====== Introduction to Experimental Physics ====== ====== Lab training ====== ---- ==== Introduction ==== * Introduce the lab staff * Mark Chantell – KPTC 209, 773-702-7012, mc2@uchicago.edu * Dave McCowan – KPTC 209, 773-702-7012, mccowan@uchicago.edu * Kevin Van De Bogart – KPTC 209, 773-702-7012, kevinv@uchicago.edu * Introduce the LA program * Clarify TA/LA duties vis a vis grading, interaction in lab, etc. * Are any LAs here today? ==== Course philosophy ==== === Learning objectives === For the introductory physics laboratories here at the University of Chicago, we have adopted a set of learning objectives. By the end of this lab sequence, you should be able to do the following: * collect data and revise an experimental procedure iteratively and reflectively; * evaluate the process and outcomes of an experiment quantitatively and qualitatively; * extend the scope of an investigation whether or not results come out as expected; * communicate the process and outcomes of an experiment; and * conduct an experiment collaboratively and ethically. Put succinctly, **the goal is to understand //how// we know, not //what// we know.*** === Autumn quarter themes === In Autumn Quarter, the labs students will do relate to the following key “themes”: * Student Agency * Critical Thinking * How much data is “enough”? * How do I know when I’m done? * Experiment Design * How can I improve my results? * What do I do next? * Does my experiment answer the question I asked? * How do I check my results? * Sense-Making * Model Building/Model Testing * What physics is involved? * What assumptions have I made? * Do I have disagreement because of the model or because of a systematic bias? * Drawing Conclusions/Estimating Uncertainty * How do I estimate uncertainty? * How do I identify systematic bias? * Are my data consistent with expectations? (Should they be? How consistent?) * Analysis Tools * Python as an analysis tool * How do I process data and calculate quantities? * How do I visualize data? * Design Feedback Loops * How do I use my results to inform my data collection procedure? * How do I use my results to inform my model? ==== Weekly training outline ==== * REFLECTION: Reflect on last week’s lab * Technical: * What part(s) of the lab worked best? * What part(s) worked worst? * What would you like to see improved? * Self-reflection: * What is one thing you did that went well? * What is one thing that you’d like to do better next time? * Observations/connections: * Did you practice techniques that we highlighted in training (or techniques from past weeks)? * Did you observe anything surprising, concerning, or encouraging? * How are your students growing/regressing? How is their learning changing? * PEDAGOGY: Build skills or learn teaching techniques in small groups * Sometimes it will be "skill-building workshops"... * Present a skill (or technique) that is applicable to the week’s lab * Practice that skill (through discussion or role play) * Identify roadblocks that stand in the way, or discuss strategies for positive implementation * Sometimes it will be "group discussion"... * Present an idea, question, or philosophy for discussion * Reflect on previous experiences * In small groups, discuss how this idea applies or how it can be implemented * EXPERIMENT TRAINING: Direct, hands-on training for this week’s experiment ==== This week’s lab (at home activity) ==== * Logistics * There is no in-lab activity this week. (Labs are closed and locked.) * Send a reminder to the class to complete the assignment on their own * Due date is Friday, October 6 @ 5:30 pm * Students submit a single PDF on Canvas * Have the lab submission assignments already been created? * Grading: We will go over grading more carefully next week, but it will be 4 points, mostly based on completion * Look over the assignment yourself, and ask if you have any questions about what is expected. ==== TA Expectations ==== * Provide TA Expectations handout: * [[phylabs:lab_courses:ta_info:ta_phys-120_130_140|]] * Lab safety * No food or drink in the lab * Yes, this includes water! * Students need to go out into the hall to eat or drink * Keep the lab benches tidy (store bookbags under the bench or by coats) * Maintain a respectful and kind environment * If someone is sick, ask them to go home * When leaving the lab, close the door and turn off the lights! * This applies even if you are an afternoon TA with a lab following you later. * If something breaks... * Notify lab staff * Come find us (if normal hours... before say 5:30 or 6:00 pm) * If you can’t find us, email us * Set the broken equipment aside and put a note on it clearly describing the problem (not just “broke”) * Make sure