A long, one-row-per-event log of collaborative regulation moves, with
timestamps. Used to demonstrate long-format loading: reshape with
prepare_input() (or name action = in
context_tree()) to split each actor's events into
time-gap sessions. Bundled example dataset.
Format
A data.frame with 27533 rows and 6 columns:
- Actor
integer; the learner.
- Achiever
character; an achievement-level covariate.
- Group
numeric; the collaboration group.
- Course
character; the course.
- Time
POSIXct; the event timestamp.
- Action
character; the regulation move (the state).
Examples
data(group_regulation_long)
context_tree(group_regulation_long, actor = "Actor", time = "Time",
action = "Action", max_depth = 3L)
#> <transitiontrees> 471 nodes, depth <= 3, 9 states [unpruned]
#> alphabet : adapt, cohesion, consensus, coregulate, discuss, emotion, monitor, plan, synthesis
#> fit on : 2000 sequences, 27533 observations
#> smoothing: floor(ymin=0.001, rule=interpolate) min_count = 5
#> (start) n=27533 -> consensus (0.25)
#> |-- adapt n=509 -> consensus (0.47)
#> | |-- consensus n=27 -> cohesion (0.48)
#> | | |-- discuss n=6 -> consensus (0.66)
#> | | `-- plan n=7 -> consensus (0.71)
#> | |-- coregulate n=28 -> consensus (0.50)
#> | | `-- consensus n=21 -> consensus (0.43)
#> | |-- discuss n=259 -> consensus (0.47)
#> | | |-- adapt n=6 -> cohesion (0.50)
#> | | |-- cohesion n=8 -> consensus (0.37)
#> | | |-- consensus n=60 -> consensus (0.53)
#> | | |-- coregulate n=37 -> consensus (0.43)
#> | | |-- discuss n=48 -> consensus (0.52)
#> | | |-- emotion n=14 -> consensus (0.50)
#> | | |-- monitor n=25 -> consensus (0.40)
#> | | `-- plan n=26 -> consensus (0.42)
#> | |-- emotion n=6 -> consensus (0.83)
#> | |-- monitor n=16 -> consensus (0.50)
#> | | `-- plan n=5 -> cohesion (0.40)
#> | |-- plan n=6 -> consensus (0.66)
#> | `-- synthesis n=140 -> consensus (0.48)
#> | |-- consensus n=8 -> cohesion (0.37)
#> | |-- coregulate n=5 -> consensus (0.60)
#> | |-- discuss n=107 -> consensus (0.45)
#> | `-- monitor n=6 -> consensus (0.66)
#> |-- cohesion n=1695 -> consensus (0.50)
#> ... 445 more nodes (use as.data.frame(x) or summary(x))