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¶

Claim · All claims
Evidence · 1 study (2 entries) · 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? The expert learner trajectory, with perfect mastery of all SKIVE elements throughout the game, shows the largest WD statistic values throughout the game in design matrix 1 games. → Sweet 2012 q2 i? Boxplots of the WD statistic for six prototypical learner types with distinct trajectories are well separated across task difficulty and specificity conditions in design matrix 1 games. → Sweet 2012 (2)

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

Numerical simulation results shown in Figure 7 (median WD across 100 replications for 21 learner types, design matrix 1) indicate the expert learner trajectory "shows the largest values of the WD statistic throughout the game".

"Figure 7 clearly shows that the trajectory of an expert learner, which is someone with perfect mastery of all SKIVE elements throughout the game, shows the largest values of the WD statistic throughout the game as one would expect."

Sweet 2012 (2)¶

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? · design · r2

Follow-up boxplot analyses for six prototypical learner types under design matrix 1 show boxplots for learner types with distinct trajectories "are well separated across task difficulty and specificity conditions", with overlap mimicking the similarity of WD traces.

"Overall, it is noteworthy that the boxplots for learner types with distinct trajectories are well separated across task difficulty and specificity conditions."

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
• The WD statistic is more useful for differentiating between simulated learner types when games are played longer — 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
• 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
• The system's generated practice trajectories align with learner skill levels at a peak cosine similarity of 0.962 ± 0.014 at the primary difficulty level — related