R/data-lecture_learning.R

In OpenIntroStat/openintro: Datasets and Supplemental Functions from 'OpenIntro' Textbooks and Labs

#' 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. Recored a binary variable with levels
#'   Male and Female in the study.}
#'   \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)
#'
#' # 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(), .groups = "drop")
#'
#' # Compare visually the differences in memory test proportions by delivery
#' # method and gender.
#' ggplot(lecture_learning, aes(x = method, y = memory, fill = gender)) +
#'   geom_boxplot() +
#'   theme_minimal() +
#'   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. Note that paired t-tests are identical
#' # to one sample t-test on the difference between the Live and Video methods.
#' learning_diff <- lecture_learning |>
#'   tidyr::pivot_wider(id_cols = student, names_from = method, values_from = memory) |>
#'   mutate(time_diff = Live - Video)
#' t.test(time_diff ~ 1, data = learning_diff)
#'
#' # 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"