Publications by James Hardaway
ECI 588 Unit 2 Walkthrough: Twitter Sentiment and School Reform
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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Predicting Student Achievement
knitr::opts_chunk$set(error = TRUE) 1. PREPARE During the final week of each unit, we will complete a “case study” to illustrate how Learning Analytics methods and techniques can be applied to address research questions of interest, create useful data products, and conduct reproducible research. Each case study is structured around a basic r...
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Public Sentiment & State Standards
1. PREPARE Data sources such as digital learning environments and administrative data systems, as well as data produced by social media websites and the mass digitization of academic and practitioner publications, hold enormous potential to address a range of pressing problems in education, but collecting and analyzing text-based data also presen...
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Hardaway_Unit2W6_DataProduct
library("tidyverse") install.packages("ggthemes") library(ggthemes) data_to_viz <- read_csv("data/data-to-explore.csv") filter(data_to_viz, !is.na(gender)) %>% ggplot() + geom_bar(aes(x = subject, fill = gender), width = .5, position = "dodge") + labs(title = "Course Enrollment & Gender", subtitle = "Do we still have issues getting...
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Benchmarking Fake News
Introduction Discriminating between truth and fiction is becoming increasingly difficult in the age of ubiquitous digital information. From national politics to the current COVID-19 pandemic, understanding who or what to believe impacts our lives daily. While I can still remember the days of hard copy periodicals, books, and the library’s card ...
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Case Study: Identifying At-Risk Students
1. PREPARE In Unit 2, we pick up where we left off and learn to apply some basic machine learning techniques to help us understand how a predictive model might be developed and tested as part of an early warning system to identify students at risk of failing. Specifically, we will make a very crude first attempt at developing a testing a logistic...
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ECI 589 - Unit 2 Case Study: Collaboration Ties over Time
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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Unit 2 Independent Analysis: Administrator Centrality
1. Prepare 1a. Review the Research This analysis is designed to explore a network of 43 education administrators as described in Chapter 7 of our course text, Social network analysis and education: Theory, methods & applications. The District School Leader network data set was generated through the research of Dr. Alan Daly and centers around t...
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ECI 588 - Unit 2 Independent Analysis: #Python vs #Rstats Tweets
1. PREPARE 1a. Context Two of the most widely used programming tools that have emerged in the data science field over the past twenty years are Python and R. Popularity has more than likely risen due to how both are free, open source, and relatively simple to learn (at least initially). While each language was developed for a slightly different ...
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Can Text Networks Predict Wordle Success?
0. INTRODUCTION Wordle, while not quite as popular as it was in early February, is still played enough to generate more than 150,000 daily Twitter posts by users itching to showcase their 5-letter word guessing prowess. Within some key boundaries, rules are straightforward as each game challenges a player to identify a hidden word in as few tries...
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