Simulated binary time-series data for 200 students across 30 time points. At each time point, one or more learning activities may be active (1) or inactive (0). Activities: Reading, Video, Forum, Quiz, Coding, Review. Includes temporal persistence (activities tend to continue across adjacent time points).
Format
A data frame with 6,000 rows and 7 columns:
- student
Integer. Student identifier (1–200).
- Reading
Integer (0/1). Reading activity indicator.
- Video
Integer (0/1). Video watching indicator.
- Forum
Integer (0/1). Discussion forum indicator.
- Quiz
Integer (0/1). Quiz/assessment indicator.
- Coding
Integer (0/1). Coding practice indicator.
- Review
Integer (0/1). Review/revision indicator.
Examples
# \donttest{
net <- build_network(learning_activities, method = "cna",
actor = "student", codes = c("Reading", "Video",
"Forum", "Quiz", "Coding", "Review"), window_size = 3)
net
#> Co-occurrence Network [undirected]
#> Weights: [2681.000, 3290.000] | mean: 3047.333
#>
#> Weight matrix:
#> Reading Video Forum Quiz Coding Review
#> Reading 6100 3169 3211 2891 3138 3113
#> Video 3169 6262 3183 2903 3278 3290
#> Forum 3211 3183 5692 2942 2958 3045
#> Quiz 2891 2903 2942 5379 2681 2725
#> Coding 3138 3278 2958 2681 5886 3183
#> Review 3113 3290 3045 2725 3183 6186
# }