Context: I composed this reflection as an assignment required for the Laboratory Physics course I took at Agnes Scott College during the spring 2019 semester. In this reflection, I provide an overview of my personal journey in learning how to use LaTeX for scientific typesetting and document preparation. Additionally, I discuss the relationship between my past experience coding in Python and the present challenges I faced in using LaTeX to create my lab reports. Beyond simple documentation of what it was like acquiring and fine-tuning a new skill, I ultimately use this reflection to convey important lessons I learned in Laboratory Physics about the nature of problem solving, and the value of taking the time to solve a problem fully, as opposed to opting for an easier and quicker “band-aid fix.” Understanding the significance of proper problem solving and the importance of putting in the necessary effort to take pride in one’s work is a crucial skill that I will be able to apply to any future job prospect, especially ones involving engineering, troubleshooting, or problem solving. I have included the reflection in full below.
Since my last reflection for this course, I have completed and submitted by first full lab report using Latex, and it was a learning experience!
While Latex is not “fully” coded (ie, it has less active coding involved than, say, a Jupyter Notebook running Python), Latex does use some coding elements and syntax to help create the formatting and visual styling of the lab reports for this class. This ultimately has been giving me the opportunity to learn a new skill set, much like I discussed in my last reflection for this course- when I talked about feeling as though by learning how to synthesize my prior knowledge of Python with my new scientific data fitting knowledge, I could develop a new, lab-oriented skill set which I would be able to apply in future courses at Agnes Scott. Learning Latex as another programming language or syntax of sorts furthers my digital literacy by expanding the practical applications of my coding experience to include professional lab report template creation and typesetting in Latex in addition to data fitting in Python.
While it can be at times a bit tedious, I definitely see the value in learning and mastering Latex- it allows me to create a document directly in PDF form and specify all of the visual typesetting, alignment, and styling as I create it, with built in capabilities to handle scientific features as well, such as the inclusion of figures, tables, plots, and equations. When writing lab reports in the past, I have known myself to spend entirely too much time trying to get the visual styling and alignment of my figures, equations, etc. to look nice and orderly using Google Docs or a similar word processing system. I can easily get stuck in this step, and wind up wasting time wrestling with Google Docs’ autoformatting trying to get it to look how I want, when I could better use that time to strengthen the content of my lab report. Using Latex, I have found that it is MUCH more efficient and less frustrating to use a programming language designed for typesetting. It saved me a lot of time, allowed me to stress less about the formatting and just focus on the content of the report, and helped me to create a lab report that “looks” very professional, which is a new skill I have acquired through this class. Thinking back to lab reports I turned in last semester in Modern Physics that were composed in Google Docs, they did not look nearly as professional and polished as the lab report I just recently submitted for this class, and they took ages longer to format. Thanks, Latex! This course continues to challenge me to expand and update my digital literacy knowledge and apply it to a new set of scientific applications, helping me to develop a blended skill set that I did not possess before taking this class. I’ve also learned a lot about the formatting of a professional lab report, and it’s been interesting to compare and contrast this new set of expectations with work I’ve submitted before, such as lab reports in Modern Physics, and look at the difference between the two- two columns versus one, the introduction of an abstract, the derivation of equations instead of merely including them, etc.
I feel as though I am developing ways to harness computers to “think smarter not harder” so to speak- utilizing coding engines to handle the tedious parts of lab reports such as formatting and data fitting and save myself time while creating a more polished product that is closer in line with what the scientific community would expect from a paper or lab report. It is a goal of mine to continue developing these new skills to the point where I feel comfortable with them and they are second nature. In the past, I have known myself to approach learning new digital literacy skills (how to use a new program, a new technique in Python, etc.) with a fair amount of apprehension, and I think this class is giving me a lot more confidence in just diving right in to trying to pick up a new digital literacy skill without stressing out about it and feeling intimidated. Feeling intimidated by something is one of my personal biggest barriers to learning. Conversely, feeling confident using material, applying it to different situations, and teaching it to others is my personal benchmark for knowing that I have truly mastered it, and so I am grateful for any ways to feel confident about a new skill instead of intimidated. Especially for people with academic perfectionist anxiety issues, as I discussed in my last reflection, it is important to remember that you can’t learn anything without feeling out of your element at first, and that the more you embrace that feeling, the more you will be able to master, and the less nervous or worried you’ll have to feel along the way, so you can just focus on the material instead of beating yourself up for not understanding it perfectly.
