Dudes, teaching this night class is the best thing ever.
Like…I have all day to walk and then go to work from 6 PM to 9 PM? You couldn’t even imagine a better time of day for me to work.
This is so fantastic. My job cannot get better.
Well, actually, I guess the only way it could be better is if there was any sort of permanence to it past this semester. I would love to have this as my career, I really would. But I have to just be patient at this point.
Will everything work out so that I can do this for the rest of my life?
But I really, really, really am hoping it will.
Edit: here’s the room I’m teaching in. This is the view from my little lecture podium.
What’s the only thing better than teaching stats?
Teaching an evening class of stats!
(Which…I guess…is still teaching stats…just…just humor me here.)
I talked to Jim today and not only does it sound like I’m going to get to teach in the spring (which is like the first summer session at UI), but I’ll get to teach the super late “none of the other instructors want this timeslot” evening class of 217. That means I can spend ALL DAY WALKING before going in to work, especially since I’ll already have my class notes mostly prepared (due to teaching 217 this semester).
That’s going to be awesome.
Now I just need to somehow get a course to teach for fall, haha. I will do everything in my power to make this a permanent thing.
It’s TEACHIN’ TIME!
I don’t think I can properly convey my excitement over being able to teach statistics again. I mean, I guess I’ve been running labs for the past two years, but it’s just not the same, you know? I really feel like teaching statistics—especially intro statistics—is what I’m meant to do with my life.
And I know this position is only temporary (I’m technically only hired through December 31st of this year), but I’m going to do whatever I can to see if I can keep it going longer. Surely some of the higher up professors who teach 213/217 would want an opportunity to focus more on their research or on the upper-division courses they’re teaching, right?
Either way, I am eternally grateful to Scott for really pushing those in charge to hire me. I’m pretty sure I would have never gotten this opportunity without his influence. Now I just have to prove that I know what I’m doing and hope that they need somebody for next semester.
I AM, THAT’S WHO!
This afternoon, in the span of about an hour, I went from hoping to eventually get a teaching job somewhere to hearing that I get to teach a section of STAT 213 in the fall. It’s currently just for that semester, meaning there’s no guarantee that I’ll be hired for any subsequent semesters, but that’s what they said at UI and I worked for two years (plus summers) as a lecturer there before heading to the frigid north.
I am super, super excited, yo.
So I picked up all my TA evaluations a few days ago because the department gets rid of them once you’ve graduated and I wanted to make sure I picked them up before I forgot to do so.
Anyway, today I feel like useless garbage (I mean, even more so than normal), so I decided to get a little ego boost from the reviews. These are my favorite comments.
- “Best TA I’ve had”
- “I feel like she can teach better than prof”
- “I have nothing but amazing things to say about her”
- “She could easily be a professor”
- “It is evident that she cares about students’ success”
- “Claudia has helped and taught me more than any of my other teachers”
- “Very friendly and welcoming and is good at creating a fun environment for learning”
- “Goes through questions and tells you information that not even the prof mentions”
- “She is very, very good (better than my prof)”
- “Should be offered a job should the need arise”
- “One of the best TAs I’ve had over my 4 years”
- “I found her lab sessions and office hours were highly beneficial to my learning (even better than the lectures)”
- “She is a very good teacher, gifted at it even”
- “Learned better from her labs than lectures”
- “She is the bomb”
This found its way onto my Tumblr dash. It’s an interesting read. It seems unlikely that he would fabricate all of this, but there’s something about it that seems a little extreme. Having worked as a lecturer at the U of I (which, as we all know, is just as prestigious as UC Berkeley) and having worked WITH lecturers at the U of C, I’ve seen things done both ways: lecturers get basically complete freedom with how/what they teach and as long as they get good reviews, it’s all fine (U of I), or they have to teach to rather specific rules/guidelines, to the point where their instruction has to be similar enough to other lecturers so that there can be a common final for all sections of a particular class, even if the sections are all taught by different people (U of C). It can be hard either way, I think.
But either way, that seems to be only part of what this guy’s saying in his statement.
Edit: here’s a different perspective.
It’s summer session, which means it’s time for me to be a TA again!
