Shonn Cheng ☕️
Shonn Cheng

Assistant Professor

National Taipei University of Technology

About Me

Dr. Shonn Cheng received his Ph.D. in Educational Studies at The Ohio State University (USA) in summer 2019. Thereafter, he pursued one-year postdoctoral training at Virginia Commonwealth University (USA). During 2020-2022, he worked as an Assistant Professor in the Department of Instructional Systems Design and Technology at Sam Houston State University (USA). Currently, Dr. Cheng is an Assistant Professor in the Graduate Institute of Technological and Vocational Education at National Taipei University of Technology (Taiwan) and the Director of the META Lab.

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Interests
  • Motivation
  • Expertise
  • Training with Technology
  • Analysis
Education

  • PhD Educational Studies

    The Ohio State University

  • MA Educational Studies

    The Ohio State University

  • MA Education

    New Mexico State University

  • BA English

    Wenzao Ursuline University of Languages

  • AMS Business Administration

    National Taipei University of Business

Recent Publications
Shonn Cheng (2024). Teaching with technology requires high expectancies and high values. Education and Information Technologies.
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Blog
R

When `reorder` Fails

I am using the mpg dataset included in the ggplot2 package. #load packages library(tidyverse) library(tidytext) #view data mpg ## Rows: 234 ## Columns: 11 ## $ manufacturer <chr> "audi", "audi", "audi", "audi", "audi", "audi", "audi", "… ## $ model <chr> "a4", "a4", "a4", "a4", "a4", "a4", "a4", "a4 quattro", "… ## $ displ <dbl> 1.8, 1.8, 2.0, 2.0, 2.8, 2.8, 3.1, 1.8, 1.8, 2.0, 2.0, 2.… ## $ year <int> 1999, 1999, 2008, 2008, 1999, 1999, 2008, 1999, 1999, 200… ## $ cyl <int> 4, 4, 4, 4, 6, 6, 6, 4, 4, 4, 4, 6, 6, 6, 6, 6, 6, 8, 8, … ## $ trans <chr> "auto(l5)", "manual(m5)", "manual(m6)", "auto(av)", "auto… ## $ drv <chr> "f", "f", "f", "f", "f", "f", "f", "4", "4", "4", "4", "4… ## $ cty <int> 18, 21, 20, 21, 16, 18, 18, 18, 16, 20, 19, 15, 17, 17, 1… ## $ hwy <int> 29, 29, 31, 30, 26, 26, 27, 26, 25, 28, 27, 25, 25, 25, 2… ## $ fl <chr> "p", "p", "p", "p", "p", "p", "p", "p", "p", "p", "p", "p… ## $ class <chr> "compact", "compact", "compact", "compact", "compact", "c… mpg %>% group_by(trans) %>% count() %>% ungroup() %>% ggplot(mapping = aes(trans, n)) + geom_bar(stat = "identity")

R

Create Dummy Variables

Load Packages library(fastDummies) library(tidyverse) library(psych) Create a DataSet # Create a vector of race scores race <- c("White", "Black", "Asian", "Hispanic", "Other") # Generate random income values for each race (100 cases) set.seed(123) # for reproducibility income <- round(runif(100, min = 20000, max = 100000), digits = 2) # Repeat each race 20 times to get 100 cases race <- rep(race, each = 20) # Combine race and income into a data frame data <- data.frame(race, income) # Print the first few rows of the dataset print(head(data)) ## race income ## 1 White 43006.20 ## 2 White 83064.41 ## 3 White 52718.15 ## 4 White 90641.39 ## 5 White 95237.38 ## 6 White 23644.52 Create Dummy Variables data<-data %>% dummy_cols(select_columns = "race") Regress Income on Race (African Americans as the Reference Category) fit<-lm(income ~ race_Asian + race_Hispanic + race_Other + race_White, data=data) summary(fit) ## ## Call: ## lm(formula = income ~ race_Asian + race_Hispanic + race_Other + ## race_White, data = data) ## ## Residuals: ## Min 1Q Median 3Q Max ## -44169 -19531 -1137 18010 40481 ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 66138 5066 13.055 <2e-16 *** ## race_Asian -15015 7165 -2.096 0.0388 * ## race_Hispanic -7004 7165 -0.977 0.3308 ## race_Other -7173 7165 -1.001 0.3193 ## race_White -2073 7165 -0.289 0.7730 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## Residual standard error: 22660 on 95 degrees of freedom ## Multiple R-squared: 0.05237, Adjusted R-squared: 0.01247 ## F-statistic: 1.313 on 4 and 95 DF, p-value: 0.2709

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