R/data-lecture_learning.R

In npaterno/data_hunter: Data set repository for OpenIntro textbooks

#' Lecture Delivery Method and Learning Outcomes
#' 
#' Data was collected from 276 students in a university psychology course
#' to determine the effect of lecture delivery method on learning. Students were
#' presented a live lecture by the professor on one day and a pre-recorded 
#' lecture on a different topic by the same professor on a different day. 
#' Survey data was collected during the lectures to determine mind wandering,
#' interest, and motivation.  Students were also ultimately asked about the
#' preferred lecture delivery method. Finally, students completed an assessment
#' at the end of the lecture to determine memory recall.    
#' 
#' 
#' @format A data frame with 552 rows and 8 variables.
#' \describe{
#'   \item{student}{Identification number of a specific student. 
#'   Each identification appears twice because same student heard both lecture 
#'   delivery methods.}
#'   \item{gender}{Gender of student.}
#'   \item{method}{Delivery method of lecture was either in-person(Live) or 
#'   pre-recorded(Video).}
#'   \item{mindwander}{An indicator of distraction during the lecture. It is a 
#'   proportion of six mind wandering probes during the lecture when a student 
#'   answered yes that mind wandering had just occurred.}
#'   \item{memory}{An indicator of recall of information provided during the 
#'   lecture. It is the proportion of correct answers in a six question assessment 
#'   given at the end of the lecture presentation.}
#'   \item{interest}{A Likert scale that gauged student interest level concerning
#'  the lecture.}
#'   \item{motivation_both}{After experiencing both lecture delivery methods, 
#'   students were asked about which method they were most motivated to remain 
#'   attentive.}
#'   \item{motivation_single}{After a single lecture delivery experience, this 
#'   Likert scale was used to gauge motivation to remain attentive during the 
#'   lecture.}
#' }
#' @examples
#' library(dplyr)
#' library(ggplot2)
#' library(openintro)
#' 
#' # Calculate the average memory test proportion by lecture delivery method 
#' # and gender.
#' lecture_learning %>%
#'  group_by(method, gender) %>%
#'  summarize(average_memory = mean(memory), count = n())
#'
#' # Compare visually the differences in memory test proportions by delivery 
#' # method and gender.
#' ggplot(data = lecture_learning, aes(x = method, y = memory, fill = gender)) +
#'  geom_boxplot() + 
#'  geom_jitter() +
#'  theme_minimal() +
#'  scale_fill_manual(values = c(IMSCOL["blue", "full"], IMSCOL["pink", "full"])) +
#'  labs(
#'    title = "Difference in memory test proportions",
#'    x = "Method",
#'    y = "Memory", 
#'    fill = "Gender"
#'    )
#' 
#' # Use a paired t-test to determine whether memory test proportion score 
#' # differed by delivery method.
#' t.test(memory ~ method, data = lecture_learning, paired = TRUE)
#' 
#' # Calculating the proportion of students who were most motivated to remain 
#' # attentive in each delivery method.
#' lecture_learning %>% 
#'  count(motivation_both) %>% 
#'  mutate(proportion = n / sum(n))
#'
#' @source [PLOS One](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0141587)
#'
"lecture_learning"