Actually, this semester it’s a little bit different. One of the professors I’m TA-ing for was one I TA-ed for last semester. He knows I want to be a stats teacher at some point and seemed to like the job I did last semester, so this semester he’s letting me basically teach a fourth day of class in place of the lab I was supposed to run.
Which is super snazzy.
I’m also TA-ing for a new class, but it’s a lower-level one than the others, so it shouldn’t be so bad.
I have an exciting life, huh?
We had to write a teaching philosophy statement, summarizing our approach/attitude towards teaching math and stats, for our seminar class. I’m going to post mine here. It explains how I like to think about teaching stats and emphasizes what I consider to be the most important things to keep in mind while delivering the subject to students who may be less than enthusiastic about anything number-related.
I have, for the majority of my life, been a student, but only recently have I taken on the role of a teacher. From 2012 to 2014, I worked as a lecturer at the University of Idaho, teaching introductory statistics to undergraduate students. Though I was brand new to teaching and wary about teaching material that had the reputation of being boring, difficult, or both, it took me only my first semester to solidify my teaching philosophy and to develop instructional techniques that have been proven effective in getting students interested in and engaged in learning statistics.
My approach to teaching can be best broken into four key components: empathy for the students, flexibility in the teaching techniques and methods, enthusiasm for the material, and the ability to show real-world application of the material. These four components work together to emphasize my core teaching philosophy and goals: in order to best foster learning, teaching should not only cater to the student but cater to the subject being taught as well, allowing for students to have a learning experience that allows them to see both the technical and the practical side of the material.
Unlike many who end up as lecturers in mathematics or statistics, I come from a background heavy in the humanities and the social sciences. Due to this background, I am able to easily empathize with students who may lack a strong mathematical background and thus may be intimidated or nervous about taking a statistics course. Such students are common in introductory courses similar to the ones I have taught, as these courses are usually used to fulfill a course requirement and are often the only classroom exposure to statistics that many students get.
An empathetic mindset—especially when introducing “basic” concepts and notations that may be brand new to many of the students in the class—is a mindset I try to adopt when teaching. Telling students that it’s okay if this is the first time they’ve seen how to calculate a mean or how to read summation notation, and then to walk them through it slowly but without condescension, can go a long way towards fostering a good relationship with them while also making them feel more comfortable with the material. In my two years of lecturing, I had many students comment that they appreciated the fact that I took the time to teach them the very basic components of statistics before delving into the more complicated material and that this helped them feel more at ease in the class.
Along with empathy for the students, another important component in my approach to teaching is to be flexible with how the material is taught. Most statistical concepts can be explained in several different ways—showing students the equations, explaining a concept using an analogy, creating a visual representation of the concept, among others. I have learned that it is important to try to approach and explain a concept from several different angles to allow as many students to grasp the concept as possible. For example, when students are learning about analysis of variance, I have found that some students understood it by looking at the equation for the F statistic, while others understood it by seeing a visual of the variances that the F statistic is actually comparing. Being able to explain a concept from multiple angles helps to make sure that every student is able to grasp the concept and understand it in a way that’s best for them.
The third component to my teaching approach is one that I feel is especially important but often overlooked in the field of statistics—showing enthusiasm for the material. In my own experience as a student, when an instructor fails to show interest for the topic they are teaching, it becomes difficult to remain engaged in the class and ultimately more difficult to learn. For a subject like statistics, which already seems to have a reputation of being dry and boring for many students, it is important for an instructor to be able to show that the material and concepts that they are teaching are more than just equations on a page.
For me, part of showing my enthusiasm for the subject is to really try to give students an “under the hood” look at statistics. Rather than just show them a formula or tell them a theorem, I’ve found that students remain much more engaged if they are shown why a formula is the way it is or why a theorem is important. For example, when teaching students the general formula used to calculate binomial probability, I choose to “derive” the formula by using some basic probability examples and then work up to the actual formula itself. Giving them this deeper glimpse into why the formula is written the way it is not only gives them a better understanding of the formula itself (they are no longer just “blindly” using it but understand the reasons behind it), but it also engages them in the process of seeing how and why the formula works.
