generated from mwc/project_argument
26c556893a
I am currently stuck on catching up on this course. I try my best to find a time to dedicate to this course and its components. I am worried that I am still behind my peers in terms of the schedule. But I am glad that I now have finished my project. Although I wish I picked a better question or found a better dataset. The project data analysis felt a bit too easy. However, since this was already overduw, I decided that it's better to just get it done. Upon self-reflection I decided that I understand and am capable of applying the skills related to this project so no need to spend more time on this and delaying its submission.
711 lines
95 KiB
Plaintext
711 lines
95 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "worldwide-blood",
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"metadata": {},
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"source": [
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"# Introduction"
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]
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},
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{
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"cell_type": "markdown",
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"id": "understanding-numbers",
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"metadata": {},
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"source": [
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"*In my teaching residency placement, I have noticed that the students in my physics class lack the fundamental mathemetical and computational reasoning skills that they should have achieved by grade level. I have been hearing from veteran teachers that their cohorts who were in middle school during the pandemic are the most prominent victims of this situation. I want to compare the Algebra I Regents performance of cohorts that were 8th graders before, during and after the COVID-19 pandemic. I chose the anchoring grade level as 8th grade because traditional this is when the students should have been learned and practiced their fundamentals of algebra*"
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]
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},
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{
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"cell_type": "markdown",
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"id": "greater-circular",
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"metadata": {},
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"source": [
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"## Overarching Question: Has the mathemetical skills of our students decreased since COVID-19?"
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]
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},
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{
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"cell_type": "markdown",
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"id": "appreciated-testimony",
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"metadata": {},
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"source": [
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"Education was greatly impacted by the COVID-19 pandemic. With the involuntary shift to online syncronous/asyncronous schooling, we lost access to structure of the schooling that provided students better grounds to learn. I have been wondering about the impact of this ever since the transition initially happened. I think being in the school physically, greatly aids students, especially those that need accomodations, and external support (emotional, mental and academic). In addition to losing access to these, I hope that the students didn't also lose potential development to their mathematical skills, hence I asked this overarching question."
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]
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},
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{
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"cell_type": "markdown",
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"id": "permanent-pollution",
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"metadata": {},
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"source": [
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"# Data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"id": "technical-evans",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import math\n",
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"import statistics\n",
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"import csv\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"from tabulate import tabulate\n",
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"import seaborn as sns\n",
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"sns.set_theme()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "overhead-sigma",
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"\n",
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"algebra = \"algebra.csv\"\n",
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"dataset_path = \"data/\" + algebra\n",
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"\n",
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"df = pd.read_csv(dataset_path)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "heated-blade",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>2023</th>\n",
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" <th>2022</th>\n",
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" <th>2021</th>\n",
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" <th>2020</th>\n",
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" <th>2018</th>\n",
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" <th>2017</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>164</td>\n",
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" <td>264</td>\n",
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" <td>137</td>\n",
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" <td>41</td>\n",
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" <td>218</td>\n",
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" <td>256</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>26</td>\n",
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" <td>60</td>\n",
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" <td>27</td>\n",
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" <td>21</td>\n",
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" <td>23</td>\n",
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" <td>23</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>26</td>\n",
