Simulated frequency counts of 9 self-regulated learning (SRL) strategies for 250 university students. Strategies are grouped into three clusters: metacognitive (Planning, Monitoring, Evaluating), cognitive (Elaboration, Organization, Rehearsal), and resource management (Help_Seeking, Time_Mgmt, Effort_Reg). Within-cluster correlations are moderate (0.3–0.6), cross-cluster correlations are weaker.
Format
A data frame with 250 rows and 9 columns. Each column is an integer count of how often the student used that strategy.
Examples
# \donttest{
net <- build_network(srl_strategies, method = "glasso",
params = list(gamma = 0.5))
net
#> Partial Correlation Network (EBICglasso) [undirected]
#> Sample size: 250
#> Weights: [0.089, 0.413] | +13 / -0 edges
#>
#> Weight matrix:
#> Planning Monitoring Evaluating Elaboration Organization Rehearsal
#> Planning 0.000 0.295 0.161 0.000 0.000 0.000
#> Monitoring 0.295 0.000 0.361 0.105 0.000 0.000
#> Evaluating 0.161 0.361 0.000 0.000 0.221 0.000
#> Elaboration 0.000 0.105 0.000 0.000 0.329 0.228
#> Organization 0.000 0.000 0.221 0.329 0.000 0.218
#> Rehearsal 0.000 0.000 0.000 0.228 0.218 0.000
#> Help_Seeking 0.000 0.000 0.000 0.000 0.000 0.000
#> Time_Mgmt 0.205 0.000 0.000 0.000 0.000 0.000
#> Effort_Reg 0.000 0.000 0.161 0.000 0.000 0.000
#> Help_Seeking Time_Mgmt Effort_Reg
#> Planning 0.000 0.205 0.000
#> Monitoring 0.000 0.000 0.000
#> Evaluating 0.000 0.000 0.161
#> Elaboration 0.000 0.000 0.000
#> Organization 0.000 0.000 0.000
#> Rehearsal 0.000 0.000 0.000
#> Help_Seeking 0.000 0.141 0.089
#> Time_Mgmt 0.141 0.000 0.413
#> Effort_Reg 0.089 0.413 0.000
#>
#> Gamma: 0.50 | Lambda: 0.1319
# }