all lab stations are returned back to their original state before leaving the lab. * You can make this part of a group’s "participation grade" if you want! ==== Pedagogy ==== === What do you want to get out of this? === * Overarching question: "What do you want to get out of this?" (And by "this", we mean lab training, of course, but also the whole teaching experience and your interactions with students.) * Write down (or share out loud) the first three things that come to mind when you think about labs. * Students can write this on a piece of paper and throw it at the instructor as an "icebreaker" * Reflect on past lab experiences more generally (see below for prompts) * Wrap up with: **What do you want to get out of this?** === Reflecting on past lab experiences === Ask TAs to reflect on the following questions... * What kind of instructional labs did you have as an undergraduate? * Cookbook/traditional? * Electronics? * Advanced physics course? * Open-ended projects? * Senior thesis? * What topics were emphasized? * concepts * demos/observations * lab skills * keeping a lab notebook * writing reports * data analysis * uncertainties * specific (specialized) equipment * verification labs * How did the TA engage with the class? * sat in the corner * ran discussions * helped debug problems * How were you evaluated? * Participation * Lab reports * Lab notebooks * Presentations * Did your labs have any unusual teaching methods? * Reading papers * Building models * Building apparatus * Students deciding on research topics/questions * Studio physics * "ungrading" ====== TA grading notes ====== ---- While we do want you to grade the first experiment on completeness, we'd also like you to give the students some feedback on what they've done to let them know what's being expected of them in the future. To that end, here's a (non-exhaustive) list of things to consider when looking at the assignments. // Only complete omission of a response or an utterly superficial response (such as those bolded below) are grounds for losing points on this assignment.// Keep in mind that the entire assignment is worth 4 points, so slight omissions in responses should result in only slight deduction of points. ==== Things to comment on when grading the report ==== - **Did the student include a picture of their ruler?** - Does their procedure for making tick marks seem reasonable? - If not, give feedback on why. Was it too arbitrary? Hard to reproduce? Done sloppily? - Were the measurements of the ID card reasonable? - How was their assessment of uncertainty? Presumably the Z dimension (thickness) will be hampered by the resolution of the system the most. - Did the student come up with reasonable ideas for systematic uncertainties? Note that they don't have to definitively be a cause of uncertainty, but they should be plausible. - "The length could change due to heating from my hand" is an example of something just on the edge of plausibility. Technically true, but extremely unlikely unless they've got very hot hands or they're making a very high precision measurement. This is a case where the student would be given credit, but you might make a note on the low likelyhood of relevance. - "My ruler seems to be bent or bowed" is a decent example; easy to assess and very likely to affect measurements. - "My markings might not be uniform" is also a good example - **"Quantum uncertainty might suddenly make my ruler shorter" is not a good prediction; it sounds science-y but is utterly implausible.** - Did the student propagate uncertainties properly for a multiplicative quantity (i.e. did they add relative uncertainties)? - Did they justify either including or excluding some uncertainty in a plausible way? - The volume calculation could neglect the uncertainties from the x and y components, because the relative uncertainty in the z component will be an order of magnitude larger or so. - Did the students try to make proper comparisons with the three hypothetical classmates? - As an example, the $t`$ statistic for Wynn and Omar's area measurements is 0.8, so they would be in agreement. Leslie and Omar's $t`$ for area is 1.2, which is inconclusive. - If a student makes math errors that lead them astray (e.g. forgetting the square root) but interpret the derived $t`$ statistic properly you should give them credit, but note where things went wrong if you are able. - Was the student's improvement suggestion reasonable? - **A half-hearted answer like "I would measure better" or "I would measure more times" isn't sufficient.** - Were the questions for other classmates sensible? Again, **completely surface-level things like "Did you do it wrong?" are insufficient here.**