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" <td>51</td>\n",
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" <td>19</td>\n",
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" <td>10</td>\n",
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" <td>40</td>\n",
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" <td>51</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>68</td>\n",
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" <td>112</td>\n",
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" <td>67</td>\n",
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" <td>10</td>\n",
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" <td>102</td>\n",
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" <td>130</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>37</td>\n",
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" <td>20</td>\n",
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" <td>20</td>\n",
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" <td>0</td>\n",
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" <td>35</td>\n",
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" <td>35</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>5</th>\n",
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" <td>7</td>\n",
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" <td>21</td>\n",
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" <td>4</td>\n",
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" <td>0</td>\n",
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" <td>18</td>\n",
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" <td>17</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" 2023 2022 2021 2020 2018 2017\n",
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"0 164 264 137 41 218 256\n",
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"1 26 60 27 21 23 23\n",
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"2 26 51 19 10 40 51\n",
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"3 68 112 67 10 102 130\n",
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"4 37 20 20 0 35 35\n",
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"5 7 21 4 0 18 17"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df.head(6)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "continental-franklin",
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"metadata": {},
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"source": [
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"**Data Overview**\n",
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"\n",
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"*The data is taken from NYSED website. It is pulled from Cheektowaga High School's recent and archival 'School Report Card(s)' The school report cards can contain very detailed information, but the website allows the users to sort what they need. In this case, the school report card was generated on the NYSED website to present the annual Regents examinations. From these examinations, I handpicked relevant data for the Algebra examination. \n",
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"\n",
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"The header row reperesents the **year** of the examination. Since Algebra one is the fundamental math examination in the Regents standards, we will make the assumption that the year also represents the high school enterance cohort year for *most, if not all* of the students.\n",
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"- The first two cohorts (2023 & 2022) are considered as post-COVID-19 cohorts as they were introduced to fundamentals of Algebra in 8th grade, a year prior to their cohort entry as tabulated.\n",
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"- The following two cohorts (2021 & 2020) are considered to be the COVID-19 cohorts, as they were in 8th grade *during* the pandemic years.\n",
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"- The last two cohorts (2018 & 2017) are considered to be the pre-COVID-19 cohorts. \n",
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"\n",
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"The 0th data row represents the **total number of students who took the Algebra Regents**.\\\n",
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"The 1st data row represents the **number of students who performed at Level 1 (lowest level).**\\\n",
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"The 2nd data row represents the **number of students who performed at Level 2.**\\\n",
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"The 3rd data row represents the **number of students who performed at Level 3.**\\\n",
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"The 4th data row represents the **number of students who performed at Level 4.**\\\n",
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"The 5th data row represents the **number of students who performed at Level 5 (highest level).**\n",
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"\n",
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"*Regents defines proficiency in Algebra as performed at Level 3 or above*. This categorization will inform our data analysis. \n",
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"*"
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]
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},
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{
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"cell_type": "markdown",
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"id": "infinite-instrument",
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"metadata": {},
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"source": [
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"# Methods and Results"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "basic-canadian",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"id": "recognized-positive",
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"metadata": {},
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"source": [
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"## First Research Question:\n",
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"## How has COVID-19 pandemic impacted student's matehmetical skills?: Exploring the high school Algebra Regents examination proficiency of post, during and pre COVID-19 middle schoolers.\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "graduate-palmer",
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"metadata": {},
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"source": [
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"### Methods"
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]
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},
