Blog Post 1: Spring 2022

It was really interesting reading these articles and seeing the video. I actually saw the video with Carol Dweck last semester, and it really stuck with me. I've used her examples in discussions and debates about the school system and the difference it makes to rewards kids for the process they used to solve a problem, rather than the result.

I feel like I mostly have a growth mindset. I've mentioned this many times in short essays and discussion board posts; I grew up struggling terribly in math. I was always a C and D student, and I thought "I'm just not good at it. There's nothing I can do." Even less than 10 years ago, I always said to myself "I can't go into a field that requires lots of math. I wouldn't make it. I'm just not good at it, and I hate it." However, five years ago I decided I wanted to go into cyber security. I don't know what changed in me, but I decided "I might struggle in math, but I can figure it out." Of course, math didn't come naturally. I have put in a lot of hard work and dedication into my college career to learn the material in every class. I never left a math problem unfinished, and got 100% or nearly 100% on all my homework in my math/math related classes. My success and math intelligence has come from a lot of practice and hard work, and I do thank having a growth mindset for this success.

"Problems in the pipeline: Stereotype threat and women's achievement in high-level math courses" by Good, Aronson and Harder really hit home for me. Although I wasn't told "women aren't as good as men at math" growing up, I was always told "you just struggle in math, it's ok. Some people just aren't good at math." After learning about growth and fixed mindsets, and about how sex stereotypes affect women's math performance, if I would have had the same experience with math growing up if I was never told "you're just not good at math." I didn't realize until 4 years ago that I was actually good at math. It took me 24 years to realize I'm not "just bad at math."

Now I am struggling in coding, and I feel like I have more of a fixed mindset with this one. I feel like no matter how much I tell myself "I just need to practice, I can learn this" I still feel like I don't belong, and I feel like I am simply not good at coding. However, I am continuing to push on and I don't plan on giving up. I think my mindset on this probably is negatively affecting my performance. However, it is also pushing me to put a little extra time in on the side to learn to code, because I really want to learn it and finally feel like I belong in this field.

My college journey has given me even more confidence that I can learn coding, and that I do belong in my field. It feels good being reassured that if I practice and see a learning opportunity in my failures, that I can learn anything.

Comments

Post a Comment

Popular posts from this blog

How I Think I Want to Use My Degree and Why

 This is a tough question for me, because lately I've been jumping around between career ideas.  My major is Applied Computing with a concentration in cybersecurity. I would like to work in cybersecurity, but with Applied Computing being so broad, I'll be qualified for all kinds of computing paths such as machine learning, software engineering, and (as I recently found out from a job ad) even some bioinformatics positions. I have the most interest in machine learning, bioinformatics, and cybersecurity. If I'm being honest, I don't necessarily have the have the cybersecurity concentration because I for sure want to go into cyber security. I just want to give myself more options since the cybersecurity field is extremely desperate for workers right now. If I end up with a job in cybersecurity after school, then that's great. And if not, I'm also ok with that.  A company that I would specifically like to work for one day is Microsoft, however, I couldn't say wh...
Read more
Image
 Hello! This week I read Chapter 4 in  Data Visualization of R by Rob Kabacoff.  Last week I read Chapter 3 on univariate graphs, which plot data about a single variable. Chapter 4 is about bivariate graph, which are used to show the relationship between 2 variables. The type of graph used depends on whether the variables are categorical or quantitative. Categorical vs. Categorical Much like a univariate graph, a bivariate graph typically uses various types of bar charts to display categorical data. There are 3 different types of bar charts used, and I'll display pictures of them all. This time, I am not going to go into detail about modifications such as color, size, etc. because it is the same as for univariate graphs. The data set used displays the relationship between automobile class and drive type: 4 wheel drive, front-wheel drive, and rear-wheel drive. Stacked bar chart The stacked bar chart is the default bar chart in R, so there are no special commands to use thi...
Read more
Image
 Yay! The district's computer system is back up, so I can access the blog from my computer again. This week I read about and practiced Univariate Graphs. This variable can be categorical or quantitative. A categorical variable is something such as race or sex, is usually plotted using a bar graph, pie graph, or tree map. A quantitative variable is something like age or height, and is usually plotted using a histogram, kernel density plot or dot plot. Both categorical and quantitative variables use the ggplot2 function, which I used last week. I followed along with the book, and started out by following the examples to demonstrate a categorical variable. The dataset being used is The Marriage dataset that has the records of 98 couples in Mobile County, Alabama.  Categorical Variable Graphs Bar Graph The first graph demonstrated is a bar graph. Just like with the graph from my last blog, you can change and edit everything about the graph including color, using percent symbols fo...
Read more