Another way of showing enthusiasm for the subject is to present students with fun and interesting examples of concepts they are being taught. For example, after students learn about visuals like bar charts and histograms, I like to take a few minutes and show them some examples of poorly constructed or exaggerated graphs used by the media or by politicians. Bringing some humorous or interesting examples into the classroom can help keep students engaged and make them more interested in the subject.
Related to the concept of showing enthusiasm for the material, the fourth and final component of my approach to teaching is to make sure to give students some real-world applications of the material they learn in the classroom. This is a component that I think is especially important in teaching an introductory statistics course, as nearly all academic fields employ statistical procedures to some degree, but students from other disciplines often think that they will never use the techniques they learn in class. The main way I employ this component is by using actual data from various different fields in my in-class examples. When teaching regression, I use data from a published psychology study. When teaching analysis of variance, I use data from a plant breeding program or data from a business-related study. I feel that it is important to emphasize to students through examples that the techniques they learn in their statistics courses can and often are applied in their own fields.
My application of these four key components in my teaching has proven effective in providing a good learning experience for students. I consistently received positive teaching reviews during my time at the University of Idaho, with many students commenting that they felt like they truly understood the material and had actually developed some enthusiasm for the field of statistics. I have also recently received a Graduate Teaching Assistant Excellence Award from the University of Calgary for my work as a TA in the fall of 2014. I plan to continue to adapt and modify my teaching strategies and techniques to further my goals as a teacher and to provide a comfortable, thorough, and enjoyable statistics education for students in both introductory courses and more advanced courses.
Hey, so it turns out that I won a Graduate Assistant Teaching Excellence Award for my TA work last semester. Snazzy!
Actually, I think I was the last person to find out I was one of the
five six winners, considering the fact that an email regarding the awards was sent out last week to everyone but me, haha. I only found out once someone said congratulations to me and I had to ask, “For what?”
Also, Multivariate Analysis looks like it’s going to be awesome. We indeed get to do factor analysis. Expect nothing but joyful screeching that week.
Edit: I guess all the other four winners are from pure/applied math. I’m the only one from stats. Badass!
Edit 2: holy crap, I get $600 as part of that award? Badass!
(Inspired by past experience, past observations, and the complaints of undergraduates from three—count ‘em, three—universities.)
- On the first day of class, go over the syllabus, ask if there are any questions, and then launch right into the material. Omit any form of road map for the course or any reasoning as to why everyone from bio majors to engineering majors to political science majors are required to take the course.
- Pick the driest, most boring textbook possible. Use it and it alone as supplemental material to your lecture notes.
- Assume that all students, regardless of actual background or major, are familiar with such thing as summation notation, factorials, slopes, and calculus. That sociology major sitting in the middle row who’s struggling simply because they don’t know that 5! = 5*4*3*2*1? That’s their problem, not yours.
- Make sure your class is all application, no theory. After all, why would people need to understand the reasoning behind the tests they’re using so long as they know how to do the tests?
- If you cannot implement the above method, try making sure your class is all theory and no application. After all, if you teach the theory really well, odds are the students will be able to derive the practical applications by themselves, right?
- Stick to the most boring examples you can think of, and make sure every example you use is coming from the same area of research. Do you have a background in business? Every example should be business-related and involve as many technical terms as you can throw in there. Same idea if your background is biology or psychology. That way, students can really see how statistics can be used in practically every field—as long as that field is yours.
- There are plenty of cool, funny, and downright fascinating examples where statistics are used in unique and exciting ways. Make sure you keep these engaging examples out of the classroom.
- If there is a topic that the majority of the class is struggling with, assume that it’s their own faults for not studying it well enough and press onward to new material. Breadth, not depth, right? Who cares if there’s a section people are struggling with as long as you cover every chapter in the textbook by the end of the semester.
- Offer only one explanation of each topic. All students learn the same, and thus why waste time trying to explain a concept in two or more different ways? If there is confusion over your explanation as to why we use an ANOVA versus a bunch of paired t-tests for comparing 3+ means, it’s not your problem. Everyone should just be able to understand your explanation with no problem, so long as they’re applying themselves.