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{
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"cell_type": "markdown",
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"id": "endless-variation",
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"metadata": {},
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"source": [
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"*Explain how you will approach this research question below. Consider the following:* \n",
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" - *Which aspects of the dataset will you use?* \n",
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" - *How will you reorganize/store the data?* \n",
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" - *What data science tools/functions will you use and why?* \n",
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" \n",
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"✏️ *Write your answer below:*\n",
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"\n",
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"- I will use the rows 1 & 2 for this part as these are the rows corresponding to below proficient levels.\n",
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"- I will add these rows together for each given year to look at the total number per cohort.\n",
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"- I will use a bar plot to compare and contrast the numbers per cohort. Bar plot would be a best graphical tool for this as these numbers belong to cohorts and do not have a relationship with another variable. \n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "portuguese-japan",
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"metadata": {},
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"source": [
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"### Results "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"id": "61bcbc93-b3bc-4e47-a978-1b5e9808536e",
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Number of students who performed below proficiency in Algebra I Regents in the given years:\n",
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"\n"
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]
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},
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>2023</th>\n",
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" <th>2022</th>\n",
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" <th>2021</th>\n",
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" <th>2020</th>\n",
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" <th>2018</th>\n",
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" <th>2017</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>52</td>\n",
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" <td>111</td>\n",
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" <td>46</td>\n",
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" <td>31</td>\n",
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" <td>63</td>\n",
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" <td>74</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" 2023 2022 2021 2020 2018 2017\n",
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"0 52 111 46 31 63 74"
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]
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},
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"execution_count": 27,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"blwprof=df.iloc[[1,2]].sum()\n",
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"below= pd.DataFrame([blwprof])\n",
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"print('Number of students who performed below proficiency in Algebra I Regents in the given years:\\n')\n",
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"below.head(6)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 34,
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"id": "negative-highlight",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[Text(0.5, 1.0, 'Number of students who performed below proficiency in Algebra I Regents in the given years')]"
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]
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},
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"execution_count": 34,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": 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",
|
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"text/plain": [
|
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"<Figure size 640x480 with 1 Axes>"
|
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]
|
|
},
|
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"metadata": {},
|
|
"output_type": "display_data"
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}
|
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],
|
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"source": [
|
|
"sns.barplot(data=below, errorbar=None, palette=\"bright\").set(title=\"Number of students who performed below proficiency in Algebra I Regents in the given years\")"
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|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "79b00ae0-4388-4345-acf9-5fcd9bfad9ab",
|
|
"metadata": {},
|
|
"source": [
|
|
"Although this bar plot shows us that the number of students who performed **below** proficiency skyrocketed immediately, this alone does not give us a definitive answer to our initial investigation as the number of students who took the examination also changed over the years. Hence we will explore a second research question that deepens our investigation and takes th"
|
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]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "6c2e8a11-e7d9-40de-ae6c-a27d77438eb7",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Second Research Question:\n",
|
|
"## What percentage of the post, during and pre COVID-19 middle schoolers are failed to reach proficiency in Alegebra I in their transition to high-school? ##"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "demographic-future",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Methods"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "incorporate-roller",
|
|
"metadata": {},
|
|
"source": [
|
|
"*Explain how you will approach this research question below. Consider the following:* \n",
|
|
" - *Which aspects of the dataset will you use?* \n",
|
|
" - *How will you reorganize/store the data?* \n",
|
|
" - *What data science tools/functions will you use and why?* \n",
|
|
"\n",
|
|
"✏️ *Write your answer below:*\n",
|
|
"\n",
|
|