In addition to developing my digital literacy skills through data analysis and lab report formatting using Python and Latex, this class is also helping me develop my troubleshooting abilities, between troubleshooting errors in Python (shoutout to the five hours I spent last weekend fighting with Jupyter Notebook over what ended up being a single missing asterisk), troubleshooting errors in Latex, and troubleshooting errors with equipment in lab itself. As someone with ADHD, I do not tend to consider myself an exceptionally patient person, and it can be really tempting for me to give in to my short attention span and find the quickest way to fix the problem instead of really taking the time to fully troubleshoot an issue that I am encountering and find an elegant solution that’s a bit more time consuming. The problem with giving in to this impulse is that without taking the time to fully unpack the problem and fix it, you likely lose the chance to fully understand what was causing the problem in the first place. Additionally, the quickest way to solve the problem is not always the best way, and in my personal case, feeling like I took the easy way out soothes my ADHD’s attention span impulse to move on to something new, while driving my academic perfectionism anxiety up the wall and creating an unproductive (at least for me) feeling of insecurity over the quality of my own work, which then hinders my progress.
I’ve run up against this problem several times in this class (not so much in lab, where the combined brainpower of me, my lab partner, and Dr. Ackerman can help guide the experiment in the direction of a hands on genuine solution to the problem) while data fitting in Python and compiling code in Latex. Often times with coding, when your code isn’t compiling there are numerous things you can do to get it to compile correctly and run without an error, but many of these do not necessarily fix the problem, especially in the case of data fitting where the quality and strength of your fit is crucial and will affect your residual plot and chi squared value. As I experienced several times with this particular lab’s data fitting, sometimes you can jury-rig something to get your code to compile properly- without being certain that this “band-aid fix” actually did anything towards fixing the underlying problems in your equation that are causing the fit to have a weirdly high chi squared, which I suspect is what might have happened with my last data fit. The lesson I learned this last lab report is that you have to ask yourself if whatever you just did really fixed the underlying problem with your fit or if it just made your code compile without an error, and if the answer is the latter, you have to take the time to try to figure out what’s really wrong with your fit. It is necessary to have the patience to sit there and focus and figure out the actual problem instead of being tempted by the band-aid fix, even if it is tedious, and this is a skill I currently feel is a bit underdeveloped in me- though I have noticed progress in it since I began taking this class, so I hope that with intention, I can continue to develop my patience in troubleshooting through this course. Developing this skill would serve me well not only in this class, but also in life in general.
Plus, as someone whose job consists of teaching science, physics, car maintenance, and coding to children through hands-on experiments, my workplace and work skill set requires tons of patience- in managing a classroom of excited and rowdy children, in the necessary patience involved in the helping my students with the troubleshooting portion of hands on engineering projects, and in teaching the children how to develop their own patience when working on these projects. By developing techniques to work through the frustration of the troubleshooting process, I will be able to share these with my students next summer- especially students with ADHD like me, who may have additional difficulties with patience and who could use some extra support in the classroom.
Overall, I am starting to develop a belief that good, professional laboratory practices and good science (keeping a lab notebook, patient troubleshooting, utilizing digital resources to make your life easier, etc.) not only allows for taking better, more accurate data, but also for developing a number of widely applicable personal skills that one might not instinctively associate with science (patience, flexibility, coding ability, determination, etc.). Thus, it is my goal to seize this opportunity and take full advantage of it to make sure that the lessons I learn in the lab get carried with me outside of lab and into my everyday life, so I can apply them to my personal relationship with learning, my job/internship, and future classes at Agnes.
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