The WD statistic is more useful for differentiating between simulated learner types when games are played longer¶

Claim · All claims
Evidence · 1 study · causal r2 · q2
Currency · newest study 2012 · not yet checked against its sources · not reviewed by a person · overdue for a recheck since 2015: tool and model accuracy changes quickly

Subclaims¶

q2 i? WD differentiates between learner types more usefully when games are played longer, because relative differences between learner types become more pronounced over time as more evidence about learner differences accumulates. → Sweet 2012

Evidence¶

Sweet 2012¶

Sweet, S. J., & Rupp, A. A. (2012). Using the ECD Framework to Support Evidentiary Reasoning in the Context of a Simulation Study for Detecting Learner Differences in Epistemic Games. Journal of Educational Data Mining, Special Issue, Article 5, Volume 4. doi:10.5281/zenodo.3554649

q2 · i? · causal · r2

Median WD trajectories for 21 learner types across 100 replications in design matrix 1 games show "the WD is more useful for differentia ting between learner types when games are played longer" as evidence about learner differences accumulates.

"Figure 3 also shows that the WD is more useful for differentia ting between learner types when games are played longer bec ause relative differences become more pronounced over time (i.e. more evidence about learner differences can be accumulated)."

Discussion¶

Related Claims¶

• Defining and quantifying reliability and measurement error is particularly challenging in epistemic games for three principal reasons — the general claim this finding bears on, with the studies behind it
• Easy, highly specific simulated tasks compress the range of the WD statistic across learner types, while well-designed tasks widen it — related
• In simulated epistemic games, the weighted density statistic distinguishes simulated learner types with distinct mastery trajectories, with the expert trajectory showing the largest WD values — related
• In pairwise WD analyses, learner trajectory similarity dominates variation in percentage-overlap values (57.90% of variation) while remaining design factors are essentially zero — related
• Segmentation boundary choices differentially affect statistics computed on epistemic-game process data — related
• In the simulation, task complexity and task difficulty explain the majority of variance in the individual-learner WD statistic, with some effect of task specificity — related