Students' regulation strategies during collaborative learning, in long format. Contains 27,533 timestamped action records from multiple students working in groups across two courses.
Format
A data frame with 27,533 rows and 6 columns:
- Actor
Integer. Student identifier.
- Achiever
Character. Achievement level:
"High"or"Low".- Group
Numeric. Collaboration group identifier.
- Course
Character. Course identifier (
"A","B", or"C").- Time
POSIXct. Timestamp of the action.
- Action
Character. Regulation action (e.g., cohesion, consensus, discuss, synthesis).
Examples
# \donttest{
# Build a transition network per actor
net <- build_network(group_regulation_long,
method = "relative",
actor = "Actor", action = "Action", time = "Time")
net
#> Transition Network (relative probabilities) [directed]
#> Weights: [0.001, 0.498] | mean: 0.116
#>
#> Weight matrix:
#> adapt cohesion consensus coregulate discuss emotion monitor plan
#> adapt 0.000 0.273 0.477 0.022 0.059 0.120 0.033 0.016
#> cohesion 0.003 0.027 0.498 0.119 0.060 0.116 0.033 0.141
#> consensus 0.005 0.015 0.082 0.188 0.188 0.073 0.047 0.396
#> coregulate 0.016 0.036 0.135 0.023 0.274 0.172 0.086 0.239
#> discuss 0.071 0.048 0.321 0.084 0.195 0.106 0.022 0.012
#> emotion 0.002 0.325 0.320 0.034 0.102 0.077 0.036 0.100
#> monitor 0.011 0.056 0.159 0.058 0.375 0.091 0.018 0.216
#> plan 0.001 0.025 0.290 0.017 0.068 0.147 0.076 0.374
#> synthesis 0.235 0.034 0.466 0.044 0.063 0.071 0.012 0.075
#> synthesis
#> adapt 0.000
#> cohesion 0.004
#> consensus 0.008
#> coregulate 0.019
#> discuss 0.141
#> emotion 0.003
#> monitor 0.016
#> plan 0.002
#> synthesis 0.000
#>
#> Initial probabilities:
#> consensus 0.214 ████████████████████████████████████████
#> plan 0.204 ██████████████████████████████████████
#> discuss 0.175 █████████████████████████████████
#> emotion 0.151 ████████████████████████████
#> monitor 0.144 ███████████████████████████
#> cohesion 0.060 ███████████
#> synthesis 0.019 ████
#> coregulate 0.019 ████
#> adapt 0.011 ██
# Group networks by achievement level
nets <- build_network(group_regulation_long,
method = "relative",
actor = "Actor", action = "Action", time = "Time",
groups = "Achiever")
nets
#> Transition Network (relative probabilities) [directed]
#> Weights: [0.001, 0.498] | mean: 0.116
#>
#> Weight matrix:
#> adapt cohesion consensus coregulate discuss emotion monitor plan
#> adapt 0.000 0.273 0.477 0.022 0.059 0.120 0.033 0.016
#> cohesion 0.003 0.027 0.498 0.119 0.060 0.116 0.033 0.141
#> consensus 0.005 0.015 0.082 0.188 0.188 0.073 0.047 0.396
#> coregulate 0.016 0.036 0.135 0.023 0.274 0.172 0.086 0.239
#> discuss 0.071 0.048 0.321 0.084 0.195 0.106 0.022 0.012
#> emotion 0.002 0.325 0.320 0.034 0.102 0.077 0.036 0.100
#> monitor 0.011 0.056 0.159 0.058 0.375 0.091 0.018 0.216
#> plan 0.001 0.025 0.290 0.017 0.068 0.147 0.076 0.374
#> synthesis 0.235 0.034 0.466 0.044 0.063 0.071 0.012 0.075
#> synthesis
#> adapt 0.000
#> cohesion 0.004
#> consensus 0.008
#> coregulate 0.019
#> discuss 0.141
#> emotion 0.003
#> monitor 0.016
#> plan 0.002
#> synthesis 0.000
#>
#> Initial probabilities:
#> consensus 0.214 ████████████████████████████████████████
#> plan 0.204 ██████████████████████████████████████
#> discuss 0.175 █████████████████████████████████
#> emotion 0.151 ████████████████████████████
#> monitor 0.144 ███████████████████████████
#> cohesion 0.060 ███████████
#> synthesis 0.019 ████
#> coregulate 0.019 ████
#> adapt 0.011 ██
# }