"- I will use the data frame that I created in the previous section that showed me the total number of below proficiency students per cohort. I will additionally use the row 0 from the original data frame which represents the total number of students who took the Algebra I Regents each year. \n",
|
|
"- I want to now compare the number with the overall percentage. So I will use the data that I mentioned in the previous answer to create the percentage barplot.\n",
|
|
"- Similar to the first part I will create a barplot to compare and contrast the percentages per cohort. Additionally, I will overlap the first barplot with the second to see if there is another layer of connection (such as percentage increase or decrease with number of below proficiency)\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "juvenile-creation",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Results "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 30,
|
|
"id": "pursuant-surrey",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div>\n",
|
|
"<style scoped>\n",
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|
" vertical-align: middle;\n",
|
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" }\n",
|
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"\n",
|
|
" .dataframe tbody tr th {\n",
|
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" vertical-align: top;\n",
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" }\n",
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"\n",
|
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" .dataframe thead th {\n",
|
|
" text-align: right;\n",
|
|
" }\n",
|
|
"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>2023</th>\n",
|
|
" <th>2022</th>\n",
|
|
" <th>2021</th>\n",
|
|
" <th>2020</th>\n",
|
|
" <th>2018</th>\n",
|
|
" <th>2017</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>31.7</td>\n",
|
|
" <td>42.0</td>\n",
|
|
" <td>33.6</td>\n",
|
|
" <td>75.6</td>\n",
|
|
" <td>28.9</td>\n",
|
|
" <td>28.9</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" 2023 2022 2021 2020 2018 2017\n",
|
|
"0 31.7 42.0 33.6 75.6 28.9 28.9"
|
|
]
|
|
},
|
|
"execution_count": 30,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"perc=(100*below)/df.iloc[[0]]\n",
|
|
"belowpercentage=pd.DataFrame(perc).round(1)\n",
|
|
"belowpercentage.head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 33,
|
|
"id": "located-night",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"[Text(0.5, 1.0, 'Percentage of students who performed below proficiency in Algebra I Regents in the given years')]"
|
|
]
|
|
},
|
|
"execution_count": 33,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"sns.barplot(data=belowpercentage, errorbar=None, palette=\"pastel\").set(title=\"Percentage of students who performed below proficiency in Algebra I Regents in the given years\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "c84c3319-efa4-43b6-96aa-6be0d2e377e0",
|
|
"metadata": {},
|
|
"source": [
|
|
"We can overlap these bar graph to compare and contrast. "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 38,
|
|
"id": "da766429-dfc3-4418-b81f-16284cd7f3e2",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<Axes: >"
|
|
]
|
|
},
|
|
"execution_count": 38,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"overlap=plt.subplots()\n",
|
|
"sns.barplot(data=belowpercentage, errorbar=None, palette=\"pastel\")\n",
|
|
"sns.barplot(data=below, errorbar=None, alpha=0.5, palette=\"bright\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "infectious-symbol",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Discussion"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "furnished-camping",
|
|
"metadata": {
|
|
"code_folding": []
|
|
},
|
|
"source": [
|
|
"## Considerations"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "bearing-stadium",
|
|
"metadata": {},
|
|
"source": [
|
|
"*It's important to recognize the limitations of our research.\n",
|
|
"Consider the following:*\n",
|
|
"\n",
|
|
"- *Do the results give an accurate depiction of your research question? Why or why not?*\n",
|
|
"- *What were limitations of your datset?*\n",
|
|
"- *Are there any known biases in the data?*\n",
|
|
"\n",
|
|
"✏️ *Write your answer below:*\n",
|
|
"- I think that it sort of does! I can see the number of students who remained below proficient in their math skills as they trainsitioned from middle school to high school and I could also cross-analyze this number as a percentage of their cohort.\n",
|
|
"- The main limitation is that standardized tests might not paint the best picture for students' capabilities. Although they are supposed to set the bar and provide a fair comparison of achievement across all schools, they do not represent the full reality of the school districts, students' overall performance and math skills.\n",
|
|
"- I don't think there are biases in the data set on its own as it is a data pulled directly from the NYSED and it represents a standardized test data. "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "beneficial-invasion",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Summary"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "about-raise",
|
|
"metadata": {},
|
|
"source": [
|
|
"*Summarize what you discovered through the research. Consider the following:*\n",
|
|
"\n",
|
|
"- *What did you learn about your media consumption/digital habits?*\n",
|
|
"- *Did the results make sense?*\n",
|
|
"- *What was most surprising?*\n",
|
|
"- *How will this project impact you going forward?*\n",
|
|
"\n",
|
|
"✏️ *Write your answer below:*\n",
|
|
"- I am not really sure how this related to my media consumption or digital habits. I think maybe it helped me piecing different data sets together.\n",
|
|
"- I think the results made sense. Although not very pronounced, there is an increase of percentage in students that performed below proficiency. The skyrocketing percentage increase in 2020 also makes a lot of sense considering this was the transitioning year and students did not get quality education as the districts struggled to navigate through the transition, keeping structure and motivation afloat.\n",
|
|
"- I am surprised that the students were not performing better pre-COVID-19. This kind of comes back to the limitation of this research: singular Regents examination doesn't paint the most comprehensive picture of students' skills. I was expercting to see a more pronounced difference in performance pre and post COVID-19.\n",
|
|
"- I think getting to practice data analysis in python helped me refresh my skills. I am more used to just using tabulate and plt, so seaborn is new to me but I enjoyed playing with it. In terms of the research itself, I think I will be questioning what exactly it is that's currently failing our students in their math skills, if it isn't the lasting impacts of COVID-19. "
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]
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