility of computers to crunch huge amounts of data and instantly deliver accurate results.  For instance, we could take a data set of 10,000 values, instantly visualize it, analyze it, and in a few minutes develop an in situ stoichiometry.  Or we could follow a signal and real time determine if we what was happening in a reaction was what we thought was happening.

I also recall  a time when everyone realized that computer code had bugs, and those bugs could significantly effect the accuracies of the results that we were taken on faith, trusting the computer in the same way that a student trusts a calculator to magically give them the correct answer on standardized test.

In many ways our faith in our equipment is the  same as the faith of the student in their calculator, as we use the results without understanding of the process.  In many ways, this is nothing new.  Researchers have been purchasing equipment for years without completely understanding what the equipment does.  However, the fact that we are now using closed code, or home brewed code, or code that we never review adds another level of uncertainty.

The fact that we worry about such things is not new.  In my senior lab class for physics my professor would not accept anything unless I explained what the computer was doing and verified results.  A major error in software was discover in the lab I worked in `as we compared out results with other labs doing the same work.  This is how science works.

Over the past decade or so there has been an increased realization that we have introduced error into our results not only by unfaithful actors in the research community, but also because our equipment has become so complex, our assumptions so ingrained, that we accept results as valid without verification.  In the work we are starting this week, I see this desire for clarity.  There are many researchers asking what can we really know by looking at the  Photoplethysmogram(PPG) waveform.  I also see manufacturers aggressively hiding the code and data, presenting results of their Pulse Oximeter, presenting the results as accurate with no transparency.