- Finally, lecture in the most unenthusiastic voice possible. After all, you’re talking about numbers, right? Numbers are obviously inherently boring and this boringness should be conveyed through your lecturing style. If students are willing to learn, they should be able to get past your droning voice at 8:30 in the morning. Those who fall asleep simply are slackers.
All of the above info must already be very obvious, because so many statistics teachers seem to glean their teaching techniques from at least one or two of the above points.
After all, if we’re not making statistics the most painful subject to learn, we’re not doing our jobs right.
I get to teach in the summer!!
And what’s better: it’s not the 7:30 AM section!!! It’s the totally reasonable and much more Claudia-friendly 11:30 AM section, which runs for two hours on M/T/W/Th and starts right after spring semester is done.
I still don’t have any concrete plans past that point—which SUCKS and I hate it—but I’m glad that I at least get to teach in the summer.
I think my favorite part of teaching is helping the students who really struggle with math/stats.
There’s a huge range of familiarity and comfort with math in 251. That’s likely because you only need two years of high school algebra to take it (or, failing that, MATH 108). The stuff we start with isn’t too conceptually hard or even computationally hard—the mean, median, standard deviation, etc. However, the notation for these calculations seems to be what trips some people up.
Example: I’m sure we’re all familiar with the mean and how it’s calculated. Suppose we had a dataset with values 3, 5, 2, 6, and 7. To find the mean, we add up all the values and then divide by the total number of values, which in this case is 5.
But here’s the formula to do that:
If you’re not familiar with summation notation, this calculation, as simple as it is, might look really intimidating, right? And I’d guess that students who have taken MATH 108 as their highest math class at the U of I haven’t been exposed to summation notation. I know we never saw it in MATH 143 (which is a horrible class in general, but I won’t go into that). Personally, I don’t think it was ever explicitly explained until I took MATH 176 (Discrete Math).
Unfortunately, I’ve heard from a LOT of students who are re-taking STAT 251 or who had taken stats in high school that a lot of the “basic” notation we use right at the beginning of class was never really explained. It was just knowledge that they were assumed to already know, and so was glossed over rather quickly.
That really bothers me.
That’s why my favorite part of teaching is actually helping to make sense of these “everybody should already know this” concepts…these concepts that, in actuality, not everybody already knows. I’ve had a lot of students who have taken stats in the past say that no one has ever explained those things to them and that they really appreciated a basic “here’s what this capital sigma means and how to calculate stuff with it” and other such explanations.
And that makes me happy. Not happy enough to alleviate my anger over the lack of standardization across all sections of STAT 251 at UI, but happy.
Elaboration: After our Proofs final this afternoon (which was surprisingly easy), I had to go copy the tests for STAT 251 (‘cause, you know, I don’t plan ahead like I should and instead waited until the night before the test to copy them). So Wayne and I went back to Brink. We went up to the stats department floor and he worked on his SAS project while I started the copier.
The first ¾ of the copies went fine, but then the machine stopped so I went to check on it. I figure it was just out of paper; I was making 145 copies, after all.
But no. What was it out of? Staples.
Replacing the staples in that particular copier is the hardest freaking thing ever.
So I manage to mangle the damn staple holder to the point where I’m pretty sure it’s broken. I’m totally freaking out because I’ve got 37 more copies to make, and Wayne, with his seemingly boundless patience, tries to fix the staple holder for like 15 minutes before we’re both like, “okay, screw this” and I leave a frantic note with the gist of “sorry, I broke the staple holder ‘cause I’m an idiot” and went to Kinkos to make the remaining copies.
Got home at 9:45. I have my probability final tomorrow at 7 AM.
Screw finals week, man.
I was talking to some dude in the Commons today while I was waiting for English to start. We were blah-blahing about school and stuff and he asked me if I was working and going to school at the same time and I automatically said “no.”
I freaking forgot I was employed. That’s how much I love my job: I keep forgetting that it’s a job. I’m getting paid $23 an hour to talk about what I love.
THAT’S SO FREAKING AWESOME I CAN HARDLY STAND IT.
(Sorry, I’ll stop now)
Not only do they want me to lecture next spring, but they want me to do TWO sections! WOOT.
