Publications by Duncan Culbreth
ECI 586 - Duncan Culbreth's Final Project
1. PREPARE 1a. Reviewed Literature There has been some initial research how sentiment might affect learning in online forums (Kagklis et al, 2015), as well as how text mining could reveal some of the discursive processes within Reddit communities (Mueller, 2016; White, 2019). I want to build on these lines of inquiry by seeing how one particular...
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Grade Distribution by Subject & Gender
library(tidyverse) data_to_viz <- read_csv("data/data-to-explore.csv") data_to_viz %>% select(subject, section, time_spent_hours, proportion_earned, gender) %>% mutate(subject = recode(subject, "AnPhA" = "Anat", "BioA" = "Bio", "F...
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SNA Unit 2 Independent Analysis
Unit 2 Independent Analysis: Fraternity Network I was interested in (Carolan 2014)’s usage of Newcomb’s fraternity data in Chapter 4, so I decided to run my own analysis of the data, namely using some of the visualization techniques that we used in our Unit 2 case study. My research question, which has two sub-questions, is as follows: In th...
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SNA Unit 2 Case Study
1. Prepare Our Unit 2 Case Study: Collaboration Ties over Time revisits the research of Dr. Alan Daly and centers around the impact No Child Left Behind reform efforts on school and district leadership networks. In this unit we move beyond visual depictions of networks from our previous SNA case study and learn to describe networks using common ...
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Text Mining Unit 2 Walkthrough
0. INTRODUCTION This week, our walkthrough is guided by my colleague Josh Rosenberg’s recent article, Advancing new methods for understanding public sentiment about educational reforms: The case of Twitter and the Next Generation Science Standards. We will focus on conducting a very simplistic “replication study” by comparing the sentiment ...
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Unit 2 Independent Analysis: Yelp During Pandemic
Unit 2 Independent Analysis: Yelp During Pandemic Introduction Looking through Yelp’s free datasets, I recently came about a special dataframe that they released specifically for how some businesses adapted their Yelp pages in response to the COVID-19 pandemic. Variables included a deidentified codename for each business, several boolean indic...
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SNA Unit 4 Independent Analysis
Intro We’re back at it again with our district leader data from the Social Network Analysis and Education companion site! I was interested in seeing the relationship between some factors that we didn’t see in combination during our case study: Reciprocal ties between actors, i.e. confidential and mutual exchanges between individuals, whether...
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Text Mining Unit 4 Independent Analysis
Intro We’re back at it again with the Yelp COVID dataset! With our tokenization to include bigrams, as well as examining the affordances of text networks made my existing set of banner text a natural choice for this independent analysis. As a quick recap, the main target of my inquiry has been the “COVID Banner”, a special open-ended text a...
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SNA Unit 3 Independent Analysis
Prepare Introduction Hey folks! I decided to take one of the other “default” datasets for this analysis: Pittinsky’s Middle School Science Classroom Friendship Nominations. As someone who has taught in a classroom before (albeit at the college level), I was interested in the student vs. teacher perspective on a small social network. My re...
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Text Mining Unit 3 Independent Analysis
Prepare In my previous independent analysis, I attempted to analyze the sentiment of various messages given out by businesses’ Yelp profiles during peak lockdown due to COVID-19. The data was collected from “covid banners”, which were special spaces on a given business page that could be used for anything they deemed relevant to how their b...
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