Does that count as a promotion?
(I say yes.)
I’m so lucky to have a job that brings me happiness. I know it’s not a permanent one (unfortunately), but to be paid to do something that doesn’t even feel like work is freaking awesome. The summer session of stats is over this Thursday. Now that I’ve (almost) finished teaching three sessions of intro stats, I think I’ve figured out the main reasons why I love this job so much.
The obvious one: I love statistics.
I think I kind of always have, I just didn’t know it. When I started college back in the stone age in 2006, I went into psychology not because I didn’t know what major to pick and that’s kind of the “default” one, but because I was actually interested in methods of quantifying intelligence. After I took the class I’m teaching now (STAT 251), I kind of changed my mind and became interested in psychological testing in general. Then it was recommended that I take MORE stats classes in order to solidify my chances to get into a psychometrics program…and, well, we all see where that’s led. I just really, really like stats.
I’m imparting knowledge.
This is a totally cliché reason, I know, but it’s a reason nonetheless. There’s something very satisfying about imparting knowledge to people. It’s like giving them power. I think it’s even more satisfying in the case of statistics because a lot of peoples’ visceral reaction to a stats class is either “god, this is going to be so BORING” or “god, this is going to be so HARD” or a combination of the two. I love showing (or at least trying to show) that stats can be both fun and fairly easy if you get a solid understanding of what you’re actually doing when you do stats.
It’s intro statistics…
Intro classes are broad as hell. A lot of time, in my opinion, they exist to kind of help “weed out” people from certain majors before getting more in depth with the history/materials of whatever the major is (or to put it in a less sinister-sounding phrasing: they’re “survey” classes used to give people a general sweeping idea of what the major entails before they fully engage in it).
STAT 251 is like that too, but there’s actually kind of an extra bonus to that. Since it’s a broad intro class and we have to cover a lot of stuff, we really can only touch on the stuff that’s really, really important. That really, really important stuff is actually the really, really useful stuff—it’s the statistics that non-statisticians use. It’s the stuff that biologists use. It’s the stuff that advertisers use. It’s the stuff that a person working for a big business uses. It’s “hey, you only gonna take one stats class ever? Here’s the stuff that will get you through most of your life.” Because you WILL use something from this class at LEAST once. I’m definitely not saying that more “complex” stats aren’t important…I’m just saying that this is the stuff that even those people who stand at the entrance of a stats class with their fingers in an “X” and yelling “No…nooooooooo!” will probably use. And that’s cool.
…and almost everyone has to take it.
You really get a mix of ability/familiarity with the material in an intro class, I think. In the case of stats, some people are coming right out of high school and have taken AP stats. Others have never heard the term “median.” Both this semester and last semester I had students come up to me who said something to the effect of, “I was terrified to take stats because I knew nothing about it and people always said it was hard (or “I hated AP Stats!). But this class was actually really cool and I’ll definitely be using this stuff in [insert major here].” Seriously. I think one person said they even want to go for a stats minor. That produces so much freaking glee you don’t even know, and that’s actually probably the biggest reason why I like teaching intro so much: you get to expose people to stuff they’ve never seen, and in doing so, there’s always the chance that you’ll spark an interest or fascination (Tests and Measurements did that for me, holy hell). Every time a student tells me they like the material or that they really got something out of the class I just want to jump up and down and hug them. But I don’t. Because then I would get fired for being inappropriate.
I have yet to dread going to work. Even dragging my unwilling body out of bed at 6 AM, my mind’s like, “ooh, get up you lazy fool! Today you get to teach them REGRESSION!” I think that’s the reason, too, why I don’t exhibit any of my usual public speaking anxiety when I’m teaching: I just love stats and I love talking about stats and it’s a really casual thing for me rather than being something I have to prepare for or rehearse 4,000 times. Like…I would teach for free simply because it makes me so freaking happy (but don’t tell anyone that, I need money to keep going to school, haha).
It’s teachin’ time!
These next two weeks are going to be insane, though. Teaching, finishing calc, and doing the data analysis stuff. But calc’s over in two weeks and then I have no more classes until fall.
I anticipate a weekend of solid Minecrafting once these two weeks are over.